Papers by Kan Xu
V-DPO: Mitigating Hallucination in Large Vision Language Models via Vision-Guided Direct Preference Optimization (2024.findings-emnlp)
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| Challenge: | Existing large vision-language models suffer from hallucination due to over-reliance on the Large Language Model (LLM) backbone. |
| Approach: | They propose a method to improve visual context learning by using a large-scale preference learning algorithm to improve hallucination. |
| Outcome: | The proposed method improves on human-annotated hallucination datasets. |
CorefDiffs: Co-referential and Differential Knowledge Flow in Document Grounded Conversations (2022.coling-1)
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| Challenge: | Document-grounded dialogs need smooth transitions between knowledge selected for generating responses. |
| Approach: | They propose a multi-document co-referential graph to capture inter- and intra-document relationships . they propose 'Coref-MDG' method to linearize static Coref-mDG into conversational sequence logic. |
| Outcome: | The proposed method outperforms the state-of-the-art by 9.5%, 7.4% and 8.2% on three public benchmarks. |
Towards IP Intelligence: Benchmarking Large Language Models on Intellectual Property Knowledge and Practice (2026.findings-acl)
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Qiyao Wang, Guhong Chen, Hongbo Wang, Huaren Liu, Minghui Zhu, Zhifei Qin, Li Linwei, Yilin Yue, Shiqiang Wang, Jiayan Li, Wu Yihang, Ziqiang Liu, Longze Chen, Run Luo, Liyang Fan, Jiaming Li, Lei Zhang, Kan Xu, Hamid Alinejad-Rokny, Chengming Li, Shiwen Ni, Yuan Lin, Min Yang
| Challenge: | Existing datasets and benchmarks focus only on patents or cover limited aspects of the IP field, lacking alignment with real-world scenarios. |
| Approach: | They propose a bilingual IP task taxonomy and a large-scale bilingual benchmark to evaluate LLMs in real-world IP practice. |
| Outcome: | The proposed model achieves only 75.8% accuracy, indicating room for improvement . open-source IP and law-oriented models lag behind closed-source general-purpose models . |
Hierarchical Inductive Transfer for Continual Dialogue Learning (2022.findings-acl)
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| Challenge: | Existing frameworks for learning and deployment of neural dialogue models have been used for online chit-chat scenarios. |
| Approach: | They propose a hierarchical inductive transfer framework to learn and deploy dialogue skills continually and efficiently. |
| Outcome: | The proposed framework achieves comparable performance under deployment-friendly model capacity. |
Regularizing Dialogue Generation by Imitating Implicit Scenarios (2020.emnlp-main)
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| Challenge: | Existing models for dialogue generation lack the flexibility to handle such freedoms. |
| Approach: | They propose to take into account dialogue history and future conversation to implicitly reconstruct the scenario knowledge. |
| Outcome: | The proposed approach outperforms state-of-the-art models on diversity and relevance and expresses scenario-specific knowledge. |
Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models (2024.findings-acl)
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Ran Xu, Hejie Cui, Yue Yu, Xuan Kan, Wenqi Shi, Yuchen Zhuang, May Dongmei Wang, Wei Jin, Joyce Ho, Carl Yang
| Challenge: | Clinical natural language processing (NLP) is a subfield that requires the extraction, analysis, and interpretation of unstructured clinical text. |
| Approach: | They propose a model which infuses knowledge into clinical text generation with LLMs for clinical NLP tasks. |
| Outcome: | The proposed model improves performance across 8 clinical NLP tasks and 18 datasets by 7.7%-8.7% on average. |
EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction (2025.naacl-long)
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| Challenge: | EASYTOOL combines tools from diverse tool documentation into a single tool instruction. |
| Approach: | They propose a framework that transforms tool documentation into a unified tool instruction. |
| Outcome: | EASYTOOL combines extensive tool documentation into a concise tool instruction . it reduces token consumption and improves performance of LLM-based agents . |
ASTRA: An Automated Framework for Strategy Discovery, Retrieval, and Evolution for Jailbreaking LLMs (2026.acl-long)
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| Challenge: | Existing methods lack the capability for continuous learning and self-evolution from interactions, limiting the diversity and adaptability of attack strategies. |
| Approach: | They propose an automated framework capable of discovering, retrieving, and evolving attack strategies. |
| Outcome: | The proposed framework outperforms existing baselines in a black-box setting. |
WECA: A WordNet-Encoded Collocation-Attention Network for Homographic Pun Recognition (D18-1)
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Yufeng Diao, Hongfei Lin, Di Wu, Liang Yang, Kan Xu, Zhihao Yang, Jian Wang, Shaowu Zhang, Bo Xu, Dongyu Zhang
| Challenge: | Homographic puns have a long history in human writing, widely used in written and spoken literature, which intended as jokes. |
| Approach: | They propose a WordNet-encoded model to settle polysemy of homographic puns and a word weighted model for recognizing them. |
| Outcome: | The proposed model can distinguish between homographic pun and non-homographic pun texts. |