Papers by Xia Du
LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing (2024.emnlp-main)
Copied to clipboard
Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing, Cheng Jiayang, Zhaowei Wang, Ying Su, Raj Shah, Ruohao Guo, Jing Gu, Haoran Li, Kangda Wei, Zihao Wang, Lu Cheng, Surangika Ranathunga, Meng Fang, Jie Fu, Fei Liu, Ruihong Huang, Eduardo Blanco, Yixin Cao, Rui Zhang, Philip Yu, Wenpeng Yin
| Challenge: | a comparative analysis of paper (meta-)reviews by large language models (LLMs) aims to identify and distinguish LLMs from human activities . |
| Approach: | They present a comparative analysis to identify and distinguish LLM activities from human activities. |
| Outcome: | The proposed analysis aims to improve recognition of instances when someone implicitly uses LLMs for reviewing activities. |
Prompting ELECTRA: Few-Shot Learning with Discriminative Pre-Trained Models (2022.emnlp-main)
Copied to clipboard
| Challenge: | Pre-trained masked language models perform few-shot learning, but discriminative models like ELECTRA do not fit into the paradigm. |
| Approach: | They propose to use ELECTRA to train pre-trained models to score originality of target options without introducing new parameters. |
| Outcome: | The proposed model outperforms masked language models in a wide range of tasks without adding new parameters. |
Robustness Challenges in Model Distillation and Pruning for Natural Language Understanding (2023.eacl-main)
Copied to clipboard
| Challenge: | Recent studies have focused on compressing pre-trained language models (PLMs) however, few studies have examined the impact of compression on generalizability and robustness of compressed models for out-of-distribution data. |
| Approach: | They propose to use knowledge distillation and pruning to reduce model generalization and generalization on out-of-distribution data. |
| Outcome: | The proposed compression techniques overfit on shortcut samples and generalize poorly on hard ones. |
CSTree-SRI: Introspection-Driven Cognitive Semantic Tree for Multi-Turn Question Answering over Extra-Long Contexts (2025.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have achieved remarkable success in natural language processing (NLP), particularly in single-turn question answering (QA) on short-text. |
| Approach: | They propose a framework that captures logical correlations across chunks of ELC and maintains coherence of multi-turn Questions. |
| Outcome: | The proposed framework is able to capture logical correlations across chunks of ELC and maintain coherence of multi-turn Questions. |
Med-SRAF: A Multi-Agent Framework for Medical Reasoning via Semantic Routing and Agentic Fusion (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing RAG methods suffer from a two-part problem: semantic drift and concatenation fallacy . et al.: rapid development of Large Language Models has led to a paradigm shift in artificial intelligence . |
| Approach: | They propose a multi-agent retrieval augmentation framework guided by medical domain knowledge to address these challenges. |
| Outcome: | The proposed framework outperforms existing general RAG baselines on five widely used medical benchmarks. |
Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU models (2021.naacl-main)
Copied to clipboard
Mengnan Du, Varun Manjunatha, Rajiv Jain, Ruchi Deshpande, Franck Dernoncourt, Jiuxiang Gu, Tong Sun, Xia Hu
| Challenge: | Recent studies indicate that NLU models are prone to rely on shortcut features for prediction, without achieving true language understanding. |
| Approach: | They propose a shortcut mitigation framework to suppress NLU models from making overconfident predictions for samples with large shortcut degree. |
| Outcome: | The proposed framework suppresses the model from making overconfident predictions for samples with large shortcut degree. |
MultiFileTest: A Multi-File-Level LLM Unit Test Generation Benchmark and Impact of Error Fixing Mechanisms (2026.findings-acl)
Copied to clipboard
Yibo Wang, Congying Xia, Wenting Zhao, Jiangshu Du, Chunyu Miao, Zhongfen Deng, Philip S. Yu, Chen Xing
| Challenge: | Existing evaluation benchmarks for LLM unit test generation focus on function-level code rather than on more practical, challenging multi-file codebases. |
| Approach: | They propose a multi-file-level benchmark for unit test generation covering Python, Java, and JavaScript. |
| Outcome: | The proposed benchmarks show that most LLMs exhibit moderate performance on MultiFileTest, highlighting the benchmark’s inherent difficulty. |
Investigating Value-Reasoning Reliability in Small Large Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | sLLMs have been widely deployed in practical applications, but little attention has been paid to their value-reasoning abilities, particularly in terms of reasoning reliability. |
| Approach: | They propose a systematic evaluation framework for assessing the Value-Reasoning Reliability of small Large Language models (sLLMs) . framework includes three core tasks: Repetition Consistency task, Interaction Stability task, and Open-ended Expression Consistencies task. |
| Outcome: | The proposed framework incorporates self-reported confidence scores to evaluate the model’s value reasoning reliability from two perspectives: the model's self awareness of its values, and its value-based decision-making. |
FaithLM: Towards Faithful Explanations for Large Language Models (2026.eacl-long)
Copied to clipboard
Yu-Neng Chuang, Guanchu Wang, Chia-Yuan Chang, Ruixiang Tang, Shaochen Zhong, Fan Yang, Andrew Wen, Mengnan Du, Xuanting Cai, Vladimir Braverman, Xia Hu
| Challenge: | Large language models (LLMs) produce natural language explanations, but they lack faithfulness and do not reflect the evidence the model uses to decide. |
| Approach: | They propose a model-agnostic framework that evaluates and improves the faithfulness of LLM explanations without token masking or task-specific heuristics. |
| Outcome: | The proposed framework improves faithfulness of large language models without masking or heuristics. |
Bypass Back-propagation: Optimization-based Structural Pruning for Large Language Models via Policy Gradient (2025.acl-long)
Copied to clipboard
| Challenge: | Recent pruning methods rely on heuristically hand-crafted metrics, leading to suboptimal performance. |
| Approach: | They propose a method that optimizes pruning masks by minimizing back-propagation . they learn an underlying Bernoulli distribution to sample binary pruning mask samples . |
| Outcome: | The proposed method is able to support global and heterogeneous pruning without back-propagation. |
FOFO: A Benchmark to Evaluate LLMs’ Format-Following Capability (2024.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks fail to assess large language models’ format-following proficiency adequately. |
| Approach: | They propose a benchmark to evaluate large language models' ability to follow complex, domain-specific formats. |
| Outcome: | The proposed framework evaluates large language models' ability to follow complex, domain-specific formats across open-source and closed-source models. |
RethinkMCTS: Refining Erroneous Thoughts in Monte Carlo Tree Search for Code Generation (2025.emnlp-main)
Copied to clipboard
Qingyao Li, Wei Xia, Xinyi Dai, Kounianhua Du, Weiwen Liu, Yasheng Wang, Ruiming Tang, Yong Yu, Weinan Zhang
| Challenge: | Existing tree search methods neglect the underlying reasoning process, resulting in poor search quality. |
| Approach: | They propose a framework that systematically explores and refines the reasoning process for code generation by using a tree search engine and a reflection mechanism. |
| Outcome: | The proposed framework outperforms existing methods in the code generation domain. |
Planning with Diffusion Models for Target-Oriented Dialogue Systems (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods for directing conversations toward specific targets generate dialogue plans in a step-by-step sequential manner and suffer from compounding errors and myopic actions. |
| Approach: | They propose a framework that leverages diffusion models to enable non-sequential dialogue planning. |
| Outcome: | The proposed framework performs non-myopic lookahead exploration and optimizes action strategies over a long horizon through non-sequential dialogue planning. |
Merlin’s Whisper: Enabling Efficient Reasoning in Large Language Models via Black-box Persuasive Prompting (2026.acl-long)
Copied to clipboard
| Challenge: | Large reasoning models (LRMs) have demonstrated proficiency in tackling complex tasks through step-by-step thinking. |
| Approach: | They propose a black-box persuasive prompting framework that generates concise responses without compromising accuracy. |
| Outcome: | The proposed framework reduces token usage while preserving performance. |
Secure Your Model: An Effective Key Prompt Protection Mechanism for Large Language Models (2024.findings-naacl)
Copied to clipboard
| Challenge: | Recent years have seen an unprecedented surge in the development and application of large language models (LLMs) however, the development of LLMs is a complex endeavor, requiring substantial investments in terms of financial and computational resources. |
| Approach: | They propose a mechanism wherein a unique key prompt is embedded within the LLM to protect it from unauthorized access and potential theft. |
| Outcome: | The proposed protection can protect the model without significantly impacting its original function. |
Document-level Causal Relation Extraction with Knowledge-guided Binary Question Answering (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing research on Event-Event Causal Relation Extraction (ECRE) has highlighted the lack of document-level modeling and causal hallucinations. |
| Approach: | They propose a Knowledge-guided binary Question Answering method with event structures for ECRE that utilizes cross-task knowledge in IE. |
| Outcome: | The proposed method achieves state-of-the-art on the MECI and MAVEN-ERE datasets. |
SAPIENT: Mastering Multi-turn Conversational Recommendation with Strategic Planning and Monte Carlo Tree Search (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing methods train RL-based agents with greedy action selection or sampling strategy and suffer from suboptimal conversational planning. |
| Approach: | They propose a Monte Carlo Tree Search-based CRS framework called SAPIENT . it consists of a conversational agent and a communication planner . |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on four benchmark datasets. |
Multi-grained Named Entity Recognition (P19-1)
Copied to clipboard
| Challenge: | Existing approaches treat Named Entity Recognition (NER) as a sequence labeling task. |
| Approach: | They propose a framework for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested. |
| Outcome: | The proposed framework outperforms current state-of-the-art frameworks by 4.4% in terms of the F1 score among nested/non-overlapping NER tasks. |