# few-shot Published articles for few-shot. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Собственный контент-фильтр на базе LLM: от эксперимента до стабильной системы DevFeed: [Собственный контент-фильтр на базе LLM: от эксперимента до стабильной системы]() Original publisher: [Read original article]() Author: Martianov (red\_mad\_robot) Published: 2025-06-27T15:23:17Z Content type: tutorial Language: ru Sources: [Redmadrobot EN](), [Redmadrobot RU]() Topics: [Large Language Model](), [Artificial Intelligence](), [llama]() Tags: [ai](), [false-positive](), [few-shot](), [few-shot-learning](), [filter](), [llama](), [llm](), [tag-6faff4be08e9](), [tag-b6293b9d443d]() ### AI overview The article describes how red_mad_robot developed a proprietary LLM-based content filter for AI services. It explains why heuristic methods such as regular expressions and keyword filters produced false positives and were easy to bypass, and outlines the use of real anonymized user queries and a structured model prompt to improve moderation. ### Source excerpt Привет! Меня зовут Миша Мартьянов, я инженер по исследованиям и разработке в red_mad_robot. Моя работа -- искать новые идеи, проверять гипотезы и улучшать продукты. На этом пути иногда приходится изобретать уникальные решения. Например, мы создали собственный фильтр, чтобы отсеивать нежелательный контент с помощью LLM. Рассказываю, как мы к этому пришли и с какими сложностями столкнулись. Читать далее ## Social learning: Collaborative learning with large language models DevFeed: [Social learning: Collaborative learning with large language models]() Original publisher: [Read original article]() Author: Google AI (noreply@blogger.com) Published: 2024-03-07T18:15:00Z Content type: article Language: en Sources: [Google Research]() Topics: [Large Language Model](), [Federated Learning](), [datasets](), [Learning]() Tags: [datasets](), [federated-learning](), [few-shot](), [few-shot-learning](), [large-language-models](), [machine-intelligence](), [natural-language-processing](), [privacy](), [research](), [use-cases]() ### AI overview This Google Research article presents a privacy-aware social learning framework in which large language models teach one another using natural language rather than shared gradients. It describes evaluations involving spam detection, grade-school mathematics, and text-based question answering, including settings where labeled examples cannot be directly shared because of privacy concerns. ### Source excerpt Posted by Amirkeivan Mohtashami, Research Intern, and Florian Hartmann, Software Engineer, Google Research Large language models (LLMs) have significantly improved the state of the art for solving tasks specified using natural language, often reaching performance close to that of people. As these models increasingly enable assistive agents, it could be beneficial for them to learn effectively from each other, much like people do in social settings, which would allow LLM-based agents to improve each other's performance. To discuss the learning processes of humans, Bandura and Walters described the concept of social learning in 1977, outlining different models of observational learning used by people. One common method of learning from others is through a verbal instruction (e.g., from a teacher) that describes how to engage in a particular behavior. Alternatively, learning can happen through a live model by mimicking a live example of the behavior. Given the success of LLMs mimicking human communication, in our paper "Social Learning: Towards Collaborative Learning with Large Language Models", we investigate whether LLMs are able to learn from each other using social learning. To this end, we outline a framework for social learning in which LLMs share knowledge with each other in a privacy-aware manner using natural language. We evaluate the effectiveness of our framework on various datasets, and propose quantitative methods that measure privacy in this setting. In contrast to previous approaches to collaborative learning, such as common federated learning approaches that often rely on gradients, in our framework, agents teach each other purely using natural language. Social learning for LLMs To extend social learning to language models, we consider the scenario where a student LLM should learn to solve a task from multiple teacher entities that already know that task. In our paper, we evaluate the student's performance on a variety of tasks, such as spam detection i ## Expert-in-the-loop generative AI for text generation at Stitch Fix DevFeed: [Expert-in-the-loop generative AI for text generation at Stitch Fix]() Original publisher: [Read original article]() Published: 2023-03-06T09:00:00Z Content type: article Language: en Sources: [Stitch Fix]() Topics: [Generative AI](), [Large Language Model](), [text-generation](), [Deep learning](), [data](), [Embeddings]() Tags: [data](), [deep-learning](), [embeddings](), [few-shot](), [generative-ai](), [large-language-models-llms](), [text-generation]() ### AI overview Stitch Fix describes using generative AI and large language models for text-generation tasks, including advertisement headlines and product descriptions, with human experts involved in the process. ### Source excerpt Generative AI has been gaining attention and popularity in recent years. Made possible with advances in deep learning algorithms and trained with previously unimaginable amounts of data, generative AI has already contributed many real-world use cases, from creating realistic images with systems like DALL-E 2 and Midjourney, to generating human-like responses with ChatGPT. At Stitch Fix, we are constantly exploring innovative ways to utilize the latest advancements in AI and ML to enhance the experiences of our clients. In this blog post, we will delve into our approach to generative AI, with a special focus on our text generation use cases. By combining algo-generated text with a human expert-in-the-loop approach, we aim to streamline tasks such as crafting engaging advertisement headlines and producing high-fidelity product descriptions. Algo-generated Ad Headlines Generative AI in the text space is powered by large language models (LLMs) that are pre-trained on vast amounts of data (for example, GPT-3 is pre-trained on nearly the entire internet) and can understand and generate natural language. However, once pre-trained, it can generalize from very limited amounts of data and is capable of performing a wide variety of natural language tasks such as Q&A, translation, summarization, and text generation. This few-shot learning capability, which relies only on a few examples to make predictions, makes it especially well suited for tasks that require creativity and originality, such as crafting compelling ad headlines. Ad headlines are often the first interaction with potential clients, so it's crucial to make them engaging. Traditional marketing requires a copywriter to write new headlines for every new ad asset, which can be time consuming and costly, and may not always result in unique copy. Using generative AI, such as GPT-3, we can quickly generate a large number of headlines tailored to our brand tone and messaging. We achieve this by using a combination of late