Evaluating the Correctness of Inference Patterns Used by LLMs for Judgment
Fuente:
arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916745527164928 |
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| author | Chen, Lu Huang, Yuxuan Li, Yixing Liu, Dongrui Ren, Qihan Zhao, Shuai Kuang, Kun Zheng, Zilong Zhang, Quanshi |
| author_facet | Chen, Lu Huang, Yuxuan Li, Yixing Liu, Dongrui Ren, Qihan Zhao, Shuai Kuang, Kun Zheng, Zilong Zhang, Quanshi |
| contents | This paper presents a method to analyze the inference patterns used by Large Language Models (LLMs) for judgment in a case study on legal LLMs, so as to identify potential incorrect representations of the LLM, according to human domain knowledge. Unlike traditional evaluations on language generation results, we propose to evaluate the correctness of the detailed inference patterns of an LLM behind its seemingly correct outputs. To this end, we quantify the interactions between input phrases used by the LLM as primitive inference patterns, because recent theoretical achievements have proven several mathematical guarantees of the faithfulness of the interaction-based explanation. We design a set of metrics to evaluate the detailed inference patterns of LLMs. Experiments show that even when the language generation results appear correct, a significant portion of the inference patterns used by the LLM for the legal judgment may represent misleading or irrelevant logic. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_09083 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evaluating the Correctness of Inference Patterns Used by LLMs for Judgment Chen, Lu Huang, Yuxuan Li, Yixing Liu, Dongrui Ren, Qihan Zhao, Shuai Kuang, Kun Zheng, Zilong Zhang, Quanshi Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition Machine Learning This paper presents a method to analyze the inference patterns used by Large Language Models (LLMs) for judgment in a case study on legal LLMs, so as to identify potential incorrect representations of the LLM, according to human domain knowledge. Unlike traditional evaluations on language generation results, we propose to evaluate the correctness of the detailed inference patterns of an LLM behind its seemingly correct outputs. To this end, we quantify the interactions between input phrases used by the LLM as primitive inference patterns, because recent theoretical achievements have proven several mathematical guarantees of the faithfulness of the interaction-based explanation. We design a set of metrics to evaluate the detailed inference patterns of LLMs. Experiments show that even when the language generation results appear correct, a significant portion of the inference patterns used by the LLM for the legal judgment may represent misleading or irrelevant logic. |
| title | Evaluating the Correctness of Inference Patterns Used by LLMs for Judgment |
| topic | Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2410.09083 |