Multi-AI collaboration helps reasoning and factual accuracy in large language models

Recommendable! This seems to be a powerful approach! I did not have time yet to read the underlying research paper (written by the two well known senior authors Joshua B. Tenenbaum, Igor Mordatch and two others).

"... The crux of the problem with large language models (LLMs) lies in the inconsistency of their generated responses, leading to potential inaccuracies and flawed reasoning. This new approach lets each agent actively assess every other agent’s responses, and uses this collective feedback to refine its own answer. In technical terms, the process consists of multiple rounds of response generation and critique. Each language model generates an answer to the given question, and then incorporates the feedback from all other agents to update its own response. This iterative cycle culminates in a final output from a majority vote across the models' solutions. It somewhat mirrors the dynamics of a group discussion — where individuals contribute to reach a unified and well-reasoned conclusion. ..."

Multi-AI collaboration helps reasoning and factual accuracy in large language models | MIT News | Massachusetts Institute of Technology

Improving Factuality and Reasoning in Language Models through Multiagent Debate