Papers by Ruosen Li
FaithScore: Fine-grained Evaluations of Hallucinations in Large Vision-Language Models (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) lack the capacity to handle multimodal inputs effectively. |
| Approach: | They introduce a reference-free and fine-grained evaluation metric that measures the faithfulness of the generated free-form answers from large vision-language models. |
| Outcome: | The proposed metric measures the faithfulness of free-form answers from large vision-language models. |
Your Reasoning Model is Secretly a Reward Model - Optimization-Free Verification from Experience (2026.acl-long)
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| Challenge: | Existing verifiers operate on the surface text or on confidence proxies derived from token probabilities, which can be brittle. |
| Approach: | They propose a training-free, non-parametric verifier that summarizes each reasoning trace by an activation delta and compares it to two class centroids computed from labeled experience. |
| Outcome: | The proposed model improves selection and reranking on large and less-calibrated models. |
Leveraging Structured Information for Explainable Multi-hop Question Answering and Reasoning (2023.findings-emnlp)
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| Challenge: | Neural models, including large language models (LLMs), achieve superior performance on multi-hop question-answering tasks. |
| Approach: | They propose to use the chain-of-thought mechanism to generate both the reasoning chain and the answer. |
| Outcome: | Empirical results show that the proposed framework generates more faithful reasoning chains and significantly improves the QA performance on two benchmark datasets. |
FG-PRM: Fine-grained Hallucination Detection and Mitigation in Language Model Mathematical Reasoning (2025.findings-emnlp)
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| Challenge: | Existing methods to detect hallucinations in large language models lack nuanced understanding of their types and manifestations. |
| Approach: | They propose a taxonomy that categorizes hallucinations into six types . they propose an augmented model to detect and mitigate hallucinosity in a fine-grained manner . |
| Outcome: | The proposed model detects and mitigates hallucinations in a fine-grained manner . it significantly boosts the performance of LLMs on GSM8K and MATH benchmarks. |