Papers by Hanlin Wang
Learning Personalized Alignment for Evaluating Open-ended Text Generation (2024.emnlp-main)
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| Challenge: | Traditional evaluation metrics rely heavily on lexical similarity with human-written references, showing poor correlation with human judgments and failing to account for alignment with the diversity of human preferences. |
| Approach: | They propose an interpretable evaluation framework that evaluates alignment with specific human preferences by providing detailed comments and fine-grained scoring. |
| Outcome: | The proposed framework outperforms GPT-4 in Kendall correlation and accuracy with zero-shot reviewers. |
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. |
Domaino1s: Guiding LLM Reasoning for Explainable Answers in High-Stakes Domains (2025.findings-acl)
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| Challenge: | Existing Large Language Models (LLMs) generate brief answers without reasoning processes and explanations. |
| Approach: | They propose supervised fine-tuning and tree search to enhance LLMs’ reasoning capabilities on domain tasks. |
| Outcome: | The proposed model improves on stock investment recommendation and legal reasoning QA tasks. |
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. |
STeCa: Step-level Trajectory Calibration for LLM Agent Learning (2025.findings-acl)
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| Challenge: | Existing work focuses on behavior cloning from expert demonstrations or preference learning through exploratory trajectory sampling, but these methods often struggle to address long-horizon tasks where suboptimal actions accumulate step by step, causing agents to deviate from correct task trajectories. |
| Approach: | They propose a framework for LLM-based agent learning that identifies suboptimal actions through a step-level reward comparison during exploration and constructs calibrated trajectories using LLM reflection. |
| Outcome: | The proposed framework outperforms existing methods in long-horizon tasks where suboptimal actions accumulate step by step, causing agents to deviate from correct task trajectories. |
Seeing Isn’t Believing: Mitigating Belief Inertia via Active Intervention in Embodied Agents (2026.findings-acl)
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| Challenge: | Existing large language models (LLMs) have enabled agents to tackle complex embodied tasks through environmental interaction, but they still make suboptimal decisions and perform ineffective actions. |
| Approach: | They propose an active belief intervention mechanism that generates explicit belief states . they characterize belief inertia as a key failure mode of LLM-based agents . |
| Outcome: | The proposed method achieves significant gains in task success rates across embodied benchmarks. |
MATCH: Modulating Attention via In-Context Retrieval for Long-Context Transformers (2026.acl-long)
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Linrui Ma, Chun Hei Lo, Xinyu Wang, Peng Lu, Xihao Yuan, Hanting Chen, Kai Han, Xinghao Chen, Chengjun Zhan, Hanlin xu, Yichun Yin, Lifeng Shang, Feng Wen, Boxing Chen, Yufei Cui
| Challenge: | Existing approaches to improve efficiency often enforce rigid structural constraints such as local attention windows. |
| Approach: | They propose a framework that augments sparse-attention mechanisms with dynamically integrated in-context information through an efficient retrieval system. |
| Outcome: | Empirical results show that MATCH significantly improves the performance of sparse-attention models on synthetic and real-world natural-language tasks. |
E2CL: Exploration-based Error Correction Learning for Embodied Agents (2024.findings-emnlp)
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| Challenge: | Language models are exhibiting increasing capability in knowledge utilization and reasoning, but they often suffer from misalignment between their intrinsic knowledge and environmental knowledge, leading to infeasible actions. |
| Approach: | They propose a framework that leverages exploration-induced errors and environmental feedback to enhance environment alignment for embodied agents. |
| Outcome: | The proposed framework outperforms baseline methods and exhibits superior self-correction capabilities. |