Papers by Hanlin Xiao
Zero-shot Event Detection Using a Textual Entailment Model as an Enhanced Annotator (2024.lrec-main)
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| Challenge: | Recent work proposed to use a pre-trained textual entailment model for event detection . but, those methods treated the TE model as a frozen annotator . |
| Approach: | They propose to use TE models to annotate large-scale unlabeled text and annotated data to fine-tune the TE model. |
| Outcome: | The proposed method outperforms baseline methods by 15% on the ACE05 dataset. |
Not All Citations Are Equal:Entropy-Guided Citation Selection for Noise-Resistant Medical LLM (2026.findings-acl)
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| Challenge: | Large language models have demonstrated extensive potential in medical applications . however, their practical deployment in healthcare faces significant challenges . |
| Approach: | They propose a training-free multi-turn reasoning framework and a post-training methodology that provides external knowledge support for large language models. |
| Outcome: | The proposed framework elicits internal thought, external thought, and fusion thought, with an entropy-based reward that encourages selective citation of beneficial external knowledge while penalizing noisy citations. |
NaturalCodeBench: Examining Coding Performance Mismatch on HumanEval and Natural User Queries (2024.findings-acl)
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| Challenge: | Large language models (LLMs) generate code for productive activities, but current benchmarks for code synthesis are oriented towards introductory tasks on algorithm and data science. |
| Approach: | They propose a code benchmark to mirror the complexity and variety of scenarios in real-world coding tasks. |
| Outcome: | The proposed benchmark improves on 39 large language models with close HumanEval scores and achieves an efficiency increase of more than 4 times. |
From A and B to A+B: Can Large Language Models Solve Compositional Math Problems? (2025.emnlp-main)
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| Challenge: | Existing studies that create problem variants by adding perturbations to a single problem focus on the interaction between problems. |
| Approach: | They propose a pipeline with 98.2% accuracy to combine two original problems with a logical connection and to evaluate LLMs' generalization ability on the compositional problems. |
| Outcome: | The proposed pipeline can combine two original problems with a logical connection to get a new math problem and evaluate its compositional generalization on the compositional problems. |