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Towards Mitigation of Hallucination for LLM-empowered Agents: Progressive Generalization Bound Exploration and Watchdog Monitor
Created by
Haebom
Author
Siyuan Liu, Wenjing Liu, Zhiwei Xu, Xin Wang, Bo Chen, Tao Li
Outline
In this paper, we present HalMit, a novel black-box monitoring framework for solving the hallucination problem of intelligent agents based on large-scale language models (LLMs). Hallucination of LLMs is a serious problem that undermines the reliability of intelligent agents by generating inconsistent outputs. Existing hallucination detection and mitigation approaches have limitations such as requiring white-box access to LLMs or having low accuracy. HalMit adopts a black-box approach to detect hallucinations by modeling the generalization boundary of LLM-based agents without knowledge of the internal structure of LLMs. It uses probabilistic fractal sampling technique to generate a sufficient number of queries to induce hallucinations in parallel and efficiently identify the generalization boundary of the target agent. Experimental results show that HalMit significantly outperforms existing approaches in hallucination monitoring performance.
Takeaways, Limitations
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Takeaways:
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A novel black-box approach to solving hallucination problems in LLM-based intelligent agents is presented.
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Development of the HalMit framework that shows superior hallucination detection performance compared to existing methods.
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Effective detection of hallucinations without access to the internal structure of the LLM.
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Efficient hallucination induction and detection using probabilistic fractal sampling techniques.
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Contributes to improving the reliability of LLM-based systems.
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Limitations:
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Further research is needed on the generalization performance and versatility of HalMit presented in this paper.
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Applicability to different types of LLM and agent environments needs to be verified.
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Additional validation of performance and stability in real-world environments is needed.
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Research is needed on optimal parameter settings for fractal sampling techniques.
