Google AI Blog March 7, 2024 By Google AI

Social learning: Collaborative learning with large language models

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Google AI Blog.

arXiv AI

3d ago

From Solo to Social Learning: Characterizing Recursive Social Improvement in LLMs

arXiv:2609.38516v1 Announce Type: cross Abstract: Large language models (LLMs) can now improve themselves by revising the instructions they follow, and LLM agents are increasingly orchestrated to wor...

By Kunal Jha, Max Kleiman-Weiner, Natasha Jaques

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Mar 14, 2024

Cappy: Outperforming and boosting large multi-task language models with a small scorer

Posted by Yun Zhu and Lijuan Liu, Software Engineers, Google Research Large language model (LLM) advancements have led to a new paradigm that unifies various natural language processing (NLP) tasks within an instruction-following framework. This paradigm is exemplified by recent multi-task LLMs, such as T0 , FLAN , and OPT-IML .

By Google AI

llms nlp reinforcement-learning fine-tuning safety

Berkeley AI Research

Sep 1, 2025

What exactly does word2vec learn?

What exactly does word2vec learn, and how? Answering this question amounts to understanding representation learning in a minimal yet interesting language modeling task.

llms rag diffusion nlp benchmarks

Berkeley AI Research

Mar 13

Identifying Interactions at Scale for LLMs

--> Understanding the behavior of complex machine learning systems, particularly Large Language Models (LLMs), is a critical challenge in modern artificial intelligence. Interpretability research aims to make the decision-making process more transparent to model builders and impacted humans, a step toward safer and more trustworthy AI.

llms rag nlp efficiency benchmarks safety

arXiv AI

Jun 9

Some hypotheses on how chatbots work in problem-solving-driven conversations. Large Language Models as confirmation of the Innovation Illusion

arXiv:2606. 07722v1 Announce Type: new Abstract: This article offers a perspective on the nature of chatbots as genuine conversation partners when discussing problems in relation to their solutions.

By S. F. M. van Vlijmen, H. D. Lethe jr

llms

arXiv AI

Jun 16

Lect\=uraAgents: A Multi-Agent Framework for Adaptive Personalized AI-Assisted Learning and Embodied Teaching

arXiv:2606. 16428v1 Announce Type: cross Abstract: Effective personalized AI-assisted learning demands systems that can not only generate accurate learner-specific educational materials, but also dynamically adapt their instruction to diverse learners.

By Jaward Sesay, Yue Yu, Siwei Dong, Yemin Shi, Guangyao Chen, B\"orje F. Karlsson

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