Papers by Zexuan Li
Generating Diverse Training Samples for Relation Extraction with Large Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Existing models for Relation Extraction (RE) have good results on many benchmarks, but data scarcity is a common problem. |
| Approach: | They propose to use Large Language Models to generate training data for Relation Extraction . they propose to make LLMs produce dissimilar samples by direct instruction . |
| Outcome: | The proposed approach improves the diversity of training samples generated with LLMs while maintaining correctness. |
Enhanced Reasoning for Biomedical Document-Level Relation Extraction via a Novel Cascade Language Model Framework (2026.acl-long)
Copied to clipboard
| Challenge: | Pre-trained language models (PLMs) are the leading paradigm in document-level relation extraction. |
| Approach: | They propose a cascade framework that leverages the complementary strengths of PLMs and LLMs through a detect-then-rethink paradigm. |
| Outcome: | The proposed framework improves on BioRED and CDR datasets and improves existing models. |
Privacy Implications of Retrieval-Based Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | a study of retrieval-based language models shows improved interpretability, factuality, and adaptability compared to parametric counterparts . kNN-LMs are more susceptible to leaking private information from their private datastore than parametric models . |
| Approach: | They present the first study of privacy risks in retrieval-based language models . they aim to strike a balance between utility and privacy in domains where privacy is of concern . |
| Outcome: | The proposed methods improve interpretability, factuality, and adaptability compared to parametric models . the study finds that kNN-LMs are more susceptible to leaking private data than parametric ones . |
Discovery and Reinforcement of Tool-Integrated Reasoning Chains via Rollout Trees (2026.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to augment Large Language Models (LLMs) with computational capabilities have focused on short Chain-of-thought (CoT) integrating tool-use into long CoT remains underexplored due to the scarcity of training data and the challenge of integrating it without compromising the model’s intrinsic long-chain reasoning. |
| Approach: | They propose a framework that enables spontaneous tool-use during long CoT reasoning without additional human annotation. |
| Outcome: | Experiments on AIME and GPQA-Diamond show that DART significantly outperforms existing methods, successfully harmonizing tool execution with long CoT reasoning. |
Entropy-Based Decoding for Retrieval-Augmented Large Language Models (2025.naacl-long)
Copied to clipboard
| Challenge: | Despite their success, retrieval-augmented LLMs still face the distractibility issue, where the generated responses are negatively influenced by noise from both external and intrinsic knowledge sources. |
| Approach: | They propose a entropy-based document-parallel ensemble decoding method that prioritizes low-entropies from retrieved documents and incorporates a contrastive decoding mechanism that contrasts the obtained low- and high-entropic ensemble distributions with the high-end internal knowledge across layers. |
| Outcome: | The proposed method improves on open-domain question answering datasets and shows that it is highly efficient. |
CLongEval: A Chinese Benchmark for Evaluating Long-Context Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Developing long-context LLMs with robust long-text capabilities is underdeveloped due to a lack of benchmarks. |
| Approach: | They propose a Chinese benchmark for evaluating long-context LLMs with Chinese capabilities. |
| Outcome: | The proposed model is based on 6 open-source LLMs and 2 commercial ones. |
M-BRe: Discovering Training Samples for Relation Extraction from Unlabeled Texts with Large Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to extract training instances from unlabeled texts are expensive . sentences that contain the target relations in texts can be scarce and difficult to find . |
| Approach: | They propose a framework that can automatically extract training instances from unlabeled texts for RE. |
| Outcome: | The proposed method can extract training instances from unlabeled texts for RE. |
OneRec-Think: In-Text Reasoning for Generative Recommendation (2026.acl-long)
Copied to clipboard
Zhanyu Liu, Shiyao Wang, Xingmei Wang, Rongzhou Zhang, Jiaxin Deng, Honghui Bao, Jinghao Zhang, Wuchao Li, PengFei Zheng, Xiangyu Wu, Yifei Hu, Qigen Hu, Xinchen Luo, Lejian Ren, Zhang Zixing, Qianqian Wang, Kuo Cai, Yunfan Wu, Hongtao Cheng, Zexuan Cheng, Lu Ren, Huanjie Wang, Yi Su, Ruiming Tang, Kun Gai, Guorui Zhou
| Challenge: | Existing generative models lack the capacity for explicit and controllable reasoning, a key advantage of LLMs. |
| Approach: | They propose a framework that integrates dialogue, reasoning, and personalized recommendation. |
| Outcome: | Experiments across public benchmarks show state-of-the-art performance. |