Papers by Xiaoming Liu
RepoDebug: Repository-Level Multi-Task and Multi-Language Debugging Evaluation of Large Language Models (2025.findings-emnlp)
Copied to clipboard
Jingjing Liu, Zeming Liu, Zihao Cheng, Mengliang He, Xiaoming Shi, Yuhang Guo, Xiangrong Zhu, Yuanfang Guo, Yunhong Wang, Haifeng Wang
| Challenge: | Large Language Models (LLMs) have exhibited significant proficiency in code debugging, especially in automatic program repair. |
| Approach: | They propose a repository-level code debugging dataset with 22 subtypes of errors that supports 8 commonly used programming languages and 3 debug tasks. |
| Outcome: | The proposed dataset supports 8 commonly used programming languages and 3 debugging tasks. |
CoCo: Coherence-Enhanced Machine-Generated Text Detection Under Low Resource With Contrastive Learning (2023.emnlp-main)
Copied to clipboard
| Challenge: | Recent proposed methods fail to consider the linguistic structure of texts and lack the ability to handle the low-resource problem. |
| Approach: | They propose a coherence-based contrastive learning model named CoCo to detect MGTs under low-resource scenario. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two datasets and two self-constructed datasets. |
PDC & DM-SFT: A Road for LLM SQL Bug-Fix Enhancing (2025.coling-industry)
Copied to clipboard
| Challenge: | Existing code large language models focus on generating correct code, but struggle with bug repair. |
| Approach: | They propose a set of methods to enhance LLM’s SQL bug-fixing abilities by combining a data set construction and a supervised bug-fixed learning approach. |
| Outcome: | The proposed methods exceed current best performing model which size is much larger. |
Stumbling Blocks: Stress Testing the Robustness of Machine-Generated Text Detectors Under Attacks (2024.acl-long)
Copied to clipboard
| Challenge: | Existing studies on this topic focus on the robustness of specific detectors or particular attack methods. |
| Approach: | They stress test the detectors’ robustness to malicious attacks under realistic scenarios using LLMs and metric-based detectors. |
| Outcome: | The proposed methods are based on a set of LLM-based models and their performance is compared under different budget levels. |
Matching Distributions between Model and Data: Cross-domain Knowledge Distillation for Unsupervised Domain Adaptation (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods require to learn to adapt the target model by exploiting the source data and sharing the network architecture across domains. |
| Approach: | They propose a framework that allows to transfer the knowledge of source domain to the unlabeled target domain without using source data. |
| Outcome: | The proposed framework matches distributions between a trained source model and a set of target data and achieves superior performance on cross-domain text classification. |
AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-Tuning (2025.emnlp-demos)
Copied to clipboard
Zhong Zhang, Yaxi Lu, Yikun Fu, Yupeng Huo, Shenzhi Yang, Yesai Wu, Han Si, Xin Cong, Haotian Chen, Yankai Lin, Xie Xie, Wei Zhou, Wang Xu, Zhou Su, Zhongwu Zhai, Xiaoming Liu, null Meiyudong, Jianming Xu, Hongyan Tian, Chongyi Wang, Chi Chen, Yuan Yao, Zhiyuan Liu, Maosong Sun
| Challenge: | Large language model agents have enabled GUI-based automation, but their deployment is limited by noisy data, poor generalization, and lack of support for non-English GUIs. |
| Approach: | They propose an 8B-parameter GUI agent built for robust and efficient on-device GUI interaction. |
| Outcome: | The proposed GUI agent achieves promising performance on five public benchmarks and proposed Chinese benchmark CAGUI. |
Towards Database-Free Text-to-SQL Evaluation: A Graph-Based Metric for Functional Correctness (2025.coling-main)
Copied to clipboard
Yi Zhan, Longjie Cui, Han Weng, Guifeng Wang, Yu Tian, Boyi Liu, Yingxiang Yang, Xiaoming Yin, Jiajun Xie, Yang Sun
| Challenge: | Existing metrics for evaluating functional correctness of SQL queries are prone to false positives due to inadequately prepared test databases. |
| Approach: | They propose a graph-based metric that uses a relational operator tree to extract rich semantic information from the logical execution plan of SQL queries and embed it into a diagram. |
| Outcome: | The proposed method eliminates the need for extensive test database preparation and performs graph matching on unseen SQL queries. |
Exploiting Contextual Information via Dynamic Memory Network for Event Detection (D18-1)
Copied to clipboard
| Challenge: | Existing methods for event detection only process context once . a multi-hop mechanism to capture contextual information improves performance . |
| Approach: | They propose to use dynamic memory network to capture contextual information . they propose to model event triggers by identifying word or phrase which most represents it . |
| Outcome: | The proposed model achieves best F1 score compared to the state-of-the-art models. |
Iron Sharpens Iron: Defending Against Attacks in Machine-Generated Text Detection with Adversarial Training (2025.acl-long)
Copied to clipboard
| Challenge: | Existing MGT detectors are vulnerable to simple perturbations and adversarial attacks. |
| Approach: | They propose an adversarial framework for training a robust machine-generated text detector called GREedy Adversary PromoTed DefendER. |
| Outcome: | The proposed framework reduces the Attack Success Rate (ASR) by 0.67% compared with SOTA defense methods. |
KwaiChat: A Large-Scale Video-Driven Multilingual Mixed-Type Dialogue Corpus (2025.findings-naacl)
Copied to clipboard
Xiaoming Shi, Zeming Liu, Yiming Lei, Chenkai Zhang, Haitao Leng, Chuan Wang, Qingjie Liu, Wanxiang Che, Yunhong Wang
| Challenge: | Currently, video-based dialogue systems rely on a single dialogue type, hindering their versatility in practical applications. |
| Approach: | They propose to generate video-driven multilingual mixed-type dialogues using KwaiChat . they propose to create a video-based multilingual mix of 4 dialogue types, 30 domains, 4 languages, 13 topics . |
| Outcome: | The proposed model performs best on KwaiChat but is not perfect in this situation. |
Improving Pacing in Long-Form Story Planning (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing systems for writing long-form stories suffer from unnatural pacing, whether glossing over important events or over-elaborating on insignificant details. |
| Approach: | They propose a system that uses a concreteness evaluator to judge which of two events is more concrete. |
| Outcome: | The proposed system improves pacing when automatically generating story outlines. |
DataSage: Multi-agent Collaboration for Insight Discovery with External Knowledge Retrieval, Multi-role Debating, and Multi-path Reasoning (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing data insight agents fail to deliver satisfactory results due to insufficient utilization of domain knowledge, shallow analytical depth, and error-prone code generation. |
| Approach: | They propose a novel multi-agent framework that incorporates external knowledge retrieval to enrich the analytical context, a multi-role debating mechanism to simulate diverse analytical perspectives and deepen analytical depth, and multi-path reasoning to improve the accuracy of the generated code and insights. |
| Outcome: | Extensive experiments on InsightBench show that DataSage outperforms existing data insight agents across all difficulty levels, improving by 7.5% and 13.9% respectively in insight-level and summary-level metrics. |
Confidence Should Be Calibrated More Than One Turn Deep (2026.acl-long)
Copied to clipboard
| Challenge: | Existing work on confidence estimation and calibration focuses on single-turn settings . existing work on multi-turn calibration ignores the risks and potential of multi-turned conversations . |
| Approach: | They propose a multi-turn calibration task that reframes calibration from a static property into a dynamic challenge central to reliable multi- turn conversations. |
| Outcome: | The proposed model minimizes ECE@T and leverages ConfChat to improve confidence . the proposed model preserves and even enhances model performance in multi-turn interactions. |
Stealthy Jailbreak Attacks on Large Language Models via Benign Data Mirroring (2025.naacl-long)
Copied to clipboard
Honglin Mu, Han He, Yuxin Zhou, Yunlong Feng, Yang Xu, Libo Qin, Xiaoming Shi, Zeming Liu, Xudong Han, Qi Shi, Qingfu Zhu, Wanxiang Che
| Challenge: | Existing black-box jailbreak methods often rely on model feedback . existing methods may be intercepted by content moderators during the search process . |
| Approach: | They propose a method that guides malicious prompt construction by local training a mirror model of the target black-box model through benign data distillation. |
| Outcome: | The proposed method achieves a 92% attack success rate and 80% stealth rate on a subset of AdvBench. |
Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise Reward (2025.findings-emnlp)
Copied to clipboard
Han Weng, Puzhen Wu, Cui Longjie, Yi Zhan, Boyi Liu, Yuanfeng Song, Dun Zeng, Yingxiang Yang, Qianru Zhang, Dong Huang, Xiaoming Yin, Yang Sun, Xing Chen
| Challenge: | Existing methods to enhance performance of large language models (LLMs) on Text-to-SQL tasks rely on execution-based or LLM-based reward models. |
| Approach: | They propose a reward model framework for RL-based Text-to-SQL that employs the GMNScore outcome reward model. |
| Outcome: | The proposed reward model outperforms existing reward models on standard benchmarks including Spider and BIRD. |
Beyond Static Persona Consistency: Dynamic Persona Coherence in LLM Role-Playing (2026.acl-long)
Copied to clipboard
Yirui QI, Xiaoming Zhang, Ruilin Zeng, Mengyao Liu, Ziyi Zhou, Dezhuang Miao, Bingyu Yan, Zhenyu Guan
| Challenge: | Existing LLMs conflate identity consistency with emotional rigidity . Existing models exhibit either robotic repetition or persona drift . |
| Approach: | They propose a framework that decouples Identity-Layer Stability from Adaptive-Layer Appropriateness to achieve persona coherence repair. |
| Outcome: | Experiments on GPT-4o, Claude-3.5-Sonnet, and DeepSeek-V3.2 show consistent improvements (+16–84% gains) |
Direct Large Language Model Alignment Through Self-Rewarding Contrastive Prompt Distillation (2024.acl-long)
Copied to clipboard
| Challenge: | Existing methods to evaluate preference data without human annotations are difficult . et al., 2022b) is effective for aligning large language models with human expectations . |
| Approach: | They propose a method to evaluate the response preference using output probabilities under contrastive prompts. |
| Outcome: | The proposed method could surpass the RLHF method without human-annotated preference data. |
Can Reasoning Path still be Effective as Input? Bridging Post-Reasoning to Chain-of-Thought Compression (2026.acl-long)
Copied to clipboard
Chengzhengxu Li, Xiaoming Liu, Zhaohan Zhang, Shengchao Liu, Guoxin Ma, Yu Lan, Cong Wang, Chao Shen
| Challenge: | Existing work on reducing CoT generation in reasoning impairs the necessary information for deriving the correct answer. |
| Approach: | They propose a reasoning paradigm that takes CoT as a part of context to simplify the reasoning task for Large Language Models (LLMs). |
| Outcome: | The proposed framework reduces the generation length of LLMs, but its effectiveness hinges on the efficiency and reliability of the contextual CoT generation. |
Medical Dialogue System: A Survey of Categories, Methods, Evaluation and Challenges (2024.findings-acl)
Copied to clipboard
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. |
MidMed: Towards Mixed-Type Dialogues for Medical Consultation (2023.acl-long)
Copied to clipboard
| Challenge: | Current medical dialogue systems assume that patients have explicit goals but are often unavailable in real-world situations due to the lack of medical knowledge. |
| Approach: | They propose a human-to-human mixed-type medical consultation dialogue corpus . they build benchmarking baselines on MidMed and propose an instruction-guiding framework . Experimental results show the effectiveness of InsMed . |
| Outcome: | The proposed system can help patients clarify their goals in real-world situations . it covers four departments with 8,309 dialogues and provides benchmarking baselines . |
UniDataBench: Evaluating Data Analytics Agents Across Structured and Unstructured Data (2026.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks do not assess agents’ capabilities across data types . Existing tools only evaluate agents' ability to extract reasonable insights across data formats. |
| Approach: | They propose a multi-source benchmark to evaluate the performance of data analytics agents in handling diverse data sources. |
| Outcome: | The proposed agent performs end-to-end analysis over diverse data sources by automatically discovering cross-source linkages, decomposing goals, and generating robust, self-correcting code to extract actionable insights. |
Does DetectGPT Fully Utilize Perturbation? Bridging Selective Perturbation to Fine-tuned Contrastive Learning Detector would be Better (2024.acl-long)
Copied to clipboard
Shengchao Liu, Xiaoming Liu, Yichen Wang, Zehua Cheng, Chengzhengxu Li, Zhaohan Zhang, Yu Lan, Chao Shen
| Challenge: | Existing methods to detect MGT from human-written texts are inadequate . existing methods are fine-tuned and zero-shot metric-based, but they can be more accurate. |
| Approach: | They propose a novel fine-tuned detector that can detect MGT from human-written texts by contrastive learning on selective perturbation. |
| Outcome: | The proposed method outperforms the state-of-the-art by 1.20% on four public datasets. |
Mis-prompt: Benchmarking Large Language Models for Proactive Error Handling (2025.acl-long)
Copied to clipboard
| Challenge: | Current error-handling works are performed in a passive manner, with explicit error- handling instructions. |
| Approach: | They propose a new benchmark to analyze LLMs' performance on a mis-prompt benchmark and a dataset to promote further research. |
| Outcome: | The proposed benchmark shows that current LLMs show poor performance on proactive error handling, and that SFT improves on error handling instances. |
LIFBench: Evaluating the Instruction Following Performance and Stability of Large Language Models in Long-Context Scenarios (2025.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks rarely focus on instruction-following in long-context scenarios or stability on different inputs. |
| Approach: | They propose a scalable dataset to evaluate LLMs’ instruction-following capabilities and stability across long contexts. |
| Outcome: | The proposed method evaluates LLMs’ instruction-following capabilities and stability across long contexts. |
FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models (2025.naacl-long)
Copied to clipboard
Xin Guo, Haotian Xia, Zhaowei Liu, Hanyang Cao, Zhi Yang, Zhiqiang Liu, Sizhe Wang, Jinyi Niu, Chuqi Wang, Yanhui Wang, Xiaolong Liang, Xiaoming Huang, Bing Zhu, Zhongyu Wei, Yun Chen, Weining Shen, Liwen Zhang
| Challenge: | Large language models have demonstrated outstanding performance in various natural language processing tasks, but their security capabilities in the financial domain have not been explored. |
| Approach: | They propose to use a benchmark to evaluate large language models' financial domain knowledge and practical abilities. |
| Outcome: | The proposed benchmark evaluates large language models' financial domain knowledge and practical abilities. |
Flow2Code: Evaluating Large Language Models for Flowchart-based Code Generation Capability (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing code generation benchmarks neglect flowchart-based code generation . existing benchmarks lack flowcharting-based evaluation, limiting the potential of large language models and minimizing human error. |
| Approach: | They propose to use flowcharts to evaluate existing LLMs' code generation capabilities. |
| Outcome: | The proposed benchmarks show that the supervised fine-tuning technique contributes greatly to the models’ performance. |
MDS: A Fine-Grained Dataset for Multi-Modal Dialogue Summarization (2024.lrec-main)
Copied to clipboard
| Challenge: | Summarizing the dialogue into a short message has drawn much attention due to the explosion of various dialogue scenes. |
| Approach: | They develop a multi-modal dialogue summarization dataset to enhance the variety of data available for this research area. |
| Outcome: | The proposed dataset provides a demanding testbed for multi-modal dialogue summarization. |
StablePT : Towards Stable Prompting for Few-shot Learning via Input Separation (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies on prompt tuning have shown that language models can be effective few-shot learners with prompting. |
| Approach: | They propose to treat the hard prompt and soft prompt as separate inputs to mitigate noise brought by prompt initialization. |
| Outcome: | Experimental results show that the proposed method outperforms state-of-the-art methods by 6.97% in accuracy and reduces the standard deviation by 1.92 on average. |