Papers by Xiping Hu
CLHA: A Simple Yet Effective Contrastive Learning Framework for Human Alignment (2024.lrec-main)
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Feiteng Fang, Liang Zhu, Xi Feng, Jinchang Hou, Qixuan Zhao, Chengming Li, Xiping Hu, Ruifeng Xu, Min Yang
| Challenge: | Large language models (LLMs) have attracted considerable attention from academic and industrial communities due to their outstanding performance in various natural language processing tasks. |
| Approach: | They propose a Contrastive Learning Framework for Human Alignment to evaluate the noise within the data and dynamically adjust the training process. |
| Outcome: | The proposed framework surpasses other algorithms in terms of reward model scores, automatic evaluations, and human assessments on the widely used dataset "Helpful and Harmless" |
Forget for Get: A Lightweight Two-phase Gradient Method for Knowledge Editing in Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing knowledge editing methodologies often encounter parameter conflict during knowledge overwriting and excessive computational overhead. |
| Approach: | They propose a method that erases outdated knowledge and inserts new knowledge at the location that corresponds to the target knowledge. |
| Outcome: | The proposed method achieves more effective knowledge editing at a lower cost compared to previous methods across various base models. |
CPsyExam: A Chinese Benchmark for Evaluating Psychology using Examinations (2025.coling-main)
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Jiahao Zhao, Jingwei Zhu, Minghuan Tan, Min Yang, Renhao Li, Yang Di, Chenhao Zhang, Guancheng Ye, Chengming Li, Xiping Hu, Derek F. Wong
| Challenge: | CPsyExam prioritizes psychological knowledge and case analysis separately, recognizing the significance of applying psychological knowledge to real-world scenarios. |
| Approach: | They propose a psychological benchmark, CPsyExam, constructed from questions from Chinese examination systems. |
| Outcome: | The proposed benchmark prioritizes psychological knowledge and case analysis separately, recognizing the significance of applying psychological knowledge to real-world scenarios. |
Forgetting before Learning: Utilizing Parametric Arithmetic for Knowledge Updating in Large Language Models (2024.acl-long)
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| Challenge: | Existing methods of model editing and knowledge updating add additional network parameters, knowledge bases, knowledge base, and model parameters. |
| Approach: | They propose a new paradigm for fine-tuning called F-Learning that employs parametric arithmetic to facilitate the forgetting of old knowledge and learning of new knowledge. |
| Outcome: | The proposed model outperforms existing models on two datasets and is comparable to full fine-tuning and LoRA fine-uning. |
CPsyCoun: A Report-based Multi-turn Dialogue Reconstruction and Evaluation Framework for Chinese Psychological Counseling (2024.findings-acl)
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Chenhao Zhang, Renhao Li, Minghuan Tan, Min Yang, Jingwei Zhu, Di Yang, Jiahao Zhao, Guancheng Ye, Chengming Li, Xiping Hu
| Challenge: | Existing datasets lack consulting knowledge, resulting in LLMs lacking professional consulting competence. |
| Approach: | They propose a report-based multi-turn dialogue reconstruction framework for Chinese psychological counseling that uses large language models to assist counseling. |
| Outcome: | The proposed framework is open-source and can be used in future research. |
TP-Link: Fine-grained Pre-Training for Text-to-SQL Parsing with Linking Information (2024.lrec-main)
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Ziqiang Liu, Shujie Li, Zefeng Cai, Xiangyu Li, Yunshui Li, Chengming Li, Xiping Hu, Ruifeng Xu, Min Yang
| Challenge: | Existing pre-training frameworks for text-to-SQL parsing have shown inherent differences in distributions between tables and plain text. |
| Approach: | They propose a framework to improve context-dependent Text-to-SQL parsing by leveraging Linking information. |
| Outcome: | The proposed framework achieves state-of-the-art performance on two leading downstream benchmarks. |
Joint Document-Level Event Extraction via Token-Token Bidirectional Event Completed Graph (2023.acl-long)
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| Challenge: | a joint exaction method can be used to extract document-level event records . it avoids inefficiency and error propagation issues in traditional pipeline methods . |
| Approach: | They propose a joint exaction method that can avoid inefficiency and error propagation issues . they propose eType-Role1-Roul2 as the edge type to reveal which tokens play argument roles . |
| Outcome: | The proposed method can avoid inefficiency and error propagation issues in traditional pipeline methods. |
DEGAP: Dual Event-Guided Adaptive Prefixes for Templated-Based Event Argument Extraction with Slot Querying (2025.coling-main)
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| Challenge: | Recent advances in event argument extraction (EAE) involve incorporating useful auxiliary information into models during training and inference. |
| Approach: | They propose a method that uses two prefixes to learn from different events and templates. |
| Outcome: | The proposed method achieves state-of-the-art performance on four datasets . it can leverage possible connections between different events and capture relevant information from the prefix . |
E-EVAL: A Comprehensive Chinese K-12 Education Evaluation Benchmark for Large Language Models (2024.findings-acl)
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Jinchang Hou, Chang Ao, Haihong Wu, Xiangtao Kong, Zhigang Zheng, Daijia Tang, Chengming Li, Xiping Hu, Ruifeng Xu, Shiwen Ni, Min Yang
| Challenge: | despite the rapid development of Large Language Models, there is no dedicated benchmark for evaluating LLMs in Chinese K-12 education. |
| Approach: | They propose to develop a benchmark specifically tailored for Chinese K-12 education. |
| Outcome: | EVAL is the first evaluation benchmark specifically tailored for Chinese K-12 education. |
MoZIP: A Multilingual Benchmark to Evaluate Large Language Models in Intellectual Property (2024.lrec-main)
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Shiwen Ni, Minghuan Tan, Yuelin Bai, Fuqiang Niu, Min Yang, Bowen Zhang, Ruifeng Xu, Xiaojun Chen, Chengming Li, Xiping Hu
| Challenge: | Large language models (LLMs) have demonstrated impressive performance in various natural language processing tasks. |
| Approach: | They propose a benchmark for the evaluation of large language models in the IP domain . they also propose supervised multilingual large language model called MoZi . |
| Outcome: | The proposed model outperforms four well-known LLMs on the MoZIP benchmark . the most powerful ChatGPT does not reach the passing level . |