Summary of Gratr: Zero-shot Evidence Graph Retrieval-augmented Trustworthiness Reasoning, by Ying Zhu et al.
GRATR: Zero-Shot Evidence Graph Retrieval-Augmented Trustworthiness Reasoning
by Ying Zhu, Shengchang Li, Ziqian Kong, Qiang Yang, Peilan Xu
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 GRATR framework is a zero-shot approach that enables large language models to identify potential allies and adversaries in multiplayer games with incomplete information. The framework uses graph retrieval to evaluate trustworthiness towards a target agent, considering evidence from multiple trusted sources. This approach outperforms the baseline method in reasoning accuracy by 50.5% and reduces hallucination by 30.6% in experiments using the game Werewolf. GRATR also surpasses the baseline in accuracy by 10.4% when tested on a Twitter dataset from the U.S. election period, demonstrating its potential in real-world applications such as intent analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers created a new way for computers to understand who they can trust or not in games where information is incomplete. They call it GRATR (Graph Retrieval-Augmented Trustworthiness Reasoning). It works by looking at what other players are doing and figuring out how that affects trust levels between them. Then, when making decisions, the computer looks for evidence from multiple trusted sources to decide who to trust or not. This approach was tested in a game called Werewolf and did better than other methods in identifying trustworthy allies and avoiding hallucinations. It even worked well on real-world data like Twitter posts during an election. |
Keywords
» Artificial intelligence » Hallucination » Zero shot
