Summary of Preemptive Detection and Correction Of Misaligned Actions in Llm Agents, by Haishuo Fang et al.
Preemptive Detection and Correction of Misaligned Actions in LLM Agents
by Haishuo Fang, Xiaodan Zhu, Iryna Gurevych
First submitted to arxiv on: 16 Jul 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 A novel approach, called InferAct, is introduced to address the critical challenge of misaligned behavior between language model-based (LLM) agents and user intent. The misalignment can lead to unintentional execution of critical actions with negative outcomes, such as accidentally triggering a “buy-now” in web shopping. To detect and correct these misaligned actions before they are executed, InferAct leverages the belief reasoning ability of LLMs grounded in Theory-of-Mind. The approach achieves up to 20% improvements on Marco-F1 against baselines in detecting misaligned actions, demonstrating its effectiveness in improving agent alignment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary InferAct is a new way to help language models make better decisions by understanding what people mean when they ask for something. Sometimes, these models do things we don’t want them to do because they don’t understand what we really meant. InferAct can detect when this happens and alert us so we can correct the mistake before it’s too late. |
Keywords
» Artificial intelligence » Alignment » Language model
