Summary of Agents on the Bench: Large Language Model Based Multi Agent Framework For Trustworthy Digital Justice, by Cong Jiang et al.
Agents on the Bench: Large Language Model Based Multi Agent Framework for Trustworthy Digital Justice
by Cong Jiang, Xiaolei Yang
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 paper proposes a novel AI framework called AgentsBench to improve the quality and transparency of judicial decision-making. The approach utilizes large language models (LLMs) to simulate collaborative deliberation among multiple agents, mimicking the real-world process of a judicial bench. Experiments on legal judgment prediction tasks demonstrate that AgentsBench outperforms existing LLM-based methods in terms of performance and decision quality. The framework aims to enhance accuracy, fairness, and societal consideration by reflecting real-world judicial processes more closely. This AI-powered decision-making approach has strong potential for application across various case types and legal scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using artificial intelligence (AI) to improve the justice system. Right now, AI helps make decisions faster, but it’s not very clear or transparent, which makes people worried. The authors created a new way called AgentsBench that uses big language models to mimic how judges work together. They tested it and found it did a better job than other methods in making good judgments. This approach can help make the justice system more fair and considerate of society. |
