Papers by Chunhui Li
M2DF: Multi-grained Multi-curriculum Denoising Framework for Multimodal Aspect-based Sentiment Analysis (2023.emnlp-main)
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| Challenge: | Existing work mainly utilizes image information to improve the performance of MABSA task. |
| Approach: | They propose a multimodal Aspect-based Sentiment Analysis task that uses image information to improve model performance. |
| Outcome: | The proposed framework outperforms state-of-the-art work on three sub-tasks of MABSA. |
EFUF: Efficient Fine-Grained Unlearning Framework for Mitigating Hallucinations in Multimodal Large Language Models (2024.emnlp-main)
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| Challenge: | Existing methods to eliminate hallucinations require expensive human annotation . hallucination in multimodal large language models poses unique challenges for current research . |
| Approach: | They propose a fine-grained unlearning framework that performs gradient ascent to eliminate hallucinations without paired data. |
| Outcome: | The proposed method reduces hallucinations while preserving quality with modest computational overhead. |
MultiSQL: A Schema-Integrated Context-Dependent Text2SQL Dataset with Diverse SQL Operations (2024.findings-acl)
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| Challenge: | Text2SQL is a task that translates natural language into SQL statements. |
| Approach: | They propose a task that translates natural language into SQL statements. |
| Outcome: | The proposed task enables users to convert natural language into SQL statements. |
Demystifying Uncertainty in LLMs: Active Calibration between Concepts and Human Evaluations (2026.acl-long)
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| Challenge: | Existing static strategies for mitigating hallucinations do not explicitly model the information gain from interacting with the external environment. |
| Approach: | They propose a calibration-driven interactive learning strategy that selects clarification queries by optimizing calibration error. |
| Outcome: | The proposed method provides theoretical guarantees and empirical gains for reliability. |