Papers by Haolun Wu
Optimizing User Profiles via Contextual Bandits for Retrieval-Augmented LLM Personalization (2026.acl-long)
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Linfeng Du, Ye Yuan, Zichen Zhao, Fuyuan Lyu, Emiliano Penaloza, Xiuying Chen, Zipeng Sun, Jikun Kang, Laurent Charlin, Xue Liu, Haolun Wu
| Challenge: | Existing approaches for personalizing large language models require modifying parameters. |
| Approach: | They propose a lightweight approach to personalizing large language models via retrieval augmentation . relevance serves as an unreliable proxy for utility, they argue . |
| Outcome: | The proposed framework outperforms strong heuristic and retrieval-augmented baselines on nine personalization tasks. |
Context-Fidelity Boosting: Enhancing Faithful Generation through Watermark-Inspired Decoding (2026.findings-acl)
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Weixu Zhang, Fanghua Ye, Qiang Gao, Jian Li, Haolun Wu, Yuxing Tian, Sijing Duan, Nan Du, Xiaolong Li
| Challenge: | Large language models produce content that contradicts or overlooks information provided in the input context, a phenomenon known as faithfulness hallucination. |
| Approach: | They propose a lightweight framework that boosts the generation probability of context-relevant tokens by boosting the generation of tokens. |
| Outcome: | The proposed framework improves faithfulness metrics with minimal generation overhead. |
Learning to Extract Structured Entities Using Language Models (2024.emnlp-main)
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| Challenge: | Language Models (LMs) play a pivotal role in extracting structured information from unstructured text. |
| Approach: | They propose to reformulate the task to be entity-centric, enabling the use of diverse metrics that can provide more insights from various perspectives. |
| Outcome: | The proposed model outperforms baselines and human evaluations on the extracted entities. |
LLM Safety From Within: Detecting Harmful Content with Internal Representations (2026.acl-long)
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| Challenge: | State-of-the-art guard models rely on terminal-layer representations and overlook safety-relevant features encoded across internal layers. |
| Approach: | They propose a lightweight guard model that harnesses safety neurons from LLM internals without modifying the underlying model. |
| Outcome: | The proposed model outperforms open-source guard models across multiple benchmarks while using 250 fewer trainable parameters. |
Learning to Generate Structured Output with Schema Reinforcement Learning (2025.acl-long)
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Yaxi Lu, Haolun Li, Xin Cong, Zhong Zhang, Yesai Wu, Yankai Lin, Zhiyuan Liu, Fangming Liu, Maosong Sun
| Challenge: | Recent advances in large language models have facilitated the development of intelligent applications like automatic web search (Qin et al., 2023) Several methods exist for generating JSON strings from LLMs, including Prompting but often miss certain schemas. |
| Approach: | They propose to use 40K different JSON schemas to assess models' ability to generate valid JSON outputs. |
| Outcome: | The proposed model improves both in generating JSON outputs and downstream tasks. |
Preference Heads in Large Language Models: A Mechanistic Framework for Interpretable Personalization (2026.acl-long)
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Weixu Zhang, Ye Yuan, Changjiang Han, Yuxing Tian, Zipeng Sun, Linfeng Du, Jikun Kang, Hong Kang, Xue Liu, Haolun Wu
| Challenge: | Large Language Models exhibit strong implicit personalization ability, but most approaches treat this behavior as a black box. |
| Approach: | They propose a mechanistic interpretation perspective and propose 'sparse' set of Preference Heads . they compute a Preference Contribution Score for each attention head and compare their predictions . |
| Outcome: | The proposed framework computes a Preference Contribution Score (PCS) for each attention head and measures its causal impact on user aligned outputs. |
GlossaGen: Making Academic Translation Smarter with Glossing (2026.findings-acl)
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| Challenge: | Existing machine translation systems obscure or mistranslate key terminology, while paraphrasing aimed at lay readers often oversimplifies it, hindering their ability to master domain-specific technical vocabulary. |
| Approach: | They propose a task which produces translations dynamically adapted to a reader’s academic proficiency, or level, and a framework to address this challenge. |
| Outcome: | The proposed framework achieves higher scores than baselines on a synthesized benchmark and human evaluations. |