Papers by Pan Du
FuxiTranyu: A Multilingual Large Language Model Trained with Balanced Data (2024.emnlp-industry)
Copied to clipboard
Haoran Sun, Renren Jin, Shaoyang Xu, Leiyu Pan, null Supryadi, Menglong Cui, Jiangcun Du, Yikun Lei, Lei Yang, Ling Shi, Juesi Xiao, Shaolin Zhu, Deyi Xiong
| Challenge: | Large language models exhibit significant performance discrepancies between high- and low-resource languages. |
| Approach: | They present an open-source multilingual LLM with 8 billion parameters and a multilingual instruction dataset. |
| Outcome: | The proposed model achieves consistent multilingual representations across languages. |
Misalignment Attack on Text-to-Image Models via Text Embedding Optimization and Inversion (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Text embedding is a key component of modern NLP models but also poses additional risks. |
| Approach: | They propose a framework that optimizes embeddings and inverts them to obtain misaligned prompts. |
| Outcome: | The proposed framework exploits the continuity and distribution characteristics of text embeddings to obtain misaligned prompts of discrete tokens. |
Inductive Topic Variational Graph Auto-Encoder for Text Classification (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing methods for text classification do not assume explicit latent semantic structure of documents, making them less effective and difficult to interpret. |
| Approach: | They propose a model that integrates a topic model into variational graph-auto-encoder to capture hidden semantic information between documents and words. |
| Outcome: | The proposed model outperforms existing models on supervised and semi-supervised text classification and unsupervised representation learning. |
Graph Relational Topic Model with Higher-order Graph Attention Auto-encoders (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for learning low-dimensional representations of networked documents are largely ignored for document networks. |
| Approach: | They propose a graph relational topic model to explore document neighborhood information . the model can learn efficient networked document representations in the latent topic space . |
| Outcome: | The proposed model outperforms existing methods on unsupervised representation learning and other downstream tasks. |
MED-COPILOT: A Medical Assistant Powered by GraphRAG and Similar Patient Case Retrieval (2026.acl-demo)
Copied to clipboard
| Challenge: | MED-COPILOT is an interactive research prototype for evidence-aware clinical reasoning . large language models (LLMs) are prone to hallucinations and lack verifiable evidence grounding . |
| Approach: | They propose a system that integrates GraphRAG and semantic-keyword similar-patient retrieval to support transparent clinical reasoning. |
| Outcome: | The proposed system outperforms baseline and standard RAGs on clinical note completion and medical question answering. |
Making LLMs Better Many-to-Many Speech-to-Text Translators with Curriculum Learning (2025.acl-long)
Copied to clipboard
Yexing Du, Youcheng Pan, Ziyang Ma, Bo Yang, Yifan Yang, Keqi Deng, Xie Chen, Yang Xiang, Ming Liu, Bing Qin
| Challenge: | Existing studies on English-centric translation tasks have focused on multimodal large language models, but the exploration of many-to-many translation is limited by the scarcity of parallel data. |
| Approach: | They propose a three-stage curriculum learning strategy that leverages the machine translation capabilities of large language models and adapts them to S2TT tasks. |
| Outcome: | The proposed strategy achieves state-of-the-art average performance in 1514 language pairs, requiring fewer than 10 hours of speech data per language to achieve competitive results. |
Mitigating Context Interference for Reliable and Efficient Search Agents (2026.acl-long)
Copied to clipboard
Boyang Xue, Bin Wu, Shuofei Qiao, Sheng Wang, Rui Wang, Yiming Du, Hongru Wang, Jeff Z. Pan, Emine Yilmaz, Kam-Fai Wong, Aldo Lipani
| Challenge: | Recent research empowers Large Language Models (LLMs) as multi-turn search agents to iteratively retrieve and generate outputs until complex tasks are solved. |
| Approach: | They propose a distill-based context refiner to dynamically mitigate context interference . they also propose RLs that refine contexts to generate outputs . |
| Outcome: | The proposed refiner can mitigate context interference in multi-turn search agents. |
AntiLeakBench: Preventing Data Contamination by Automatically Constructing Benchmarks with Updated Real-World Knowledge (2025.acl-long)
Copied to clipboard
Xiaobao Wu, Liangming Pan, Yuxi Xie, Ruiwen Zhou, Shuai Zhao, Yubo Ma, Mingzhe Du, Rui Mao, Anh Tuan Luu, William Yang Wang
| Challenge: | Existing studies solve this challenge by updating benchmarks with newly collected data, but they fail to guarantee contamination-free evaluation as the newly collected knowledge may contain pre-existing knowledge. |
| Approach: | They propose an automated anti-leakage benchmarking framework that builds and updates benchmarks without human labor instead of using newly collected data. |
| Outcome: | The proposed framework significantly reduces the cost of benchmark maintenance to accommodate emerging LLMs. |
Augmenting Legal Judgment Prediction with Contrastive Case Relations (2022.coling-1)
Copied to clipboard
| Challenge: | Existing legal judgment prediction methods only consider one case fact description as input, which may not fully utilize information in the data such as case relations and frequency. |
| Approach: | They propose a new perspective that introduces some contrastive case relations to construct case triples as input and a corresponding judgment prediction framework with case triple modeling. |
| Outcome: | The proposed framework can be used to refine encoding and decoding processes using three customized modules on two public datasets. |
MCGA: A Multi-task Classical Chinese Literary Genre Audio Corpus (2026.findings-acl)
Copied to clipboard
Yexing Du, Kaiyuan Liu, Bihe Zhang, Youcheng Pan, Bo Yang, Liangyu Huo, Xiyuan Zhang, Jian Xie, Daojing He, Yang Xiang, Ming Liu, Bing Qin
| Challenge: | Multimodal Large Language Models (MLLMs) have advanced Chinese Classical Studies (CCS) but the audio dimension of CCS remains underexplored due to a lack of high-quality, domain-specific audio corpora. |
| Approach: | They propose a 119-hour audio corpus comprising 22,000 audio samples to bridge this gap . it encompasses a diverse range of literary genres across six tasks . |
| Outcome: | The proposed corpus encompasses a diverse range of literary genres across six tasks: Automatic Speech Recognition (ASR), Speech-to-Text Translation (S2TT), Speech Emotion Captioning (SEC), Spoken Question Answering ( SQA), Speech Understanding (SU), and Speech Reasoning (SR). |
ProjectEval: A Benchmark for Programming Agents Automated Evaluation on Project-Level Code Generation (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing benchmarks lack the ability to automatically evaluate from users’ perspective and lack the explainability of the results of LLM agents’ code generation capabilities. |
| Approach: | They propose a new benchmark for LLM agents' automated evaluation by simulating user interaction. |
| Outcome: | The proposed benchmark can evaluate the generated projects by user interaction simulation and by code similarity through existing objective indicators. |
Leveraging Information Bottleneck for Scientific Document Summarization (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to extract salient sentences from document are unsupervised and rely on graph-based methods for sentence ranking. |
| Approach: | They propose an unsupervised extractive approach to document level summarization based on the Information Bottleneck principle. |
| Outcome: | The proposed framework can be extended to a multi-view framework by different signals. |
COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values (2026.findings-eacl)
Copied to clipboard
Siwei Wu, JinCheng Ren, Xeron Du, Shuyue Guo, Xingwei Qu, Yiming Liang, Jie Liu, Yunwen Li, Tyler Loakman, Tianyu Zheng, Boyu Feng, Huaqing Yuan, Zili Wang, Jiaheng Liu, Wenhao Huang, Chenglin Cai, Haoran Que, Jian Yang, Yuelin Bai, Zekun Moore Wang, Zhouliang Yu, Qunshu Lin, Ding Pan, Yuchen Eleanor Jiang, Tiannan Wang, Wangchunshu Zhou, Shenzhi Wang, Xingyuan Bu, Minghao Liu, Guoyin Wang, Ge Zhang, Chenghua Lin
| Challenge: | Existing Chinese preference datasets suffer from limited scale, restricted domain coverage, and insufficiently rigorous data validation. |
| Approach: | They propose an LLM-based data annotation pipeline with no human intervention to annotate Chinese preference datasets. |
| Outcome: | The proposed pipeline outperforms existing Chinese preference datasets on AlignBench and Chinese Reward Benchmark. |
Batched Self-Consistency Improves LLM Relevance Assessment and Ranking (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing work has focused on a one-by-one pointwise (PW) scoring strategy where each LLM call judges one passage at a time against the query. |
| Approach: | They propose to use batched PW methods to evaluate multiple passages per LLM call to improve efficiency and judgment quality by enabling content from multiple passage to be seen jointly. |
| Outcome: | The proposed methods improve efficiency and judgment quality by enabling content from multiple passages to be seen jointly. |