Papers by Yuxuan Du
An Information Bottleneck Perspective for Effective Noise Filtering on Retrieval-Augmented Generation (2024.acl-long)
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Kun Zhu, Xiaocheng Feng, Xiyuan Du, Yuxuan Gu, Weijiang Yu, Haotian Wang, Qianglong Chen, Zheng Chu, Jingchang Chen, Bing Qin
| Challenge: | Experimental results show that retrieval-augmented generation improves accuracy and relevance of large language models. |
| Approach: | They propose to introduce the information bottleneck theory into retrieval-augmented generation by maximizing mutual information between compression and ground output while minimizing mutual information . |
| Outcome: | The proposed approach improves accuracy and correctness of answer generation and conciseness with 2.5% compression rate. |
Cross-Lingual Question Answering over Knowledge Base as Reading Comprehension (2023.findings-eacl)
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| Challenge: | Existing high-quality xMRC datasets can be further utilized to fine-tune our model. |
| Approach: | They propose a cross-lingual question answering over knowledge base approach that converts KB subgraphs into passages to narrow the gap between KB schemas and questions. |
| Outcome: | The proposed approach outperforms baselines and achieves strong few-shot and zero-shot performance on two xKBQA datasets in 12 languages. |
Spiral of Silence in Large Language Model Agents (2025.findings-emnlp)
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| Challenge: | Existing theories of Spiral of Silence do not apply to large language models . |
| Approach: | They propose an evaluation framework for examining SoS in large language models . they consider four controlled conditions that vary the availability of "History" and "Persona" signals . |
| Outcome: | The proposed framework examines the SoS-like dynamics in large language models . it shows that history and persona together produce strong majority dominance . |
Towards Reliable Large Audio Language Model (2025.findings-acl)
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Ziyang Ma, Xiquan Li, Yakun Song, Wenxi Chen, Chenpeng Du, Jian Wu, Yuanzhe Chen, Zhuo Chen, Yuping Wang, Yuxuan Wang, Xie Chen
| Challenge: | Recent advances in large audio language models (LALMs) have demonstrated impressive results and promising prospects in universal understanding and reasoning across speech, music, and general sound. |
| Approach: | They propose to use training-free and training-based methods to enhance LALM reliability to different extents. |
| Outcome: | The proposed methods improve the reliability of large audio language models to different extents. |
Each graph is a new language: Graph Learning with LLMs (2025.findings-acl)
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| Challenge: | Natural language is used to describe graphs, but graph descriptions become verbose and only relying on attribute embeddings limits LLM’s ability to capture adequate graph structural information. |
| Approach: | They propose a graph-defined language for large language model that translates the graph into a corpus instead of graph descriptions and pre-trains LLMs on this corpus to adequately understand the graph. |
| Outcome: | Experiments on five datasets show that the proposed framework outperforms description-based and embedding-based baselines by efficiently modeling different orders of neighbors. |
MTP-RL: Acceleration of Reinforcement Learning Rollouts with Policy-Aligned Multi-Token Prediction (2026.findings-acl)
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| Challenge: | Reinforcement learning (RL) is widely applied to boost the performance of pretrained models, yet its training efficiency is severely constrained by rollout generation. |
| Approach: | They propose a framework that accelerates the rollout phase for diverse models by equipping a pipeline to equip the multi-layer parameter-sharing MTP for all models and an advantage-aware MTP optimization strategy. |
| Outcome: | The proposed framework achieves stable growth of acceptance length during RL training, and also accelerates RL rollouts, achieving an average 23.1%–55.3% reduction in rollout time compared to baselines. |
ATLAS: Agent Tuning via Learning Critical Steps (2025.findings-acl)
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| Challenge: | Existing agent tuning approaches employ supervised finetuning on entire expert trajectories, but behavior-cloning of full traitories introduces expert bias and weakens generalization to states not covered by the expert data. |
| Approach: | They propose a method that finetunes LLMs on critical steps in expert trajectories and identifies and finetuns them on these steps with reduced costs. |
| Outcome: | The proposed method outperforms existing methods and open-source LLM agents on only 30% critical steps in extensive experiments. |
SWE-Mutation: Can LLMs Generate Reliable Test Suites in Software Engineering? (2026.findings-acl)
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Yuxuan Sun, Yuze Zhao, Yufeng Wang, Yao Du, Zhiyuan Ma, Jinbo Wang, Mengdi Zhang, Kai Zhang, Zhenya Huang
| Challenge: | Evaluating software engineering capabilities is a core component of large language models (LLMs). |
| Approach: | They propose a benchmark to evaluate LLM-generated test suites that introduces mutated solutions that attempt to "fool" them. |
| Outcome: | The proposed test suites are based on 2,636 mutated variants derived from 800 original instances and include a multilingual subset spanning nine programming languages. |
Medical Dialogue System: A Survey of Categories, Methods, Evaluation and Challenges (2024.findings-acl)
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Xiaoming Shi, Zeming Liu, Li Du, Yuxuan Wang, Hongru Wang, Yuhang Guo, Tong Ruan, Jie Xu, Xiaofan Zhang, Shaoting Zhang
| Challenge: | Existing medical dialogue systems have significant potential to simplify diagnostic procedure and reduce the cost of collecting information from patients. |
| Approach: | They analyze 325 papers from well-known computer science, natural language processing conferences and journals to find out the major challenges of medical dialog systems. |
| Outcome: | The proposed systems have been surveyed in the medical community but have not been evaluated from a technical perspective. |
RRInf: Efficient Influence Function Estimation via Ridge Regression for Large Language Models and Text-to-Image Diffusion Models (2025.emnlp-main)
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| Challenge: | Influence function estimates the impact of training data on model predictions . high computational cost has hindered their applicability in large-scale applications. |
| Approach: | They propose a method to quantify the impact of training data on model predictions . they use a ridge regression problem to transform the estimation into a problem . |
| Outcome: | The proposed method outperforms existing methods on noisy data detection and influential data identification tasks. |
NiuTrans.LMT: Toward Inclusive and Scalable Multilingual Machine Translation with LLMs (2026.acl-long)
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Yingfeng Luo, Ziqiang Xu, Yuxuan Ouyang, MuRun Yang, DingYang Lin, Kaiyan Chang, Tong Zheng, Bei Li, Peinan Feng, Quan Du, Tong Xiao, JingBo Zhu
| Challenge: | Large language models have significantly advanced Multilingual Machine Translation (MMT) yet scaling to many languages while maintaining robust performance across directions remains challenging. |
| Approach: | They propose a strategy to reduce the number of translations in one direction . they propose auxiliary parallel sentences to promote cross-lingual transfer . |
| Outcome: | The proposed model performs on par with or better than substantially larger baselines. |