Fine-grained Hallucination Detection and Mitigation in Language Model Mathematical Reasoning
Large Language Model;Process Reward Model;Data Augmentation
This paper is desk rejected for being substantially similar to the COLING 2025 submission "Fine-grained Hallucination Mitigation and Detection in Language Model Reasoning", which is ID 2476 at COLING. The paper shares the same content, nearly identical figures, nearly identical tables, and large similarities in text. Dual submissions are not allowed at ICLR.
Submission Desk Rejected by Program Chairs
@misc{
li2024finegrained,
title={Fine-grained Hallucination Detection and Mitigation in Language Model Mathematical Reasoning},
author={Ruosen Li and Ziming Luo and Xinya Du},
year={2024},
url={https://openreview.net/forum?id=7bwE5MJAVJ}
}
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 task into six types: fabrication, factual inconsistency, context inconsistency, instruction inconsistency, logical inconsistency, and logical error. 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. By injecting hallucinations into reasoning steps of correct solutions, we create a diverse and balanced synthetic dataset for training FG-PRM, which consists of six specialized Process Reward Models (PRMs), each tailored to detect a specific hallucination type. 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, mitigating reasoning hallucinations. Our experiments show that FG-PRM outperforms ChatGPT-3.5 and Claude-3 on fine-grained hallucination detection and substantially boosts the performance of LLMs on GSM8K and MATH benchmarks.
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Ruosen Li Ziming Luo Xinya Du
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Large Language Model Process Reward Model Data Augmentation
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li|finegrained_hallucination_detection_and_mitigation_in_language_model_mathematical_reasoning
/pdf/1663fa4c52e1a6d03f2f3bc40d68e31cdf2a8bcb.pdf
generative models
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Fine-grained Hallucination Detection and Mitigation in Language Model Mathematical Reasoning
ICLR 2025 Conference Desk Rejected Submission
ICLR.cc/2025/Conference/Desk_Rejected_Submission