Summary of Adversarial Multi-agent Evaluation Of Large Language Models Through Iterative Debates, by Chaithanya Bandi and Abir Harrasse
Adversarial Multi-Agent Evaluation of Large Language Models through Iterative Debates
by Chaithanya Bandi, Abir Harrasse
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Multiagent Systems (cs.MA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework interprets large language models (LLMs) as advocates within an ensemble of interacting agents, allowing them to defend their answers and reach conclusions through a judge and jury system. This novel approach offers a more dynamic and comprehensive evaluation process compared to traditional human-based assessments or automated metrics. The framework’s key components include LLMs as advocates, a judge, and a jury. Comparative advantages are discussed, along with the motivation behind this framework and a probabilistic model for evaluating error reduction achieved by iterative advocate systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores a new way to evaluate the outputs of large language models (LLMs). Instead of using people or computers to check their answers, we propose letting LLMs themselves work together to decide if they’re right. This is like having a debate team where each member presents their answer and then a judge decides what’s correct. We think this approach is better than just asking humans or computers to check the answers because it’s more dynamic and helps us understand how well LLMs can work together. |
Keywords
» Artificial intelligence » Probabilistic model
