FG-PRM: Fine-grained Hallucination Detection and Mitigation in Language Model Mathematical Reasoning
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866912590844657664 |
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| author | Li, Ruosen Luo, Ziming Du, Xinya |
| author_facet | Li, Ruosen Luo, Ziming Du, Xinya |
| contents | Hallucinations in large language models (LLMs) pose significant challenges in tasks requiring complex multi-step reasoning, such as mathematical problem-solving. Existing approaches primarily detect the presence of hallucinations but lack a nuanced understanding of their types and manifestations. In this paper, we first introduce a comprehensive taxonomy that categorizes the common hallucinations in mathematical reasoning tasks into six types. We then propose FG-PRM (Fine-Grained Process Reward Model), an augmented model designed to detect and mitigate hallucinations in a fine-grained, step-level manner. To address the limitations of manually labeling training data, we propose an automated method for generating fine-grained hallucination data using LLMs. Our FG-PRM demonstrates superior performance across two key tasks: 1) Fine-grained hallucination detection: classifying hallucination types for each reasoning step; and 2) Verification: ranking multiple LLM-generated outputs to select the most accurate solution. Our experiments show that FG-PRM excels in fine-grained hallucination detection and substantially boosts the performance of LLMs on GSM8K and MATH benchmarks. These results highlight the benefits of fine-grained supervision in enhancing the reliability and interpretability of LLM reasoning processes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_06304 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FG-PRM: Fine-grained Hallucination Detection and Mitigation in Language Model Mathematical Reasoning Li, Ruosen Luo, Ziming Du, Xinya Computation and Language Hallucinations in large language models (LLMs) pose significant challenges in tasks requiring complex multi-step reasoning, such as mathematical problem-solving. Existing approaches primarily detect the presence of hallucinations but lack a nuanced understanding of their types and manifestations. In this paper, we first introduce a comprehensive taxonomy that categorizes the common hallucinations in mathematical reasoning tasks into six types. We then propose FG-PRM (Fine-Grained Process Reward Model), an augmented model designed to detect and mitigate hallucinations in a fine-grained, step-level manner. To address the limitations of manually labeling training data, we propose an automated method for generating fine-grained hallucination data using LLMs. Our FG-PRM demonstrates superior performance across two key tasks: 1) Fine-grained hallucination detection: classifying hallucination types for each reasoning step; and 2) Verification: ranking multiple LLM-generated outputs to select the most accurate solution. Our experiments show that FG-PRM excels in fine-grained hallucination detection and substantially boosts the performance of LLMs on GSM8K and MATH benchmarks. These results highlight the benefits of fine-grained supervision in enhancing the reliability and interpretability of LLM reasoning processes. |
| title | FG-PRM: Fine-grained Hallucination Detection and Mitigation in Language Model Mathematical Reasoning |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2410.06304 |