# Hallucination detection Published articles for Hallucination detection. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Ground truth is a process, not a dataset DevFeed: [Ground truth is a process, not a dataset]() Original publisher: [Read original article]() Author: Venkatesh Saligrama Published: 2026-06-03T15:56:57Z Content type: article Language: en Sources: [Amazon Science homepage]() Topics: [ai observability](), [AI Chat]() Tags: [ai](), [ai-fact-checking](), [ai-generated-research-reports](), [audit-then-score](), [benchmarking](), [conversational-ai](), [datasets](), [deep-research-verification](), [deepfact-bench](), [evaluation](), [fact-verification](), [fact-verification-benchmark](), [generative-ai](), [ground-truth-benchmark-quality](), [hallucination-detection](), [hallucinations](), [human-ai-evaluation](), [large-language-models-llms](), [llm-evaluation-benchmarking](), [long-context](), [machine-learning](), [responsible-ai](), [retrieval-augmented-generation-rag]() ### AI overview The article argues that evaluating factuality in long AI-generated research reports requires a process-based approach to ground truth. It introduces audit-then-score and accompanying datasets for benchmarking AI fact checkers. ### Source excerpt Automatically fact-checking long, AI-generated research reports poses new challenges -- including benchmarking. ## Measuring Open-Source Llama Nemotron Models on DeepResearch Bench DevFeed: [Measuring Open-Source Llama Nemotron Models on DeepResearch Bench]() Original publisher: [Read original article]() Author: Jay Rodge Published: 2025-08-04T19:51:50Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Nvidia](), [Open Source](), [hugging face](), [llama](), [Nemotron](), [Large Language Model](), [Open Source Models & Datasets](), [long-context](), [Inference](), [post-training](), [GPU](), [Reinforcement learning]() Tags: [agentic](), [ai](), [gpu](), [hallucination-detection](), [hugging-face](), [inference](), [llama](), [llm](), [long-context](), [nemotron](), [nvidia](), [open-source](), [post-training](), [reinforcement-learning]() ### AI overview The article presents NVIDIA's AI-Q Blueprint as a portable, open deep research agent that reached the top of Hugging Face's "LLM with Search" leaderboard on DeepResearch Bench. It combines Llama 3.3-70B Instruct with Llama-3.3-Nemotron-Super-49B-v1.5 for retrieval, reasoning, synthesis, query planning, and tool use, and incorporates NVIDIA NeMo Retriever and the NVIDIA NeMo Agent toolkit. The architecture emphasizes long-context, multimodal, low-latency search over local and web data, privacy, compliance, on-premise deployment, transparent model lineage, and efficient inference on GPUs. ### Source excerpt NVIDIA's AI-Q Blueprint--the leading portable, open deep research agent--recently climbed to the top of the Hugging Face "LLM with Search" leaderboard on DeepResearch Bench. This is a significant step forward for the open-source AI stack, proving that developer-accessible models can power advanced agentic workflows that rival or surpass closed alternatives. What sets AI-Q apart? ## Open challenges in LLM research DevFeed: [Open challenges in LLM research]() Original publisher: [Read original article]() Author: Chip Huyen Published: 2023-08-16T00:00:00Z Content type: opinion Language: en Sources: [Chip Huyen]() Topics: [Large Language Model](), [Hallucination detection](), [context](), [LLM evaluation / benchmarking](), [Chain-of-thought](), [multimodal]() Tags: [ai](), [chain-of-thought](), [challenges](), [context](), [evaluation](), [hallucination-detection](), [llm](), [multimodal](), [research]() ### AI overview This article surveys ten research directions aimed at improving large language models. The supplied excerpt focuses on reducing and measuring hallucinations, optimizing context length and construction, and briefly identifies multimodality, new architectures, and GPU alternatives as areas of interest. ### Source excerpt [LinkedIn discussion, Twitter thread] Never before in my life had I seen so many smart people working on the same goal: making LLMs better. After talking to many people working in both industry and academia, I noticed the 10 major research directions that emerged. The first two directions, hallucinations and context learning, are probably the most talked about today. I'm the most excited about numbers 3 (multimodality), 5 (new architecture), and 6 (GPU alternatives). 1. Reduce and measure hallucinations Hallucination is a heavily discussed topic already so I'll be quick. Hallucination happens when an AI model makes stuff up. For many creative use cases, hallucination is a feature. However, for most other use cases, hallucination is a bug. I was at a panel on LLM with Dropbox, Langchain, Elastics, and Anthropic recently, and the #1 roadblock they see for companies to adopt LLMs in production is hallucination. Mitigating hallucination and developing metrics to measure hallucination is a blossoming research topic, and I've seen many startups focus on this problem. There are also ad-hoc tips to reduce hallucination, such as adding more context to the prompt, chain-of-thought, self-consistency, or asking your model to be concise in its response. To learn more about hallucination: Survey of Hallucination in Natural Language Generation (Ji et al., 2022) How Language Model Hallucinations Can Snowball (Zhang et al., 2023) A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity (Bang et al., 2023) Contrastive Learning Reduces Hallucination in Conversations (Sun et al., 2022) Self-Consistency Improves Chain of Thought Reasoning in Language Models (Wang et al., 2022) SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models (Manakul et al., 2023) A simple example of fact-checking and hallucination by NVIDIA's NeMo-Guardrails 2. Optimize context length and context construction A vast ma