Summary of Llms Are Superior Feedback Providers: Bootstrapping Reasoning For Lie Detection with Self-generated Feedback, by Tanushree Banerjee et al.
LLMs are Superior Feedback Providers: Bootstrapping Reasoning for Lie Detection with Self-Generated Feedback
by Tanushree Banerjee, Richard Zhu, Runzhe Yang, Karthik Narasimhan
First submitted to arxiv on: 25 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 bootstrapping framework leverages self-generated feedback to enhance Large Language Models (LLMs) for lie detection. The framework consists of three stages: suggestion, feedback collection, and modification. In the suggestion stage, a cost-effective language model generates initial predictions based on game state and dialogue. The framework is applied to detecting betrayal and deception in Diplomacy games, with LLM-generated feedback exhibiting superior quality and significantly enhancing performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way for computers to learn from themselves about spotting lies. It uses a special kind of AI called Large Language Models (LLMs) that are good at understanding human language. The researchers created a process where the LLM makes predictions, then gives itself feedback on those predictions. This process helps the LLM get better and better at recognizing when someone is lying or telling the truth. |
Keywords
» Artificial intelligence » Bootstrapping » Language model
