# Natural language processing

A computing discipline that transforms human communications into forms suitable for computer use and analysis.

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## From Preferences to Principles: Rubric-Based Alignment for Grounded Knowledge Answers

DevFeed: [From Preferences to Principles: Rubric-Based Alignment for Grounded Knowledge Answers](<https://devfeed.tech/articles/from-preferences-to-principles-rubric-based-alignment-for-grounded-knowledge-answers-6734.md>)

Original publisher: [Read original article](<https://machinelearning.apple.com/research/rubric-based-alignment>)

Published: 2026-08-27T00:00:00Z

Content type: article

Language: en

Sources: [Apple Machine Learning Research](<https://devfeed.tech/sources/apple-machine-learning-research.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>), [Natural language processing](<https://devfeed.tech/topics/nlp.md>), [datasets](<https://devfeed.tech/topics/datasets.md>)

Tags: [datasets](<https://devfeed.tech/tags/datasets.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [models](<https://devfeed.tech/tags/models.md>), [post-training](<https://devfeed.tech/tags/post-training.md>), [research](<https://devfeed.tech/tags/research.md>), [responses](<https://devfeed.tech/tags/responses.md>), [search](<https://devfeed.tech/tags/search.md>)

### AI overview

The article introduces a rubric-based reward framework for grounded open-domain question answering. It generates query-specific rubrics from retrieved evidence and decomposes them into quality dimensions for fine-grained post-training supervision. The method improves composition, grounding, and instruction-following results over the stated baselines and evaluation datasets.

### Source excerpt

Designing effective reward signals for open-domain question answering is challenging because high-quality responses must simultaneously satisfy multiple aspects of answer quality that are difficult to capture with a holistic scalar objective. We introduce a rubric-based reward framework that generates query-specific rubrics grounded in retrieved evidence and decomposed into multiple quality dimensions, providing fine-grained supervision during post-training. Averaged across three evaluation axes (composition, grounding, and instruction-following), our approach improves over the...

## Synthetic Data Generation for Financial AI Research with NVIDIA NeMo

DevFeed: [Synthetic Data Generation for Financial AI Research with NVIDIA NeMo](<https://devfeed.tech/articles/synthetic-data-generation-for-financial-ai-research-with-nvidia-nemo-6943.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/synthetic-data-generation-for-financial-ai-research-with-nvidia-nemo/>)

Author: Elizabeth Goodman

Published: 2026-07-09T19:40:37Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Synthetic Data Generation](<https://devfeed.tech/topics/synthetic-data-generation.md>), [synthetic-data](<https://devfeed.tech/topics/synthetic-data.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [Natural language processing](<https://devfeed.tech/topics/nlp.md>), [Nemotron](<https://devfeed.tech/topics/nemotron.md>), [vllm](<https://devfeed.tech/topics/vllm.md>), [datasets](<https://devfeed.tech/topics/datasets.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [ai-ready-data](<https://devfeed.tech/tags/ai-ready-data.md>), [cloud-services](<https://devfeed.tech/tags/cloud-services.md>), [compute](<https://devfeed.tech/tags/compute.md>), [data](<https://devfeed.tech/tags/data.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [featured](<https://devfeed.tech/tags/featured.md>), [financial-services](<https://devfeed.tech/tags/financial-services.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [generation](<https://devfeed.tech/tags/generation.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [inference](<https://devfeed.tech/tags/inference.md>), [llms](<https://devfeed.tech/tags/llms.md>), [models](<https://devfeed.tech/tags/models.md>), [natural-language-processing](<https://devfeed.tech/tags/natural-language-processing.md>), [nemo](<https://devfeed.tech/tags/nemo.md>), [nemotron](<https://devfeed.tech/tags/nemotron.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [research](<https://devfeed.tech/tags/research.md>), [structured-generation](<https://devfeed.tech/tags/structured-generation.md>), [synthetic](<https://devfeed.tech/tags/synthetic.md>), [synthetic-data](<https://devfeed.tech/tags/synthetic-data.md>), [synthetic-data-generation](<https://devfeed.tech/tags/synthetic-data-generation.md>), [vllm](<https://devfeed.tech/tags/vllm.md>), [workflow](<https://devfeed.tech/tags/workflow.md>)

### AI overview

This developer article presents an iterative pipeline for generating a diverse synthetic dataset of more than 500,000 financial news headlines. It combines NeMo Data Designer for structured generation, NeMo Curator for semantic deduplication, Nemotron models for synthesis, and a farthest-from-centroid few-shot strategy to reduce repetition and correct category imbalance.

### Source excerpt

Fine-tuning LLMs for financial natural language processing (NLP) is constrained by limited, imbalanced data. Real-world financial news overrepresents earnings...

## Capital One at ACL 2026

DevFeed: [Capital One at ACL 2026](<https://devfeed.tech/articles/capital-one-at-acl-2026-22571.md>)

Original publisher: [Read original article](<https://medium.com/capital-one-tech/capital-one-at-acl-2026-ad9c245333fe?source=rss----3db3a67cb648---4>)

Author: Capital One Tech

Published: 2026-07-01T15:28:51Z

Content type: article

Language: en

Sources: [Capital One Tech](<https://devfeed.tech/sources/capital-one-tech.md>)

Topics: [Natural language processing](<https://devfeed.tech/topics/nlp.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [LLM security](<https://devfeed.tech/topics/llm-security.md>), [Machine Learning, Security Attacks](<https://devfeed.tech/topics/machine-learning-security-attacks.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [Jailbreak](<https://devfeed.tech/topics/jailbreak.md>), [Security](<https://devfeed.tech/topics/security.md>), [Routing (disambiguation)](<https://devfeed.tech/topics/routing.md>), [datasets](<https://devfeed.tech/topics/datasets.md>)

Tags: [2026](<https://devfeed.tech/tags/2026.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-ml](<https://devfeed.tech/tags/ai-ml.md>), [ai-research](<https://devfeed.tech/tags/ai-research.md>), [conference](<https://devfeed.tech/tags/conference.md>), [jailbreak](<https://devfeed.tech/tags/jailbreak.md>), [llm-security](<https://devfeed.tech/tags/llm-security.md>), [natural-language-processing](<https://devfeed.tech/tags/natural-language-processing.md>), [paper](<https://devfeed.tech/tags/paper.md>), [partners](<https://devfeed.tech/tags/partners.md>), [red-teaming](<https://devfeed.tech/tags/red-teaming.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [research](<https://devfeed.tech/tags/research.md>), [science](<https://devfeed.tech/tags/science.md>)

### AI overview

Capital One describes its accepted ACL 2026 research on natural language processing, including work on adaptive LLM red teaming, query-only model routing with generated data, and language identification on web data. The article also highlights collaboration with academic partners.

### Source excerpt

Discover how Capital One is advancing state-of-the-art AI/ML science through collaborative natural language processing research.Advancing AI and NLP Frontiers at ACL 2026 As language models grow more deeply integrated into technology ecosystems, pioneering robust, efficient, and reliable Natural Language Processing (NLP) techniques becomes paramount. Capital One continues to invest in state-of-the-art AI/ML science through deep multi-sector collaboration and peer-reviewed research. At the upcoming Annual Meeting of the Association for Computational Linguistics (ACL 2026), Capital One researchers and academic partners will showcase novel findings stretching from LLM security to multilingual capabilities. Through the Science & Academic Partnerships program, Capital One bridges industry needs with academic expertise, funding critical university research and engineering solutions that make technology safer and more powerful. Our accepted publications at ACL 2026 demonstrate this thriving flywheel of talent and collaborative innovation across multiple research categories. Main Conference Research Adaptive Instruction Composition for Automated LLM Red Teaming Routing with Generated Data: Annotation-Free LLM Skill Estimation and Expert Selection Capital One Authors: Jesse Zymet, Swapnil Shinde, Sahil Wadhwa, Andy Luo Overview: Standard red teaming approaches often struggle with a limited range of jailbreak strategies or rely on ineffective, randomized crowd-sourced tactics. This paper introduces a novel framework -- Adaptive Instruction Composition -- that utilizes reinforcement learning and a neural contextual bandit to tailor attack compositions dynamically, balancing diversity and effectiveness to proactively uncover target model vulnerabilities. Routing with Generated Data: Annotation-Free LLM Skill Estimation and Expert Selection Capital One Authors: Genta Winata, Sambit Sahu, Supriyo Chakraborty, Shixiong Zhang Overview: Emerging from our gifted research collaboration

## Эволюция данных: генетический алгоритм в задаче классификации текстов

DevFeed: [Эволюция данных: генетический алгоритм в задаче классификации текстов](<https://devfeed.tech/articles/article-23997.md>)

Original publisher: [Read original article](<https://habr.com/ru/companies/redmadrobot/articles/1027238/>)

Author: evgeneration (red\_mad\_robot)

Published: 2026-04-23T19:05:42Z

Content type: article

Language: ru

Sources: [Redmadrobot EN](<https://devfeed.tech/sources/redmadrobot-en.md>), [Redmadrobot RU](<https://devfeed.tech/sources/redmadrobot-ru.md>)

Topics: [Natural language processing](<https://devfeed.tech/topics/nlp.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [evolution](<https://devfeed.tech/tags/evolution.md>), [llm](<https://devfeed.tech/tags/llm.md>), [ml](<https://devfeed.tech/tags/ml.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [prompt](<https://devfeed.tech/tags/prompt.md>), [tag-1cd610c0e518](<https://devfeed.tech/tags/tag-1cd610c0e518.md>), [tag-1f12060c781a](<https://devfeed.tech/tags/tag-1f12060c781a.md>), [tag-2d1b71a8786f](<https://devfeed.tech/tags/tag-2d1b71a8786f.md>), [tag-7bc388df28ed](<https://devfeed.tech/tags/tag-7bc388df28ed.md>), [tag-9bf5e01ce62e](<https://devfeed.tech/tags/tag-9bf5e01ce62e.md>), [tag-9d8cf70dc46c](<https://devfeed.tech/tags/tag-9d8cf70dc46c.md>)

### AI overview

The article describes using a genetic algorithm to evolve prompts that generate training data for text-classification models. The approach was motivated by poor results from synthetic datasets and manual prompt iteration, with prompt fitness evaluated by classifier performance on a fixed test set.

### Source excerpt

Привет! Меня зовут Женя Андриевская. Я -- NLP-инженер в лаборатории R&D red_mad_robot. Сегодня расскажу, как мы призвали на помощь эволюцию, чтобы улучшить качество данных в задачах классификации текстов. Да-да, ту самую эволюцию, с мутациями и скрещиваниями -- только в мире данных, промптов и LLM. Читать далее

## QIMMA قِمّة ⛰: A Quality-First Arabic LLM Leaderboard

DevFeed: [QIMMA قِمّة ⛰: A Quality-First Arabic LLM Leaderboard](<https://devfeed.tech/articles/qimma-a-quality-first-arabic-llm-leaderboard-7511.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/tiiuae/qimma-arabic-leaderboard>)

Author: Leen AlQadi; Ahmed Alzubaidi; Mohammed Alyafeai; Maitha Alhammadi; Shaikha Alsuwaidi; Omar saif alkaabi; Basma Boussaha; Hakim Hacid

Published: 2026-04-21T10:09:58Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Ground truth / benchmark quality](<https://devfeed.tech/topics/ground-truth-benchmark-quality.md>), [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [LLMs](<https://devfeed.tech/topics/llms.md>), [Natural language processing](<https://devfeed.tech/topics/nlp.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Code](<https://devfeed.tech/topics/code.md>)

Tags: [benchmark](<https://devfeed.tech/tags/benchmark.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [blog](<https://devfeed.tech/tags/blog.md>), [code](<https://devfeed.tech/tags/code.md>), [data](<https://devfeed.tech/tags/data.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [inference](<https://devfeed.tech/tags/inference.md>), [leaderboard](<https://devfeed.tech/tags/leaderboard.md>), [llm](<https://devfeed.tech/tags/llm.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [open-source](<https://devfeed.tech/tags/open-source.md>)

### AI overview

QIMMA is a quality-first Arabic LLM leaderboard that validates benchmark data before evaluating models. It addresses translation issues, annotation errors, encoding problems, cultural bias, reproducibility gaps, and fragmented task coverage. The platform combines native Arabic content, systematic validation, code evaluation, and public per-sample inference outputs across 109 subsets from 14 benchmarks and more than 52,000 samples.

### Source excerpt

A Blog post by Technology Innovation Institute on Hugging Face

## Natural Language Processing (NLP) Series

DevFeed: [Natural Language Processing (NLP) Series](<https://devfeed.tech/articles/natural-language-processing-nlp-series-4513.md>)

Original publisher: [Read original article](<https://feeds.feedblitz.com/~/952881911/0/baeldung/cs>)

Author: baeldung

Published: 2026-04-01T15:52:18Z

Content type: tutorial

Language: en

Sources: [Baeldung - CS](<https://devfeed.tech/sources/baeldung-cs.md>)

Topics: [Natural language processing](<https://devfeed.tech/topics/nlp.md>)

Tags: [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [artificial-intelligence-series-natural-language-processing](<https://devfeed.tech/tags/artificial-intelligence-series-natural-language-processing.md>), [guide](<https://devfeed.tech/tags/guide.md>), [natural-language-processing](<https://devfeed.tech/tags/natural-language-processing.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [series](<https://devfeed.tech/tags/series.md>)

### AI overview

A guide series covering the core areas of natural language processing.

### Source excerpt

This guide walks through the core areas of NLP. The post Natural Language Processing (NLP) Series first appeared on Baeldung on Computer Science. Related Stories What Is Aspect-Based Sentiment Analysis? What Are Language Models in NLP? Sorting Algorithms Series

## Как маскировать персональные данные на изображениях: наш эксперимент с OCR и NER

DevFeed: [Как маскировать персональные данные на изображениях: наш эксперимент с OCR и NER](<https://devfeed.tech/articles/ocr-ner-23996.md>)

Original publisher: [Read original article](<https://habr.com/ru/companies/redmadrobot/articles/1011450/>)

Author: andrivasg (red\_mad\_robot)

Published: 2026-03-17T15:55:37Z

Content type: tutorial

Language: ru

Sources: [Redmadrobot EN](<https://devfeed.tech/sources/redmadrobot-en.md>), [Redmadrobot RU](<https://devfeed.tech/sources/redmadrobot-ru.md>)

Topics: [Natural language processing](<https://devfeed.tech/topics/nlp.md>), [pii](<https://devfeed.tech/topics/pii.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [computer-vision](<https://devfeed.tech/tags/computer-vision.md>), [llm](<https://devfeed.tech/tags/llm.md>), [ner](<https://devfeed.tech/tags/ner.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [ocr](<https://devfeed.tech/tags/ocr.md>), [pii](<https://devfeed.tech/tags/pii.md>), [red-mad-robot](<https://devfeed.tech/tags/red-mad-robot.md>), [rnd](<https://devfeed.tech/tags/rnd.md>), [tag-601fbc7112a4](<https://devfeed.tech/tags/tag-601fbc7112a4.md>), [tag-7bc388df28ed](<https://devfeed.tech/tags/tag-7bc388df28ed.md>), [tag-9bf5e01ce62e](<https://devfeed.tech/tags/tag-9bf5e01ce62e.md>), [tag-b92bf5906bbd](<https://devfeed.tech/tags/tag-b92bf5906bbd.md>), [tag-ef0b1bf200df](<https://devfeed.tech/tags/tag-ef0b1bf200df.md>)

### AI overview

The article describes red_mad_robot's experiment using OCR combined with a NER model to detect and selectively mask personally identifiable information in images without training specialized visual detectors. On a dataset of 40 annotated images, the pipeline masked 90% of personal data while falsely masking 14% of text polygons; performance declined on difficult real-world photographs.

### Source excerpt

Всем привет! Меня зовут Андрей Иванов, я NLP-исследователь в R&D red_mad_robot. Мы разрабатываем систему Guardrails для защиты персональных данных (PII) и фильтрации небезопасного контента. В этой статье расскажу, как мы решали задачу точечного маскирования PII на картинках без обучения специальных визуальных детекторов. Разберём связку оптического распознавания символов (OCR) с NER-моделью, покажем метрики на реальных данных, раскроем ограничения подхода и наши решения для их преодоления. Читать далее

## Highlights of Booking.com's publication in 2025

DevFeed: [Highlights of Booking.com's publication in 2025](<https://devfeed.tech/articles/highlights-of-booking-com-s-publication-in-2025-30451.md>)

Original publisher: [Read original article](<https://booking.ai/highlights-of-booking-coms-publication-in-2025-1c1a6deba066?source=rss----4d265f07defc---4>)

Author: Yang Yang

Published: 2026-01-20T09:20:13Z

Content type: article

Language: en

Sources: [Booking.com Data Science](<https://devfeed.tech/sources/booking-com-data-science.md>)

Topics: [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Natural language processing](<https://devfeed.tech/topics/nlp.md>), [Language models](<https://devfeed.tech/topics/language-models.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [recommendation systems](<https://devfeed.tech/topics/recommendation-systems.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [Compression](<https://devfeed.tech/topics/compression.md>), [Inference](<https://devfeed.tech/topics/inference.md>)

Tags: [2025](<https://devfeed.tech/tags/2025.md>), [ai](<https://devfeed.tech/tags/ai.md>), [booking](<https://devfeed.tech/tags/booking.md>), [compression](<https://devfeed.tech/tags/compression.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [inference](<https://devfeed.tech/tags/inference.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [latency](<https://devfeed.tech/tags/latency.md>), [neurips](<https://devfeed.tech/tags/neurips.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [publication](<https://devfeed.tech/tags/publication.md>)

### AI overview

Booking.com highlights its 2025 machine learning publications, including papers accepted at major conferences and research on applying Medusa speculative decoding and knowledge distillation to travel-related language model tasks.

### Source excerpt

At Booking.com, our mission is to make experiencing the world easier for everyone. We are committed to investing in cutting-edge technology that removes the barriers to travel, enabling seamless connections between millions of travelers and unforgettable experiences, diverse transportation options, and exceptional accommodations. The intersection of academic rigor and industry application is where true transformation happens. In 2025, our ML community bridged this gap more effectively than ever, contributing vital new insights to the global scientific community. With 8 out of 13 papers accepted at premier conferences -- including NeurIPS, SIGIR, KDD, and ACL -- our colleagues have demonstrated world-class expertise in AI, NLP, recommendation systems, uplift modeling, etc. These aren't just theoretical wins; they are the engines of innovation that allow us to push technological boundaries, ensuring our platform remains the most sophisticated and intuitive guide in the ever-evolving travel industry. Below, we highlight some of the key achievements and insights from these groundbreaking works. Speed Without Sacrifice: Fine-Tuning Language Models with Medusa and Knowledge Distillation in Travel Applications By Daniel Zagyva, Emmanouil Stergiadis, Laurens Van Der Maas, Aleksandra Dokic, Eran Fainman, Ilya Gusev, Moran Beladev Best paper award of 2025 ACL Industry Track https://aclanthology.org/2025.acl-industry.48/ In high-stakes industrial NLP applications, balancing generation quality with speed and efficiency presents significant challenges. We address them by investigating two complementary optimization approaches: Medusa for speculative decoding and knowledge distillation (KD) for model compression. We demonstrate the practical application of these techniques in real-world travel domain tasks, including trip planning, smart filters, and generating accommodation descriptions. We introduce modifications to the Medusa implementation, starting with base pre-trained models

## Introducing Falcon-H1-Arabic: Pushing the Boundaries of Arabic Language AI with Hybrid Architecture

DevFeed: [Introducing Falcon-H1-Arabic: Pushing the Boundaries of Arabic Language AI with Hybrid Architecture](<https://devfeed.tech/articles/introducing-falcon-h1-arabic-pushing-the-boundaries-of-arabic-language-ai-with-hybrid-architecture-7509.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/tiiuae/falcon-h1-arabic>)

Author: Basma Boussaha; Mohammed Alyafeai; Ahmed Alzubaidi; Leen AlQadi; Shaikha Alsuwaidi; Omar saif alkaabi; Hamza Alobeidli; Hakim Hacid

Published: 2026-01-05T09:16:51Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Natural language processing](<https://devfeed.tech/topics/nlp.md>), [Mamba](<https://devfeed.tech/topics/mamba.md>), [Transformer](<https://devfeed.tech/topics/transformer.md>), [long-context](<https://devfeed.tech/topics/long-context.md>), [Architecture & Design](<https://devfeed.tech/topics/architecture-design.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [arabic](<https://devfeed.tech/tags/arabic.md>), [blog](<https://devfeed.tech/tags/blog.md>), [building](<https://devfeed.tech/tags/building.md>), [community](<https://devfeed.tech/tags/community.md>), [design](<https://devfeed.tech/tags/design.md>), [developers](<https://devfeed.tech/tags/developers.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [enterprise](<https://devfeed.tech/tags/enterprise.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [innovation](<https://devfeed.tech/tags/innovation.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [learning](<https://devfeed.tech/tags/learning.md>), [long-context](<https://devfeed.tech/tags/long-context.md>), [mamba](<https://devfeed.tech/tags/mamba.md>), [model](<https://devfeed.tech/tags/model.md>), [models](<https://devfeed.tech/tags/models.md>), [natural-language-processing](<https://devfeed.tech/tags/natural-language-processing.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [technology](<https://devfeed.tech/tags/technology.md>)

### AI overview

The article introduces Falcon-H1-Arabic, a family of 3B, 7B, and 34B Arabic language models. It describes a hybrid architecture that combines Mamba State Space Models with Transformer attention in parallel, aiming to improve long-context coherence, reasoning, efficiency, and deployment across edge devices and enterprise applications.

### Source excerpt

A Blog post by Technology Innovation Institute on Hugging Face

## MCP Tool Registry: автоматизированное создание систем RAG

DevFeed: [MCP Tool Registry: автоматизированное создание систем RAG](<https://devfeed.tech/articles/mcp-tool-registry-rag-24033.md>)

Original publisher: [Read original article](<https://habr.com/ru/companies/redmadrobot/articles/982004/>)

Author: maksimov\_m (red\_mad\_robot)

Published: 2025-12-30T16:07:23Z

Content type: tutorial

Language: ru

Sources: [Redmadrobot EN](<https://devfeed.tech/sources/redmadrobot-en.md>), [Redmadrobot RU](<https://devfeed.tech/sources/redmadrobot-ru.md>)

Topics: [Model Context Protocol](<https://devfeed.tech/topics/model-context-protocol.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Natural language processing](<https://devfeed.tech/topics/nlp.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Tool](<https://devfeed.tech/topics/tool.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [llm](<https://devfeed.tech/tags/llm.md>), [mcp](<https://devfeed.tech/tags/mcp.md>), [model-context-protocol](<https://devfeed.tech/tags/model-context-protocol.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [rag](<https://devfeed.tech/tags/rag.md>), [tag-41e9f0e0da78](<https://devfeed.tech/tags/tag-41e9f0e0da78.md>), [tag-68e701e78517](<https://devfeed.tech/tags/tag-68e701e78517.md>), [tag-db2a5a2010aa](<https://devfeed.tech/tags/tag-db2a5a2010aa.md>), [tag-ebcf3e175cd3](<https://devfeed.tech/tags/tag-ebcf3e175cd3.md>), [tool](<https://devfeed.tech/tags/tool.md>), [workflows](<https://devfeed.tech/tags/workflows.md>)

### AI overview

This Russian-language developer article presents MCP Tool Registry, a central registry for managing MCP servers used to build Retrieval Augmented Generation systems. It explains how RAG and Model Context Protocol address LLM limitations around context and access to external tools and data, and describes registry-based orchestration of multi-step workflows across multiple servers.

### Source excerpt

Всем привет! Меня зовут Максим Максимов, я -- NLP-инженер в AI R&D лаборатории в red_mad_robot. Мы работаем с прикладными AI-системами, проводим эксперименты и проверяем архитектурные подходы в реальных сценариях. Современные AI-архитектуры всё чаще выносят прикладную логику за пределы LLM -- в инструменты, внешние источники данных и правила их оркестрации. Модель выступает вычислительным ядром, а ценность формируется на уровне системы вокруг неё. Из этого подхода в R&D-лаборатории вырос MCP Tool Registry -- центральный реестр для управления системой серверов, необходимых для построения RAG. В этой статье я подробно расскажу, из каких частей состоит MCP Tool Registry, как он работает и покажу его применение на практическом примере. Читать далее

## Контроль против гибкости: два подхода к созданию AI-агентов

DevFeed: [Контроль против гибкости: два подхода к созданию AI-агентов](<https://devfeed.tech/articles/ai-24030.md>)

Original publisher: [Read original article](<https://habr.com/ru/companies/redmadrobot/articles/966628/>)

Author: Mentalitet (red\_mad\_robot)

Published: 2025-11-14T17:15:42Z

Content type: article

Language: ru

Sources: [Redmadrobot EN](<https://devfeed.tech/sources/redmadrobot-en.md>), [Redmadrobot RU](<https://devfeed.tech/sources/redmadrobot-ru.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [AI Development](<https://devfeed.tech/topics/ai-development.md>), [Natural language processing](<https://devfeed.tech/topics/nlp.md>), [Langgraph](<https://devfeed.tech/topics/langgraph.md>), [SDKs](<https://devfeed.tech/topics/sdks.md>), [OpenAI](<https://devfeed.tech/topics/openai.md>), [cursor](<https://devfeed.tech/topics/cursor.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [cursor](<https://devfeed.tech/tags/cursor.md>), [development](<https://devfeed.tech/tags/development.md>), [development-kit](<https://devfeed.tech/tags/development-kit.md>), [langgraph](<https://devfeed.tech/tags/langgraph.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llm-05dacf5e57d9](<https://devfeed.tech/tags/llm-05dacf5e57d9.md>), [llm-12323b29df45](<https://devfeed.tech/tags/llm-12323b29df45.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [openai](<https://devfeed.tech/tags/openai.md>), [red-mad-robot](<https://devfeed.tech/tags/red-mad-robot.md>), [sdk](<https://devfeed.tech/tags/sdk.md>), [software-development](<https://devfeed.tech/tags/software-development.md>), [tag-1605473766c5](<https://devfeed.tech/tags/tag-1605473766c5.md>), [tag-61cd5a476b1d](<https://devfeed.tech/tags/tag-61cd5a476b1d.md>), [tag-68e701e78517](<https://devfeed.tech/tags/tag-68e701e78517.md>), [tag-93edcb27480f](<https://devfeed.tech/tags/tag-93edcb27480f.md>), [tag-b0a411324cb6](<https://devfeed.tech/tags/tag-b0a411324cb6.md>), [tag-dd24610a6838](<https://devfeed.tech/tags/tag-dd24610a6838.md>)

### AI overview

The article compares code-first and graph-first approaches to building multi-agent systems. It describes how LLMs coordinate tools and other agents, including ReAct, handoff, agent-as-tool, and group-chat patterns, drawing on the author's development experience and documentation from OpenAI and LangGraph.

### Source excerpt

Привет! Меня зовут Женя Орлов, я руковожу NLP-разработкой в red_mad_robot. Мы экспериментируем с мультиагентными системами и изучаем, как LLM ведут себя в разных архитектурах. В процессе накопилось много наблюдений и рабочих инсайтов -- хочу поделиться тем, что помогает нам при проектировании AI-агентов. В последнее время внимание инженеров и исследователей привлекают агентные архитектуры -- системы, где LLM координирует работу внутренних инструментов и других моделей. Разберём два основных подхода к построению агентных систем. Читать далее

## Sentence Transformers is joining Hugging Face!

DevFeed: [Sentence Transformers is joining Hugging Face!](<https://devfeed.tech/articles/sentence-transformers-is-joining-hugging-face-7472.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/sentence-transformers-joins-hf>)

Author: Tom Aarsen

Published: 2025-10-22T00:00:00Z

Content type: news

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [sentence-transformers](<https://devfeed.tech/topics/sentence-transformers.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [Natural language processing](<https://devfeed.tech/topics/nlp.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [announcement](<https://devfeed.tech/tags/announcement.md>), [collaboration](<https://devfeed.tech/tags/collaboration.md>), [community](<https://devfeed.tech/tags/community.md>), [contributors](<https://devfeed.tech/tags/contributors.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [natural-language-processing](<https://devfeed.tech/tags/natural-language-processing.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [sentence-transformers](<https://devfeed.tech/tags/sentence-transformers.md>)

### AI overview

Sentence Transformers, also known as SentenceBERT or SBERT, is joining Hugging Face. The popular open-source library generates embeddings that capture semantic meaning and supports natural language processing tasks such as semantic search, similarity analysis, clustering, and paraphrase mining. It will remain community-driven under the Apache 2.0 license, with contributions welcomed from researchers, developers, and enthusiasts.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## AI Agents and Multi-Agent Systems: A Practical Case for Analyzing and Fixing Code Vulnerabilities

DevFeed: [AI Agents and Multi-Agent Systems: A Practical Case for Analyzing and Fixing Code Vulnerabilities](<https://devfeed.tech/articles/article-24028.md>)

Original publisher: [Read original article](<https://habr.com/ru/companies/redmadrobot/articles/957300/>)

Author: redmadrobot (red\_mad\_robot)

Published: 2025-10-16T15:18:12Z

Content type: tutorial

Language: ru

Sources: [Redmadrobot EN](<https://devfeed.tech/sources/redmadrobot-en.md>), [Redmadrobot RU](<https://devfeed.tech/sources/redmadrobot-ru.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Natural language processing](<https://devfeed.tech/topics/nlp.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [graph](<https://devfeed.tech/tags/graph.md>), [llm](<https://devfeed.tech/tags/llm.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [sast](<https://devfeed.tech/tags/sast.md>), [systems](<https://devfeed.tech/tags/systems.md>), [tag-1605473766c5](<https://devfeed.tech/tags/tag-1605473766c5.md>), [tag-4d5248fd6bfe](<https://devfeed.tech/tags/tag-4d5248fd6bfe.md>), [workflow](<https://devfeed.tech/tags/workflow.md>)

### AI overview

The article explains the defining characteristics and autonomy levels of AI agents, focusing on workflow agents and multi-agent systems. It presents a practical case involving a multi-agent environment for automatically analyzing and fixing code vulnerabilities.

### Source excerpt

Привет! Меня зовут Егор Козлов, я работаю NLP-инженером в red_mad_robot. Мы активно внедряем в бизнес AI-агентов -- автономных и полуавтономных программных сущностей, которые самостоятельно выполняют задачи и принимают решения в интересах бизнеса. В статье расскажу о принципах работы AI-агентов -- с особым вниманием к workflow-агентам и мультиагентным системам (MAS). И поделюсь практическим кейсом внедрения мультиагентной среды для автоматического анализа и исправления уязвимостей в коде. Читать далее

## Detecting hallucinations in RAG systems with circuit tracing and attribution graphs

DevFeed: [Detecting hallucinations in RAG systems with circuit tracing and attribution graphs](<https://devfeed.tech/articles/circuit-tracing-24026.md>)

Original publisher: [Read original article](<https://habr.com/ru/companies/redmadrobot/articles/951118/>)

Author: IrinaKoshkina (red\_mad\_robot)

Published: 2025-09-26T18:30:09Z

Content type: tutorial

Language: ru

Sources: [Redmadrobot EN](<https://devfeed.tech/sources/redmadrobot-en.md>), [Redmadrobot RU](<https://devfeed.tech/sources/redmadrobot-ru.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [Natural language processing](<https://devfeed.tech/topics/nlp.md>), [anthropic](<https://devfeed.tech/topics/anthropic.md>)

Tags: [anthropic](<https://devfeed.tech/tags/anthropic.md>), [circuit](<https://devfeed.tech/tags/circuit.md>), [llm](<https://devfeed.tech/tags/llm.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [rag](<https://devfeed.tech/tags/rag.md>), [red-mad-robot](<https://devfeed.tech/tags/red-mad-robot.md>), [tag-0444141ce356](<https://devfeed.tech/tags/tag-0444141ce356.md>), [tag-3f4d1152a2b4](<https://devfeed.tech/tags/tag-3f4d1152a2b4.md>), [tag-73f5f2c76797](<https://devfeed.tech/tags/tag-73f5f2c76797.md>), [tag-b6914c0b0244](<https://devfeed.tech/tags/tag-b6914c0b0244.md>), [tag-d27a0708d400](<https://devfeed.tech/tags/tag-d27a0708d400.md>), [tracing](<https://devfeed.tech/tags/tracing.md>), [transcoder](<https://devfeed.tech/tags/transcoder.md>)

### AI overview

The article explains an approach to detecting hallucinations in retrieval-augmented generation systems by examining model computation graphs with Anthropic's framework, cross-layer transcoders, and attribution graphs. It presents an implementation for analyzing Qwen2.5-7B and reports 85% detection accuracy on a test dataset for a simple implementation.

### Source excerpt

Всем привет! Меня зовут Ирина, я NLP-инженер в red_mad_robot, занимаюсь научными исследованиями интерпретируемости LLM и анализом механизмов внутренних вычислений моделей, чтобы применять полученные результаты на практике. Например, сегодня хочу рассказать, как мы подошли к решению задачи детекции галлюцинаций LLM в RAG системах со стороны исследования графов размышлений модели -- с помощью интересного фреймворка от Anthropic. В статье поговорим, как использовать cross-layer transcoders и атрибуционные графы для детекции галлюцинаций в RAG системах. Рассмотрим пример реализации детектора на графах для анализа модели Qwen2.5-7B и практические примеры использования. Спойлер: на простой реализации получили точность детекции 85% на тестовом датасете. Читать далее

## Migrating to Nvidia Triton: High-Throughput, Low-Cost Inference at Scale

DevFeed: [Migrating to Nvidia Triton: High-Throughput, Low-Cost Inference at Scale](<https://devfeed.tech/articles/migrating-to-nvidia-triton-high-throughput-low-cost-inference-at-scale-35070.md>)

Original publisher: [Read original article](<https://medium.com/gumgum-tech/migrating-to-nvidia-triton-high-throughput-low-cost-inference-at-scale-24af3ada4210?source=rss----d4c1dee0f87b---4>)

Author: Eric Luu

Published: 2025-09-19T01:05:13Z

Content type: article

Language: en

Sources: [GumGum](<https://devfeed.tech/sources/gumgum.md>)

Topics: [Inference](<https://devfeed.tech/topics/inference.md>), [model-serving](<https://devfeed.tech/topics/model-serving.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Natural language processing](<https://devfeed.tech/topics/nlp.md>), [databricks](<https://devfeed.tech/topics/databricks.md>), [Docker](<https://devfeed.tech/topics/docker.md>), [Kafka](<https://devfeed.tech/topics/kafka.md>), [Python](<https://devfeed.tech/topics/python.md>)

Tags: [batching](<https://devfeed.tech/tags/batching.md>), [databricks](<https://devfeed.tech/tags/databricks.md>), [docker](<https://devfeed.tech/tags/docker.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [kafka](<https://devfeed.tech/tags/kafka.md>), [latency](<https://devfeed.tech/tags/latency.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [performance](<https://devfeed.tech/tags/performance.md>), [triton-inference-server](<https://devfeed.tech/tags/triton-inference-server.md>)

### AI overview

This article describes GumGum's migration of NLP inference from Databricks-based jobs using Docker images, Kafka streaming, and UDFs toward NVIDIA Triton. It introduces Triton's model-serving features, including batching, ensembles, dynamic model loading, request scheduling, and reduced GPU-CPU data transfers, while outlining limitations of the previous approach such as latency, scalability, GPU-metrics, and PySpark performance issues.

### Source excerpt

Photo by Igor Omilaev on Unsplash There are tons of guides on what Nvidia Triton is and how to set it up, so I'll only go over those very briefly, instead focusing on the migration we've had and the small quirks that come from working with Python Backends. Nvidia Triton is an open source inference serving software, capable of serving virtually any machine learning model. Real time processing, batching, dynamic batching, ensembles, specialized backends, all possible with enough knobs and dials to maximize hardware use and minimize cost. Core features include File based model repository to store your models. If wanted, can be dynamically loaded onto the server Ensemble capabilities, can define whole inference pipelines, even ones that branch, on triton Ability to define each step in the inference pipeline as its own model (e.g., tokenizer -> model -> decoder), which can get hit as its own endpoint Dynamic batching between models, even in ensembles Automatic scheduling of requests between models and ensembles Minimal data transfers between GPU & CPU Our Previous Solution At GumGum we've previously deployed the bulk of our NLP inference on Databricks. These would run 24 hour jobs loading custom Docker images, streaming inputs from a Kafka topic and relying on UDF's to contain our inference logic. This was a move to mitigate issues we had on ECS related to starvation, scalability, and parallelism. Workers being paired on consumers lead to starvation. As a result, scalability of the service suffered and maintaining threading code became a pain. We've also experienced other issues as well: GPU metrics were not easily available, as we need to use an ML runtime, which was not compatible with docker containers (Although modern runtime do support this more). Latency was high as with Kafka structured streaming, microbatches must wait for all workers to finish. So if one worker hangs, the entire batch hangs. Performance on PySpark was always tough. Serializing between Java to Pyth

## Falcon-Arabic: A Breakthrough in Arabic Language Models

DevFeed: [Falcon-Arabic: A Breakthrough in Arabic Language Models](<https://devfeed.tech/articles/falcon-arabic-a-breakthrough-in-arabic-language-models-7506.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/tiiuae/falcon-arabic>)

Author: Basma Boussaha; Mohammed Alyafeai; Ahmed Alzubaidi; Leen AlQadi; Younes B; Mike Lubinets; Hakim Hacid; Falcon LLM TII UAE

Published: 2025-05-21T06:35:36Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Large language models (LLMs)](<https://devfeed.tech/topics/large-language-models-llms.md>), [Natural language processing](<https://devfeed.tech/topics/nlp.md>), [long-context](<https://devfeed.tech/topics/long-context.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [arabic](<https://devfeed.tech/tags/arabic.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [blog](<https://devfeed.tech/tags/blog.md>), [blog-post](<https://devfeed.tech/tags/blog-post.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [large-language-models-llms](<https://devfeed.tech/tags/large-language-models-llms.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [rag](<https://devfeed.tech/tags/rag.md>), [technology](<https://devfeed.tech/tags/technology.md>), [tokens](<https://devfeed.tech/tags/tokens.md>)

### AI overview

The article introduces Falcon-Arabic, a 7B-parameter multilingual language model built on the Falcon 3 architecture. It is designed for Arabic and English, supports Modern Standard Arabic and regional dialects, handles 32,000-token contexts, and targets tasks including grammar, mathematical reasoning, complex problem solving, content creation, and retrieval-augmented generation. The article presents it as an efficient, accessible, and open-source model that addresses the underrepresentation of Arabic in AI.

### Source excerpt

A Blog post by Technology Innovation Institute on Hugging Face

## The NLP Course is becoming the LLM Course

DevFeed: [The NLP Course is becoming the LLM Course](<https://devfeed.tech/articles/the-nlp-course-is-becoming-the-llm-course-7339.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/llm-course>)

Author: ben burtenshaw; Vaibhav Srivastav; Lewis Tunstall; Florent Daudens; Pedro Cuenca; Tom Aarsen; Eliott Coyac; Mishig ᠮᠢᠰᠾᠢᠭ; Sergio Paniego; Julien Chaumond

Published: 2025-04-03T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [LLMs](<https://devfeed.tech/topics/llms.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [Natural language processing](<https://devfeed.tech/topics/nlp.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Transformers](<https://devfeed.tech/topics/transformers.md>), [spaces](<https://devfeed.tech/topics/spaces.md>), [sentence-transformers](<https://devfeed.tech/topics/sentence-transformers.md>)

Tags: [education](<https://devfeed.tech/tags/education.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [inference](<https://devfeed.tech/tags/inference.md>), [llm-course](<https://devfeed.tech/tags/llm-course.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [nlp-course](<https://devfeed.tech/tags/nlp-course.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [sentence-transformers](<https://devfeed.tech/tags/sentence-transformers.md>), [spaces](<https://devfeed.tech/tags/spaces.md>), [transformers](<https://devfeed.tech/tags/transformers.md>)

### AI overview

Hugging Face is renaming and expanding its NLP course as The LLM course. The refreshed curriculum adds material on fine-tuning LLMs, reasoning models, inference, retrieval, and modern NLP methods while retaining foundational tasks such as classification and named entity recognition.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## AI-буткемп от red\_mad\_robot: прокачиваем скиллы в NLP и MarkUp

DevFeed: [AI-буткемп от red\_mad\_robot: прокачиваем скиллы в NLP и MarkUp](<https://devfeed.tech/articles/ai-red-mad-robot-nlp-markup-24009.md>)

Original publisher: [Read original article](<https://habr.com/ru/companies/redmadrobot/articles/884698/>)

Author: redmadrobot (red\_mad\_robot)

Published: 2025-02-21T12:37:26Z

Content type: release

Language: ru

Sources: [Redmadrobot EN](<https://devfeed.tech/sources/redmadrobot-en.md>), [Redmadrobot RU](<https://devfeed.tech/sources/redmadrobot-ru.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Natural language processing](<https://devfeed.tech/topics/nlp.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [genai](<https://devfeed.tech/topics/genai.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [ChatGPT](<https://devfeed.tech/topics/chatgpt.md>), [Claude](<https://devfeed.tech/topics/claude.md>), [llama](<https://devfeed.tech/topics/llama.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [chatgpt](<https://devfeed.tech/tags/chatgpt.md>), [claude](<https://devfeed.tech/tags/claude.md>), [genai](<https://devfeed.tech/tags/genai.md>), [llama](<https://devfeed.tech/tags/llama.md>), [llm](<https://devfeed.tech/tags/llm.md>), [markup](<https://devfeed.tech/tags/markup.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [rag](<https://devfeed.tech/tags/rag.md>), [tag-77ff283d3734](<https://devfeed.tech/tags/tag-77ff283d3734.md>), [tag-86b843454893](<https://devfeed.tech/tags/tag-86b843454893.md>), [tag-a6eb30d5e1b3](<https://devfeed.tech/tags/tag-a6eb30d5e1b3.md>), [tag-e076f05a03bb](<https://devfeed.tech/tags/tag-e076f05a03bb.md>)

### AI overview

red_mad_robot announces a free four-week AI bootcamp for junior developers, with tracks for NLP engineers and MarkUp engineers. The program includes practical work, senior mentorship, collaborative AI development, and a portfolio case. Applications are accepted until March 10, and the bootcamp starts March 17.

### Source excerpt

Хотите ворваться в разработку AI-решений? red_mad_robot запускает интенсивный буткемп для джунов, где за 4 недели вы освоите востребованные навыки и получите практический опыт в разработке AI-продуктов под руководством ведущих экспертов индустрии Читать далее

## The Open Arabic LLM Leaderboard 2

DevFeed: [The Open Arabic LLM Leaderboard 2](<https://devfeed.tech/articles/the-open-arabic-llm-leaderboard-2-7311.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/leaderboard-arabic-v2>)

Author: Ali El Filali; Manel ALOUI; Tarique Husaain; Ahmed Alzubaidi; Basma Boussaha; Ruxandra Cojocaru; Hakim Hacid; Clémentine Fourrier

Published: 2025-02-10T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>), [LLMs](<https://devfeed.tech/topics/llms.md>), [Natural language processing](<https://devfeed.tech/topics/nlp.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [arabic](<https://devfeed.tech/tags/arabic.md>), [benchmarking](<https://devfeed.tech/tags/benchmarking.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [leaderboard](<https://devfeed.tech/tags/leaderboard.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llms](<https://devfeed.tech/tags/llms.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [research](<https://devfeed.tech/tags/research.md>), [verification](<https://devfeed.tech/tags/verification.md>)

### AI overview

The article introduces the second Open Arabic LLM Leaderboard and explains the need for accessible, transparent, and reproducible evaluation of Arabic language models. It discusses limitations of earlier leaderboards, including high computing costs and unverifiable user-submitted results, and describes related benchmarking initiatives and datasets.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## AI Core Team Lead Mike Schuster on How to Get the Most From LLMs

DevFeed: [AI Core Team Lead Mike Schuster on How to Get the Most From LLMs](<https://devfeed.tech/articles/ai-core-team-lead-mike-schuster-on-how-to-get-the-most-from-llms-39476.md>)

Original publisher: [Read original article](<https://www.twosigma.com/articles/how-to-get-the-most-from-llms/>)

Author: Joy Looney

Published: 2025-02-04T17:03:42Z

Content type: opinion

Language: en

Sources: [Two Sigma Engineering](<https://devfeed.tech/sources/two-sigma-engineering.md>)

Topics: [LLMs](<https://devfeed.tech/topics/llms.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Generative AI](<https://devfeed.tech/topics/generative-ai.md>), [Natural language processing](<https://devfeed.tech/topics/nlp.md>), [Prompt Engineering](<https://devfeed.tech/topics/prompt-engineering.md>), [data](<https://devfeed.tech/topics/data.md>), [Programming](<https://devfeed.tech/topics/programming.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [data](<https://devfeed.tech/tags/data.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [llms](<https://devfeed.tech/tags/llms.md>), [programming](<https://devfeed.tech/tags/programming.md>)

### AI overview

Two Sigma AI Core Team Lead Mike Schuster discusses practical uses and limitations of large language models. He emphasizes measuring their value through current use cases, including faster data processing, model training, experimentation, productivity improvements, and feature extraction from financial text.

### Source excerpt

The post AI Core Team Lead Mike Schuster on How to Get the Most From LLMs appeared first on Two Sigma.

## Train 400x faster Static Embedding Models with Sentence Transformers

DevFeed: [Train 400x faster Static Embedding Models with Sentence Transformers](<https://devfeed.tech/articles/train-400x-faster-static-embedding-models-with-sentence-transformers-7491.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/static-embeddings>)

Author: Tom Aarsen

Published: 2025-01-15T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [sentence-transformers](<https://devfeed.tech/topics/sentence-transformers.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [Natural language processing](<https://devfeed.tech/topics/nlp.md>), [On-device AI](<https://devfeed.tech/topics/on-device-ai.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [recommendation systems](<https://devfeed.tech/topics/recommendation-systems.md>)

Tags: [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [community](<https://devfeed.tech/tags/community.md>), [dataset](<https://devfeed.tech/tags/dataset.md>), [edge-computing](<https://devfeed.tech/tags/edge-computing.md>), [embedded](<https://devfeed.tech/tags/embedded.md>), [embedding](<https://devfeed.tech/tags/embedding.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [guide](<https://devfeed.tech/tags/guide.md>), [inference](<https://devfeed.tech/tags/inference.md>), [low-power](<https://devfeed.tech/tags/low-power.md>), [natural-language-processing](<https://devfeed.tech/tags/natural-language-processing.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [recommendation-systems](<https://devfeed.tech/tags/recommendation-systems.md>), [retrieval](<https://devfeed.tech/tags/retrieval.md>), [sentence-transformers](<https://devfeed.tech/tags/sentence-transformers.md>)

### AI overview

This Hugging Face blog post presents a method for training static embedding models that run 100x to 400x faster on CPU while retaining most of the quality of state-of-the-art models. It introduces released models for English retrieval and multilingual similarity, along with their training strategy, scripts, evaluation reports, and datasets. The approach supports on-device, in-browser, edge, low-power, and embedded use cases.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Introducing the Open Leaderboard for Japanese LLMs!

DevFeed: [Introducing the Open Leaderboard for Japanese LLMs!](<https://devfeed.tech/articles/introducing-the-open-leaderboard-for-japanese-llms-7319.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/leaderboard-japanese>)

Author: Akim Mousterou; Yusuke Miyao; Namgi Han; Takumi Okamoto; Shigeki Ishida; hysts; Clémentine Fourrier

Published: 2024-11-20T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>), [Large language models (LLMs)](<https://devfeed.tech/topics/large-language-models-llms.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [Natural language processing](<https://devfeed.tech/topics/nlp.md>), [tokenization](<https://devfeed.tech/topics/tokenization.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [collaboration](<https://devfeed.tech/tags/collaboration.md>), [community](<https://devfeed.tech/tags/community.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [leaderboard](<https://devfeed.tech/tags/leaderboard.md>), [llms](<https://devfeed.tech/tags/llms.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [research](<https://devfeed.tech/tags/research.md>), [tokenization](<https://devfeed.tech/tags/tokenization.md>)

### AI overview

The article introduces the Open Japanese LLM Leaderboard, an open evaluation platform built by LLM-jp and Hugging Face. It uses more than 20 datasets and a specialized evaluation suite covering 16 tasks to compare Japanese large language models, address the challenges of Japanese NLP, and support transparent, collaborative, open-source research.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Introducing the Open FinLLM Leaderboard

DevFeed: [Introducing the Open FinLLM Leaderboard](<https://devfeed.tech/articles/introducing-the-open-finllm-leaderboard-7317.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/leaderboard-finbench>)

Author: Xie; Jimin Huang; Sophia Ananiadou; Xiao-Yang Liu Yanglet; Alejandro Lopez-Lira; Wang; ldruth; Ruoyu Xiang; chenzhengyu; Yangyang Yu

Published: 2024-10-04T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Finance](<https://devfeed.tech/topics/finance.md>), [LLMs](<https://devfeed.tech/topics/llms.md>), [Human-AI evaluation](<https://devfeed.tech/topics/human-ai-evaluation.md>), [Natural language processing](<https://devfeed.tech/topics/nlp.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [collaboration](<https://devfeed.tech/tags/collaboration.md>), [community](<https://devfeed.tech/tags/community.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [finance](<https://devfeed.tech/tags/finance.md>), [financial-sector](<https://devfeed.tech/tags/financial-sector.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [leaderboard](<https://devfeed.tech/tags/leaderboard.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [qa](<https://devfeed.tech/tags/qa.md>), [testing](<https://devfeed.tech/tags/testing.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>), [zero-shot](<https://devfeed.tech/tags/zero-shot.md>)

### AI overview

The article introduces the Open FinLLM Leaderboard, a specialized evaluation framework for financial language models. It evaluates models on finance-specific tasks such as information extraction, sentiment analysis, credit risk scoring, stock forecasting, question answering, text generation, and decision-making, using real-world datasets and metrics including Accuracy, F1 Score, ROUGE, and MCC.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Perplexity CEO Aravind Srinivas on building an answer engine for curiosity

DevFeed: [Perplexity CEO Aravind Srinivas on building an answer engine for curiosity](<https://devfeed.tech/articles/what-would-you-ask-if-no-one-could-judge-you-10002.md>)

Original publisher: [Read original article](<https://www.figma.com/blog/perplexity-ai-interview/>)

Author: Alia Fite

Published: 2024-06-26T00:00:00Z

Content type: article

Language: en

Sources: [Figma Blog](<https://devfeed.tech/sources/figma-blog.md>)

Topics: [Conversational AI](<https://devfeed.tech/topics/conversational-ai.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Natural language processing](<https://devfeed.tech/topics/nlp.md>), [App](<https://devfeed.tech/topics/app.md>), [Wiki](<https://devfeed.tech/topics/wiki.md>), [Google](<https://devfeed.tech/topics/google.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [app](<https://devfeed.tech/tags/app.md>), [ceo](<https://devfeed.tech/tags/ceo.md>), [conversational-ai](<https://devfeed.tech/tags/conversational-ai.md>), [learning](<https://devfeed.tech/tags/learning.md>), [llms](<https://devfeed.tech/tags/llms.md>), [natural-language-processing](<https://devfeed.tech/tags/natural-language-processing.md>), [perplexity](<https://devfeed.tech/tags/perplexity.md>), [search](<https://devfeed.tech/tags/search.md>), [slackbot](<https://devfeed.tech/tags/slackbot.md>), [web](<https://devfeed.tech/tags/web.md>), [wikipedia](<https://devfeed.tech/tags/wikipedia.md>)

### AI overview

Figma interviews Perplexity co-founder and CEO Aravind Srinivas about the company's vision for an answer engine that synthesizes information from the web. He discusses conversational search, sourcing, curiosity, and the origins of Perplexity as an internal Slackbot.

### Source excerpt

Co-founder and CEO Aravind Srinivas wants Perplexity to be not only a resource, but an engine for curiosity.

[Next page](<https://devfeed.tech/topics/nlp.md?cursor=WyIyMDI0LTA2LTI2VDAwOjAwOjAwKzAwOjAwIiwgIjM3MDY1NDBhLWZmOTAtNDk1ZC1iYzIwLTI3OWY2ZGYyNjhhMCJd>)