Papers by Xi Liu

FOLIO: Natural Language Reasoning with First-Order Logic (2024.emnlp-main)

Challenge:	Existing benchmarks and MLLMs focus on single-image input scenarios, leaving performance of ML models when handling multiple images underexplored.
Approach:	They propose a benchmark to evaluate fine-grained abilities of multimodal large language models in multi-image scenarios.
Outcome:	The proposed benchmark categorizes the multi-image abilities into three scenarios: MII, MKS and MIC.

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Simeng Han, Hailey Schoelkopf, Yilun Zhao, Zhenting Qi, Martin Riddell, Wenfei Zhou, James Coady, David Peng, Yujie Qiao, Luke Benson, Lucy Sun, Alexander Wardle-Solano, Hannah Szabó, Ekaterina Zubova, Matthew Burtell, Jonathan Fan, Yixin Liu, Brian Wong, Malcolm Sailor, Ansong Ni, Linyong Nan, Jungo Kasai, Tao Yu, Rui Zhang, Alexander Fabbri, Wojciech Kryscinski, Semih Yavuz, Ye Liu, Xi Lin, Shafiq Joty, Yingbo Zhou, Caiming Xiong, Rex Ying, Arman Cohan, Dragomir Radev

Reasoning

Challenge:	Existing benchmarks for logical reasoning in large language models lack language naturalness or limited complexity.
Approach:	They propose to use first-order logic annotations to evaluate logical reasoning capabilities of large language models.
Outcome:	The proposed dataset evaluates the FOL reasoning ability of supervised fine-tuning on medium-sized language models.

AS-ES Learning: Towards efficient CoT learning in small models (2024.findings-acl)

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Nuwa Xi, Yuhan Chen, Sendong Zhao, Haochun Wang, GongZhang GongZhang, Bing Qin, Ting Liu

AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments (2026.acl-long)

Challenge:	Existing methods to induce Chain-of-Thought (CoT) in LLMs are limited and do not consider the importance of efficiently utilizing existing CoT data.
Approach:	They propose a new training paradigm which exploits the inherent information in CoT for iterative generation.
Outcome:	The proposed training paradigm surpasses direct seq2seq training on CoT-extensive tasks without data augmentation or altering the model itself.

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Zhiheng Xi, Dingwen Yang, Jiaqi Liu, Jixuan Huang, Honglin Guo, Baodai Huang, Tinggang Chen, Qi Zhang, Zhonghang Lu, Chenyu Liu, Jiajun Sun, Jiazheng Zhang, Dingwei Zhu, Xin Guo, Junzhe Wang, Zhihao Zhang, Yuming Yang, Junjie Ye, Minghe Gao, Dongrui Liu, Jiaming Ji, Guohao Li, Tao Gui, Qi Zhang, Xuanjing Huang

LoRAMoE: Alleviating World Knowledge Forgetting in Large Language Models via MoE-Style Plugin (2024.acl-long)

Challenge:	Existing benchmarks evaluate agents in simplified, idealized settings, relying on pre-packaged tool interfaces, overlooking critical steps, and assume inputs are clean and fully specified.
Approach:	They propose a framework that evaluates language agents in simplified, idealized settings . they show that even SOTA systems like Gemini and GPT-5 struggle on AgentGym2 .
Outcome:	Experiments on 15 proprietary and open-source models show that even SOTA systems like Gemini and GPT-5 struggle on AgentGym2 .

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Shihan Dou, Enyu Zhou, Yan Liu, Songyang Gao, Wei Shen, Limao Xiong, Yuhao Zhou, Xiao Wang, Zhiheng Xi, Xiaoran Fan, Shiliang Pu, Jiang Zhu, Rui Zheng, Tao Gui, Qi Zhang, Xuanjing Huang

Learning from Contrasts: Synthesizing Reasoning Paths from Diverse Search Trajectories (2026.acl-long)

Challenge:	Experimental results show that, as the instruction data increases, LoRAMoE can significantly improve the ability to process downstream tasks, while maintaining the world knowledge stored in the LLM.
Approach:	They propose a framework that introduces several low-rank adapters and integrates them by using a router network to freeze the backbone model and force a portion of LoRAs to focus on leveraging world knowledge to solve downstream tasks.
Outcome:	The proposed framework freezes the backbone model and forces a portion of LoRAs to focus on leveraging world knowledge to solve downstream tasks, to alleviate world knowledge forgetting.

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Peiyang Liu, Zhirui Chen, Xi Wang, Di Liang, Youru Li, Zhi Cai, Wei Ye

Reasoning

Challenge:	MCTS methods retain only the single highest-reward trajectory, discarding comparative signals present in the many explored paths.
Approach:	They propose a framework that transforms supervision extraction into a synthesis procedure.
Outcome:	The proposed framework matches or exceeds baselines on 60K CRPS-synthesized examples on out-of-domain benchmarks.

The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit (2025.acl-long)

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Huixue Zhou, Hengrui Gu, Zaifu Zhan, Xi Liu, Kaixiong Zhou, Yongkang Xiao, Mingfu Liang, Srinivas Prasad Govindan, Piyush Chawla, Jiyan Yang, Xiangfei Meng, Huayu Li, Buyun Zhang, Liang Luo, Wen-Yen Chen, Yiping Han, Bo Long, Rui Zhang, Tianlong Chen

Green & Sustainable NLP

Challenge:	Existing frameworks for Large Language Models (LLMs) for Click-Through Rate prediction require a careful balance between computational efficiency and predictive accuracy.
Approach:	They propose a framework that integrates Retrieval-Augmented Generation with a novel multi-head early exit architecture to address both challenges.
Outcome:	The proposed framework reduces retrieval time while maintaining high model performance.

Improving Conversational Recommendation Systems via Bias Analysis and Language-Model-Enhanced Data Augmentation (2023.findings-emnlp)

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Xi Wang, Hossein Rahmani, Jiqun Liu, Emine Yilmaz

Progressive Re-ranking for Multimodal Retrieval-Augmented Generation via Curriculum Learning (2026.findings-acl)

Challenge:	Conversational Recommendation System (CRS) is a rapidly growing research area, along with advancements in language modelling techniques.
Approach:	They propose to use a benchmark dataset to develop CRS models and address biases arising from feedback loop inherent in multi-turn interactions to enhance model performance while mitigating biase.
Outcome:	The proposed strategies improve on ReDial and TG-ReDial benchmark datasets and offer additional insights on addressing multiple newly formulated biases.

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Zhu Min, Yanchao Hao, Jian Liu, Shizhu He, Xi Chen

C-World: A Computer Use Agent Environment Creator (2026.acl-long)

Challenge:	Existing approaches to improve retrieval performance of large language models are limited by static knowledge.
Approach:	They propose a multimodal re-ranking framework that combines curriculum learning with fine-grained reranking and multimodal section reassessment to improve CLIP-based visual coarse-grain retrieval.
Outcome:	The proposed framework achieves state-of-the-art answer accuracy and competitive retrieval performance on InfoSeek and Enc-VQA.

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Ziqiao Xi, Shuang Liang, Qi Liu, Jiaqing Zhang, Letian Peng, Fang Nan, Meshal Nayim, Tianhui Zhang, Rishika Mundada, Lianhui Qin, Biwei Huang, Kun Zhou

Challenge:	C-World enables users to build agent environments on demand.
Approach:	They propose a system that enables users to build agent environments on demand.
Outcome:	The proposed system outperforms baselines on 119k samples and achieves Spearman = 0.883 ranking correlation with real execution.

Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models (2026.findings-acl)

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Hengyuan Zhang, Zhihao Zhang, Ercong Nie, Mingyang Wang, Zunhai Su, Yiwei Wang, Qianli Wang, Shuzhou Yuan, Xufeng Duan, Qibo Xue, Zeping Yu, Chenming Shang, Xiao Liang, Jing Xiong, Hui Shen, Chaofan Tao, Zhengwu Liu, Senjie Jin, Zhiheng Xi, Dongdong Zhang, Sophia Ananiadou, Tao Gui, Ruobing Xie, Hayden Kwok-Hay So, Hinrich Schuetze, Xuanjing Huang, Qi Zhang, Ngai Wong

Explainability & Interpretability in NLP

Challenge:	Existing literature on mechanistic interpretation (MI) treats it as an observational science, leaving practical applications underexplored.
Approach:	They propose a survey structured around the pipeline to identify and improve MI models.
Outcome:	The proposed framework enables tangible improvements in Alignment, Capability, and Efficiency.

UniCoRN: Unified Cognitive Signal ReconstructioN bridging cognitive signals and human language (2023.acl-long)

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Nuwa Xi, Sendong Zhao, Haochun Wang, Chi Liu, Bing Qin, Ting Liu

Challenge:	Existing studies focus on decoding word-level fMRI volumes from a restricted vocabulary.
Approach:	They propose an open-vocabulary task to bridge fMRI time series and human language . they use a pre-trained language model to construct a robust encoder for cognitive signals .
Outcome:	The proposed task bridges fMRI time series and human language with a baseline model.

VRoPE: Rotary Position Embedding for Video Large Language Models (2025.emnlp-main)

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Zikang Liu, Longteng Guo, Yepeng Tang, Tongtian Yue, Junxian Cai, Kai Ma, Qingbin Liu, Xi Chen, Jing Liu

Analyzing Chain-of-thought Prompting in Black-Box Large Language Models via Estimated V-information (2024.lrec-main)

Challenge:	Existing versions of Large Language Models (LLMs) lack a positional encoding strategy for video.
Approach:	They propose a new positional encoding method tailored for Video-LLMs that mitigates positional biases and ensures a more uniform distribution of spatial focus.
Outcome:	The proposed method outperforms existing versions of RoPE in video understanding and reasoning tasks.

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Zecheng Wang, Chunshan Li, Zhao Yang, Qingbin Liu, Yanchao Hao, Xi Chen, Dianhui Chu, Dianbo Sui

E-ConvRec: A Large-Scale Conversational Recommendation Dataset for E-Commerce Customer Service (2022.lrec-1)

Challenge:	Chain-of-Thought (CoT) prompting and large language models (LLMs) have shown great potential in improving performance on challenging reasoning tasks.
Approach:	They propose a new metric which extends the concept of pointwise V-information to black-box models and quantifies label-relevant new information introduced by CoT prompting.
Outcome:	The proposed metric extends the concept of pointwise V-information to black-box models, quantifying label-relevant new information introduced by CoT prompting beyond pre-existing label information.

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Meihuizi Jia, Ruixue Liu, Peiying Wang, Yang Song, Zexi Xi, Haobin Li, Xin Shen, Meng Chen, Jinhui Pang, Xiaodong He

Natural Language Interfaces

Challenge:	Recent research has focused on developing conversational recommendation system (CRS), which provides valuable recommendations to users through conversations.
Approach:	They construct an authentic Chinese dialogue dataset consisting of over 25k dialogues and 770k utterances, which contains user profile, product knowledge base, and multiple sequential real conversations between users and recommenders.
Outcome:	The proposed dataset contains user profile, product knowledge base, and multiple sequential real conversations between users and recommenders.

Novel Relation Detection: Discovering Unknown Relation Types via Multi-Strategy Self-Supervised Learning (2023.findings-emnlp)

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Qingbin Liu, Yin Kung, Yanchao Hao, Dianbo Sui, Siyuan Cheng, Xi Chen, Ningyu Zhang, Jiaoyan Chen

Low-Resource NLP

Challenge:	Existing approaches to relation extraction can only recognize predefined relation types . new or out-of-scope relation types may continually emerge after the model is deployed .
Approach:	They propose a novel relation detection task that uses self-supervised learning to handle shallow semantic similarity problem.
Outcome:	The proposed method outperforms state-of-the-art methods on two datasets.

AgentV-RL: Scaling Reward Modeling with Agentic Verifier (2026.findings-acl)

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Jiazheng Zhang, Ziche Fu, Zhiheng Xi, Wenqing Jing, Mingxu Chai, Wei He, Guoqiang Zhang, Chenghao Fan, Chenxin An, Wenxiang Chen, Zhicheng Liu, Haojie Pan, Dingwei Zhu, Tao Gui, Qi Zhang, Xuanjing Huang

Reasoning

Challenge:	Existing approaches to improve LLM reasoning are limited in complex domains and lack external grounding makes verifiers unreliable on computation-intensive tasks.
Approach:	They propose a framework that transforms reward modeling into a multi-turn, tool-augmented deliberative process.
Outcome:	The proposed framework surpasses state-of-the-art ORMs by 25.2% under parallel and sequential TTS.

ChildMandarin: A Comprehensive Mandarin Speech Dataset for Young Children Aged 3-5 (2025.acl-long)

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Jiaming Zhou, Shiyao Wang, Shiwan Zhao, Jiabei He, Haoqin Sun, Hui Wang, Cheng Liu, Aobo Kong, Yujie Guo, Xi Yang, Yequan Wang, Yonghua Lin, Yong Qin

Time-for-Accuracy: Formalizing Chain-of-Thought as an Expansion of Logical Depth (2026.findings-acl)

Challenge:	Automatic speech recognition systems have advanced significantly with models like Whisper, Conformer, and self-supervised frameworks such as Wav2vec 2.0.
Approach:	They propose to use Mandarin speech datasets to analyze pronunciation and tone of children aged 3 to 5 and evaluate their models on speaker verification (SV) They find that the datasets are more robust than those used by adult speech recognition systems and are open-source and available for all academic purposes.
Outcome:	The proposed dataset includes 41.25 hours of speech with carefully crafted manual transcriptions, collected from 397 speakers across various provinces in China, with balanced gender representation.

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Yue Wang, Zhi Zhang, Wang Xi, Chengjie Sun, Lili Shan, Bingquan Liu

Challenge:	Chain-of-thought (CoT) prompting can improve multi-step reasoning, but it is unclear what kind of additional sequential computation longer traces actually enable.
Approach:	They propose a deletion-based measure of step necessity under a specified inference interface to operationalize realized depth beyond raw length.
Outcome:	The proposed method combines effective logical depth with Bennett's logical depth to show that it is more efficient than a linear model.

Self-Polish: Enhance Reasoning in Large Language Models via Problem Refinement (2023.findings-emnlp)

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Zhiheng Xi, Senjie Jin, Yuhao Zhou, Rui Zheng, Songyang Gao, Jia Liu, Tao Gui, Qi Zhang, Xuanjing Huang

Multi-Programming Language Sandbox for LLMs (2025.acl-demo)

Challenge:	Existing prompting methods have been used to enhance multistep reasoning capabilities of large language models, but they have overlooked the potential of formulating higher-quality problems.
Approach:	They propose a method that starts from the problem side and refines problems to be more comprehensible and solvable for models.
Outcome:	The proposed method achieves notable and consistent effectiveness on five reasoning benchmarks across different models.

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Shihan Dou, Jiazheng Zhang, Jianxiang Zang, Yunbo Tao, Weikang Zhou, Haoxiang Jia, Shichun Liu, Yuming Yang, Shenxi Wu, Zhiheng Xi, Muling Wu, Rui Zheng, Changze Lv, Limao Xiong, Shaoqing Zhang, Lin Zhang, Wenyu Zhan, Rongxiang Weng, Jingang Wang, Xunliang Cai, Yueming Wu, Ming Wen, Yixin Cao, Tao Gui, Xipeng Qiu, Qi Zhang, Xuanjing Huang

An Industry Evaluation of Embedding-based Entity Alignment (2020.coling-industry)

Challenge:	MPLSandbox is an out-of-the-box multi-programming language sandbox designed to provide unified and comprehensive feedback from compiler and analysis tools for Large Language Models (LLMs).
Approach:	They propose a multi-programming language sandbox that provides unified feedback from compilers and analysis tools for Large Language Models.
Outcome:	The proposed multi-language sandbox can provide comprehensive feedback from compilers and analysis tools for large language models (LLMs).

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Ziheng Zhang, Hualuo Liu, Jiaoyan Chen, Xi Chen, Bo Liu, YueJia Xiang, Yefeng Zheng

Editing the Moving World: Model Editing for Video LLMs (2026.acl-long)

Challenge:	Knowledge graphs (KGs) are increasingly important in various applications such as question answering and search engines.
Approach:	They propose to use a supervised learning environment with unbiased seed mappings for training and validation to evaluate alignment methods in an industrial context.
Outcome:	The proposed methods are evaluated in an industrial context and are compared with DBpedia and Wikidata benchmarks.

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Qian Zhang, Xinye Li, Xiaokai Wu, Junhao Xu, Zhanyue Qin, Qingbin Liu, Junxian Cai, Xi Chen, Bolin Zhang, Zhiying Tu, Dianhui Chu, Xiaoyan Yu, Dianbo Sui

Imperfect also Deserves Reward: Multi-Level and Sequential Reward Modeling for Better Dialog Management (2021.naacl-main)

Challenge:	Existing models for knowledge editing focus on knowledge-level or static visual domains, overlooking dynamic semantics.
Approach:	They propose a benchmark for modeling large language models using six representative models . they analyze the strengths and limitations of existing models and identify new directions .
Outcome:	The proposed benchmark extends existing models from static modalities to dynamic video scenarios.

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Zhengxu Hou, Bang Liu, Ruihui Zhao, Zijing Ou, Yafei Liu, Xi Chen, Yefeng Zheng

DESED: Dialogue-based Explanation for Sentence-level Event Detection (2022.coling-1)

Challenge:	Existing research on taskoriented dialog systems mainly includes pipeline and end-to-end methods due to its non-differentiable nature.
Approach:	They propose a multi-level reward modeling approach that factorizes a reward into a three-level hierarchy: domain, act, and slot.
Outcome:	The proposed approach significantly improves performance and speed of training in a wide range of dialog systems.

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Yinyi Wei, Shuaipeng Liu, Jianwei Lv, Xiangyu Xi, Hailei Yan, Wei Ye, Tong Mo, Fan Yang, Guanglu Wan

Improve Interpretability of Neural Networks via Sparse Contrastive Coding (2022.findings-emnlp)

Challenge:	Existing methods for sentence-level event detection depend on manual annotations or domain expertise to design sophisticated templates and rules.
Approach:	They propose a dialogue-based explanation paradigm to enhance sentence semantics for event detection.
Outcome:	The proposed method can be applied to two event detection datasets.

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Junhong Liu, Yijie Lin, Liang Jiang, Jia Liu, Zujie Wen, Xi Peng

Explainability & Interpretability in NLP

Challenge:	XAI has achieved remarkable advances, but few efforts have been devoted to solving the problem.
Approach:	They propose a model-agnostic explanation method termed Sparse Contrastive Coding . they use model-based explanations to explain the black-box in a more model-oriented way .
Outcome:	The proposed method outperforms five state-of-the-art methods in interpretability and classification metrics.

LLMEval-Fair: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models (2026.acl-long)

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Ming Zhang, Yujiong Shen, Jingyi Deng, Yuhui Wang, Huayu Sha, Kexin Tan, Qiyuan Peng, Yue Zhang, Junzhe Wang, Shichun Liu, Yueyuan Huang, Jingqi Tong, Changhao Jiang, Yilong Wu, Zhihao Zhang, Mingqi Wu, Mingxu Chai, Zhiheng Xi, Shihan Dou, Tao Gui, Qi Zhang, Xuanjing Huang

Improving Embedding-based Large-scale Retrieval via Label Enhancement (2021.findings-emnlp)

Challenge:	Existing evaluation of Large Language Models on static benchmarks is vulnerable to data contamination and leaderboard overfitting.
Approach:	LLMEval-Fair framework provides a framework for dynamic evaluation of Large Language Models . evaluators use a proprietary bank of 220k graduate-level questions to analyze model data .
Outcome:	LLMEval-Fair provides robust and credible evaluation framework for Large Language Models . it provides a strong empirical validation for the dynamic evaluation paradigm .

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Peiyang Liu, Xi Wang, Sen Wang, Wei Ye, Xiangyu Xi, Shikun Zhang

Representation Learning

Challenge:	Existing methods for large-scale retrieval are trained with 0-1 hard labels that indicate whether a query is relevant to a document, ignoring rich information of the relevance degree.
Approach:	They propose to introduce label enhancement for the first time to characterize query-document relevance degree by embedding label distribution into contextual embeddables.
Outcome:	The proposed method significantly outperforms existing retrieval models and its counterparts equipped with two alternative methods on English and Chinese large-scale retrieval tasks.

Enhancing Text-to-SQL Capabilities of Large Language Models through Tailored Promptings (2024.lrec-main)

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Zhao Tan, Xiping Liu, Qing Shu, Xi Li, Changxuan Wan, Dexi Liu, Qizhi Wan, Guoqiong Liao

Programming Languages in NLP

Challenge:	Large language models with prompting have achieved encouraging results on many natural language processing tasks due to the absence of task-tailored promptings.
Approach:	They propose three promptings specifically designed for Text-to-SQL: SL-prompt, CC-promped, and SL+CC prompt.
Outcome:	The proposed promptings achieve execution accuracy of 86.2% and test-suite accuracy of 76% . the granularity of schema linking and the order of clause generation have great impact on performance, which are considered little in previous research.

Can We Edit Multimodal Large Language Models? (2023.emnlp-main)

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Siyuan Cheng, Bozhong Tian, Qingbin Liu, Xi Chen, Yongheng Wang, Huajun Chen, Ningyu Zhang

Pruning via Merging: Compressing LLMs via Manifold Alignment Based Layer Merging (2024.emnlp-main)

Challenge:	Existing methods to edit multimodal models have been used to incrementally infuse a language model with a new set of facts.
Approach:	They construct a benchmark for editing multimodal Large Language Models and establish metrics for evaluation.
Outcome:	The proposed benchmarks show that editing multimodal models is not as difficult as editing single-modal models.

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Deyuan Liu, Zhanyue Qin, Hairu Wang, Zhao Yang, Zecheng Wang, Fangying Rong, Qingbin Liu, Yanchao Hao, Bo Li, Xi Chen, Cunhang Fan, Zhao Lv, Dianhui Chu, Zhiying Tu, Dianbo Sui

FlagEvalMM: A Flexible Framework for Comprehensive Multimodal Model Evaluation (2025.acl-demo)

Challenge:	Existing methods for parameter pruning fail to utilize the knowledge from pruned parameters.
Approach:	They propose a method that uses manifold learning and the Information Bottleneck measure to merge similar layers to preserve model performance.
Outcome:	The proposed method outperforms pruning methods on multiple datasets and LLMs with quantization and achieves substantial compression ratios.

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Zheqi He, Yesheng Liu, Jing-Shu Zheng, Xuejing Li, Jin-Ge Yao, Bowen Qin, Richeng Xuan, Xi Yang

Summarizing Dialogues with Negative Cues (2022.coling-1)

Challenge:	FlagEvalMM is an evaluation framework designed to assess multimodal models . it is designed to be used for vision-language understanding and generation tasks .
Approach:	They propose an evaluation framework that decouples model inference from evaluation through an independent evaluation service.
Outcome:	The evaluation framework offers accurate and efficient insights into model strengths and limitations.

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Junpeng Liu, Yanyan Zou, Yuxuan Xi, Shengjie Li, Mian Ma, Zhuoye Ding

All That Glisters Is Not Gold: A Benchmark for Reference-Free Counterfactual Financial Misinformation Detection (2026.acl-long)

Challenge:	Abstractive dialogue summarization aims to convert long dialogue content into its short form where the salient information is preserved while the redundant pieces are ignored.
Approach:	They propose to have the model perceive the redundant parts of an input dialogue history during the training phase.
Outcome:	The proposed method significantly outperforms baselines on the semantic matching and factual consistent based metrics.

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Yuechen Jiang, Zhiwei Liu, Yupeng Cao, Yueru He, Ziyang Xu, Chen Xu, Zhiyang Deng, Prayag Tiwari, Xi Chen, Alejandro Lopez-Lira, Jimin Huang, Junichi Tsujii, Sophia Ananiadou

Characterizing the Impacts of Instances on Robustness (2023.findings-acl)

Challenge:	RFC-Bench evaluates large language models on financial misinformation under realistic news . current models struggle to maintain coherent belief states without external grounding, study finds .
Approach:	They propose a benchmark for evaluating large language models on financial misinformation under realistic news.
Outcome:	The proposed model performs better when context is available, while reference-free settings expose significant weaknesses.

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Rui Zheng, Zhiheng Xi, Qin Liu, Wenbin Lai, Tao Gui, Qi Zhang, Xuanjing Huang, Jin Ma, Ying Shan, Weifeng Ge

Robustness in NLP

Challenge:	Existing defense approaches focus on developing new model structures or training algorithms, but they do little to tap the potential of training instances.
Approach:	They propose a method that can distinguish between robust and non-robust instances according to the model’s sensitivity to perturbations on individual instances during training.
Outcome:	The proposed method can distinguish between robust and non-robust instances according to the model’s sensitivity to perturbations on individual instances during training.

ScEdit: Script-based Assessment of Knowledge Editing (2025.findings-acl)

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Xinye Li, Zunwen Zheng, Qian Zhang, Dekai Zhuang, Jiabao Kang, Liyan Xu, Qingbin Liu, Xi Chen, Zhiying Tu, Dianhui Chu, Dianbo Sui

Challenge:	Knowledge Editing (KE) has gained increasing attention, yet current evaluation frameworks do not integrate KE into real-world application scenarios.
Approach:	They propose a script-based benchmark which encompasses both counterfactual and temporal edits and integrates token-level and text-level evaluation methods.
Outcome:	The proposed method combines token-level and text-level evaluation methods with a new fact-based evaluation framework.

TwinVoice: A Multi-dimensional Benchmark Towards Digital Twins via LLM Persona Simulation (2026.findings-acl)

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Bangde Du, Minghao Guo, Songming He, Ziyi Ye, Xi Zhu, Weihang Su, Shuqi Zhu, Yujia Zhou, Yongfeng Zhang, Qingyao Ai, Yiqun Liu

Natural Language Interfaces

Challenge:	Existing studies show that advanced LLMs produce text indistinguishable from human writing.
Approach:	They propose a benchmark to assess persona simulation across diverse contexts by decomposing the evaluation into six fundamental capabilities including opinion consistency, memory recall, logical reasoning, persona tone, and syntactic style.
Outcome:	The proposed model achieves moderate accuracy but falls short of the basic capabilities needed to simulate personas in real-world contexts.

Not All Directions Matter: Towards Structured and Task-Aware Low-Rank Model Adaptation (2026.acl-long)

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Xi Xiao, Chenrui Ma, Yunbei Zhang, Chen Liu, Zhuxuanzi Wang, Yanshu Li, Lin Zhao, Guosheng Hu, Tianyang Wang, Hao Xu

Towards A Unified View of Sparse Feed-Forward Network in Pretraining Large Language Model (2023.emnlp-main)

Challenge:	Low-Rank Adaptation (LoRA) is a key parameter-efficient fine-tuning method . however, its effectiveness is hampered by semantic drift and structural incoherence .
Approach:	They propose a low-rank Adaptation framework that tackles semantic drift and structural incoherence by pruning task-irrelevant directions.
Outcome:	Experiments on large language models, vision models, and vision models show that the proposed framework outperforms LoRA and advanced dynamic rank allocation and sparsity-based methods.

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Zeyu Liu, Tim Dettmers, Xi Lin, Veselin Stoyanov, Xian Li

Can MLLMs Understand the Deep Implication Behind Chinese Images? (2025.acl-long)

Challenge:	Large and sparse feed-forward layers (S-FFN) have proven effective in scaling up the model size for pretraining large language models.
Approach:	They compare S-FFN architectures for language modeling and compare their performance and efficiency . they found a simpler selection method that selects blocks through their mean aggregated hidden states .
Outcome:	The proposed model size and selection method achieve lower perplexity in language model pretraining compared to existing MoE architectures.

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Chenhao Zhang, Xi Feng, Yuelin Bai, Xeron Du, Jinchang Hou, Kaixin Deng, Guangzeng Han, Qinrui Li, Bingli Wang, Jiaheng Liu, Xingwei Qu, Yifei Zhang, Qixuan Zhao, Yiming Liang, Ziqiang Liu, Feiteng Fang, Min Yang, Wenhao Huang, Chenghua Lin, Ge Zhang, Shiwen Ni

SMASH: Improving SMAll Language Models’ Few-SHot Ability with Prompt-Based Distillation (2022.findings-emnlp)

Challenge:	MLLMs perform poorly on traditional culture images, indicating limitations in understanding high-level semantics and lacking a deep knowledge base of Chinese traditional culture.
Approach:	They propose to use Chinese images to assess MLLMs' higher-order perception and understanding of Chinese visual content.
Outcome:	The proposed model incorporates images that represent Chinese traditional culture, such as famous Chinese traditional paintings, to ensure the authenticity of the Chinese context.

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Yueqian Wang, Chang Liu, Kai Chen, Xi Wang, Dongyan Zhao

Inconsistency Matters: A Knowledge-guided Dual-inconsistency Network for Multi-modal Rumor Detection (2021.findings-emnlp)

Challenge:	Large-scale language models with prompts have shown remarkable performance on few-shot learning.
Approach:	They propose an approach to improve SMAll language models’ few-SHot ability by training on intermediate tasks before prompt-based fine-tuning on downstream tasks.
Outcome:	The proposed model improves on sentence-pair and sentiment classification tasks by training on intermediate tasks before fine-tuning on downstream tasks.

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Mengzhu Sun, Xi Zhang, Jianqiang Ma, Yazheng Liu

MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)

Challenge:	Existing rumor detection models focus on textual data to extract distinctive features, but they fail to capture the inconsistency information among the content and background knowledge.
Approach:	They propose to capture inconsistency semantics and content-knowledge level in a unified framework to detect rumors with multimedia content.
Outcome:	Extensive experiments on two public real-world datasets show that the proposed network outperforms the state-of-the-art models.

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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie

Toward Optimal LLM Alignments Using Two-Player Games (2025.findings-emnlp)

Challenge:	Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models.
Approach:	They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts.
Outcome:	The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings .

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Rui Zheng, Hongyi Guo, Zhihan Liu, Xiaoying Zhang, Yuanshun Yao, Xiaojun Xu, Zhaoran Wang, Zhiheng Xi, Tao Gui, Qi Zhang, Xuanjing Huang, Yang Liu, Hang Li

Class Lifelong Learning for Intent Detection via Structure Consolidation Networks (2023.findings-acl)

Challenge:	Alignment of large language models (LLM) is a process that ensures the model’s responses to user prompts align with human intentions and social values.
Approach:	They propose an alignment method based on a two-agent game consisting of an adversarial agent and a defensive agent.
Outcome:	The proposed method improves on a two-agent game with an adversarial agent and a defensive agent.

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Qingbin Liu, Yanchao Hao, Xiaolong Liu, Bo Li, Dianbo Sui, Shizhu He, Kang Liu, Jun Zhao, Xi Chen, Ningyu Zhang, Jiaoyan Chen

Sentiment Analysis

Challenge:	Existing intent detection models can only handle predefined intent classes in the offline environment.
Approach:	They propose a method that continually learns new intent classes from new data . structure-based retrospection and contrastive knowledge distillation are used to solve these problems .
Outcome:	The proposed method outperforms existing models on three benchmarks.

Exploring Deductive and Inductive Reasoning Capabilities of Large Language Models in Procedural Planning (2025.findings-emnlp)

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Jiabao Kang, Xinye Li, Liyan Xu, Qingbin Liu, Xi Chen, Zhiying Tu, Dianhui Chu, Dianbo Sui

EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models (2024.acl-demos)

Challenge:	Deductive and inductive reasoning are fundamental components of human cognition . authors present a benchmark to assess their performance in procedural planning .
Approach:	They propose a benchmark to assess the deductive and inductive reasoning abilities of LLMs . they propose IMSE to enable LLM to generate multiple similar procedural plans .
Outcome:	The proposed method improves inductive reasoning abilities of LLMs, the authors show . they show that LLM models show excellent deductive reasoning capabilities but suboptimal inductive performance.

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Peng Wang, Ningyu Zhang, Bozhong Tian, Zekun Xi, Yunzhi Yao, Ziwen Xu, Mengru Wang, Shengyu Mao, Xiaohan Wang, Siyuan Cheng, Kangwei Liu, Yuansheng Ni, Guozhou Zheng, Huajun Chen

Conan-Embedding-v2: Training an LLM from Scratch for Text Embeddings (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) suffer from knowledge cutoff or fallacy issues, which means they are unaware of unseen events or generate text with incorrect facts owing to outdated/noisy data.
Approach:	They propose an easy-to-use knowledge editing framework for Large Language Models that allows users to easily edit updated knowledge and adjust undesired behavior while minimizing the impact on unrelated inputs.
Outcome:	The proposed framework surpasses traditional fine-tuning in terms of reliability and generalization.

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Shiyu Li, Yang Tang, Ruijie Liu, Shi-Zhe Chen, Xi Chen

UER: An Open-Source Toolkit for Pre-training Models (D19-3)

Challenge:	Existing studies use LoRA to fine-tune existing LLMs, but this is limited by the data and training gap between them and embedding models.
Approach:	They propose a new 1.4B-parameter LLM trained from scratch and fine-tuned as a text embedder that integrates embeddings across different languages.
Outcome:	The proposed model improves performance on the Massive Text Embedding Benchmark (MTEB) and Chinese MTEB (May 19, 2025).

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Zhe Zhao, Hui Chen, Jinbin Zhang, Xin Zhao, Tao Liu, Wei Lu, Xi Chen, Haotang Deng, Qi Ju, Xiaoyong Du

LLMs May Perform MCQA by Selecting the Least Incorrect Option (2025.coling-main)

Challenge:	Existing work on pre-training models have shown that it is important to use a framework to deploy various pre- training models efficiently.
Approach:	They propose an assemble-on-demand pre-training toolkit that assembles pre-trained models on demand and encapsulates them with rich modules.
Outcome:	The proposed framework can reproduce state-of-the-art models or develop models that remain unexplored.

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Haochun Wang, Sendong Zhao, Zewen Qiang, Nuwa Xi, Bing Qin, Ting Liu

Challenge:	Multiple Choice Question Answering (MCQA) is a fundamental format for various tasks in NLP, such as commonsense reasoning.
Approach:	They propose a method to increase the number of correct options in a dataset.
Outcome:	The proposed method improves the performance of multiple choice question answering (MCQA) and improves its accuracy.

Enhancing Efficiency and Exploration in Reinforcement Learning for LLMs (2025.emnlp-main)

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Mengqi Liao, Xiangyu Xi, Chen Ruinian, Jia Leng, Yangen Hu, Ke Zeng, Shuai Liu, Huaiyu Wan

Green & Sustainable NLP

Challenge:	Existing approaches allocate an equal number of rollouts to all questions during the RL process, which is inefficient.
Approach:	They propose a mechanism for dynamically allocating rollout budgets based on the difficulty of the problems, enabling more efficient RL training.
Outcome:	The proposed model improves response precision while preserving exploratory ability to uncover potential correct pathways.

ReasonRec: A Reasoning-Augmented Multimodal Agent for Unified Recommendation (2026.findings-acl)

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Yihua Zhang, Mingfu Liang, Jiyan Yang, Rong Jin, Wen-Yen Chen, Yiping Han, Huayu Li, Buyun Zhang, Liang Luo, Luke Simon, Sijia Liu, Tianlong Chen, Xi Liu

MM-Doc-R1: Training Agents for Long Document Visual Question Answering through Multi-turn Reinforcement Learning (2026.findings-acl)

Challenge:	Recent advances in multimodal recommenders lack explicit reasoning and self-awareness of uncertainty.
Approach:	They propose a reasoning-augmented multimodal agent structured around a three-stage explicit reasoning pipeline.
Outcome:	The proposed agent improves ranking metrics and performance on four standard recommendation tasks across five real-world datasets.

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Jiahang Lin, Kai Hu, Binghai Wang, Yuhao Zhou, Zhiheng Xi, Honglin Guo, Shichun Liu, Junzhe Wang, Shihan Dou, Enyu Zhou, Hang Yan, Zhenhua Han, Tao Gui, Qi Zhang, Xuanjing Huang

Visual Data in NLP

Challenge:	Existing work on long document visual question answering is based on Retrieval-Augmented Generation (RAG) where textual or visual content is encoded into embeddings and relevance is determined by similarity scores with respect to the original query.
Approach:	They propose a framework that employs an agentic, vision-aware workflow to address long document visual question answering through iterative information discovery and synthesis.
Outcome:	The proposed framework outperforms existing RL systems by 10.4% on the MMLongbench-Doc benchmark and demonstrates superior training performance over GRPO.

LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation (2025.findings-emnlp)

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Ming Zhang, Yujiong Shen, Zelin Li, Huayu Sha, Binze Hu, Yuhui Wang, Chenhao Huang, Shichun Liu, Jingqi Tong, Changhao Jiang, Mingxu Chai, Zhiheng Xi, Shihan Dou, Tao Gui, Qi Zhang, Xuanjing Huang

Label Smoothing for Text Mining (2022.coling-1)

Challenge:	Current medical benchmarks have limitations in question design, data sources and evaluation methods.
Approach:	They propose a new benchmark covering five core medical areas . it includes 2,996 questions created from real-world electronic health records .
Outcome:	The proposed model covers five core medical areas and includes 2,996 questions created from real-world electronic health records and expert-designed clinical scenarios.

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Peiyang Liu, Xiangyu Xi, Wei Ye, Shikun Zhang

Information Retrieval

Challenge:	Existing text mining models are trained with 0-1 hard label that indicates whether an instance belongs to a class, ignoring rich information of the relevance degree.
Approach:	They propose a keyword-based method to automatically generate soft labels from hard labels . they exploit relevance between labels and instances to incorporate them into models .
Outcome:	The proposed method improves models under balanced and unbalanced conditions.

MUSIED: A Benchmark for Event Detection from Multi-Source Heterogeneous Informal Texts (2022.emnlp-main)

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Xiangyu Xi, Jianwei Lv, Shuaipeng Liu, Wei Ye, Fan Yang, Guanglu Wan

Information Extraction & Text Mining

Challenge:	Recent efforts to classify unstructured texts into specific types have been limited in practical scenarios.
Approach:	They propose to use Chinese text conversations and phone conversations to expand event detection to the scenarios involving informal and heterogeneous texts.
Outcome:	The proposed dataset is based on user reviews, text conversations, and phone conversations in a leading e-commerce platform for food service.

GBV-SQL: Guided Generation and SQL2Text Back-Translation Validation for Multi-Agent Text2SQL (2026.acl-long)

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Daojun Chen, Xi Wang, Shenyuan Ren, Qingzhi Ma, Pengpeng Zhao, An Liu

Text Generation

Challenge:	Existing frameworks for Text2SQL generation still have a critical semantic gap . a dedicated validator translates generated SQL back into natural language and checks whether its logic is aligned with the original question.
Approach:	They propose a framework that introduces Guided Generation with SQL2Text Back-translation Validation . dedicated validator translates generated SQL back into natural language and checks whether logic is aligned with original question .
Outcome:	The proposed framework achieves 63.23% execution accuracy on the BIRD benchmark and 90.42% on repaired BIDR dev.

AEGIS: A Holistic Benchmark for Evaluating Forensic Analysis of AI-Generated Academic Images (2026.acl-long)

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Bo Zhang, Tzu-Yen Ma, Zichen Tang, Junpeng Ding, Zirui Wang, Yizhuo Zhao, Peilin Gao, Zijie Xi, Zixin Ding, Haiyang Sun, Haocheng Gao, Yuan Liu, Liangjia Wang, Yiling Huang, Yujie Wang, Yuyue Zhang, Ronghui Xi, Yuanze Li, Jiacheng Liu, Zhongjun Yang, Haihong E

What Is That Talk About? A Video-to-Text Summarization Dataset for Scientific Presentations (2025.acl-long)

Challenge:	AEGIS examines whether current models can effectively audit AI-generated images in academic papers.
Approach:	They propose a holistic benchmark for forensic analysis of AI-Generated academic ImageS that reveals limitations in academic image forensics.
Outcome:	AEGIS compared with existing benchmarks on seven academic categories and features key advances in forensic analysis.

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Dongqi Liu, Chenxi Whitehouse, Xi Yu, Louis Mahon, Rohit Saxena, Zheng Zhao, Yifu Qiu, Mirella Lapata, Vera Demberg

Text Generation

Challenge:	VISTA dataset contains 18,599 recorded AI conference presentations . large multimodal models exhibit reduced performance in scientific contexts, study shows .
Approach:	They propose a dataset specifically designed for video-to-text summarization in scientific domains.
Outcome:	This paper compares the performance of large models with human models and shows that they improve on human models.

Self-Demos: Eliciting Out-of-Demonstration Generalizability in Large Language Models (2024.findings-naacl)

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Wei He, Shichun Liu, Jun Zhao, Yiwen Ding, Yi Lu, Zhiheng Xi, Tao Gui, Qi Zhang, Xuanjing Huang

ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development (2026.findings-acl)

Challenge:	Existing methods that rely on limited demos and out-of-demonstration (OOD) queries fail when faced with out- of-demotion queries.
Approach:	They propose a query-aware prompting method that elicits the inherent generalizability of large language models by query-based demo generation.
Outcome:	The proposed method outperforms state-of-the-art methods in the OOD setting and two public math benchmarks.

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Jie Yang, Honglin Guo, Li Ji, Jiazheng Zhou, Rui Zheng, Zhikai Lei, Shuo Zhang, Zhiheng Xi, Shichun Liu, Yuxin Wang, Bo Wang, Yining Zheng, Tao Gui, Xipeng Qiu

Analyzing values about gendered language reform in LLMs’ revisions (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) have redefined the role of AI in software engineering . current benchmarks focus on localized code generation, but neglect dynamic, full-process requirements of real-world engineering.
Approach:	They propose a benchmark to evaluate agentic backend coding within a realistic, executable workflow.
Outcome:	The ABC-Bench benchmark evaluates agentic backend coding within a realistic, executable workflow.

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Jules Watson, Xi Wang, Raymond Liu, Suzanne Stevenson, Barend Beekhuizen

Challenge:	In the past years, LLMs have been used in everyday tasks, especially the formulation and revision of text.
Approach:	They examine LLMs' revision of gendered role nouns and their justifications using a prompt set-up to examine their alignment with feminist and trans-inclusive language reforms for English.
Outcome:	The proposed revision choices are based on the literature and empirical evidence.

Decoding Scientific Experimental Images: The SPUR Benchmark for Perception, Understanding, and Reasoning (2026.acl-long)

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Junpeng Ding, Zichen Tang, Haihong E, Mengyuan Ji, Yang Liu, Haolin Tian, Haiyang Sun, Pengqi Sun, Yang Xu, Yichen Liu, Haocheng Gao, Zijie Xi, Ruomeng Jiang, Peizhi Zhao, Rongjin Li, Yuanze Li, Jiacheng Liu, Zhongjun Yang, Jintong Chen, Siying Lin

StepCoder: Improving Code Generation with Reinforcement Learning from Compiler Feedback (2024.acl-long)

Challenge:	Xu and Peng, 2025) . . SPUR is a comprehensive benchmark for scientific experimental image perception, understanding, and reasoning, comprising 4,264 question-answering (QA) pairs derived from 1,084 expert-curated images.
Approach:	They propose to use 4,264 question-answering (QA) pairs derived from 1,084 expert-curated images to evaluate the visual perception of multimodal large language models (MLLMs) . they also propose to utilize cross-panel relation understanding to evaluate MLLM’s ability to decipher intricate cross-panel relations.
Outcome:	The proposed model is based on 4,264 question-answering pairs derived from 1,084 expert-curated images.

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Shihan Dou, Yan Liu, Haoxiang Jia, Enyu Zhou, Limao Xiong, Junjie Shan, Caishuang Huang, Xiao Wang, Xiaoran Fan, Zhiheng Xi, Yuhao Zhou, Tao Ji, Rui Zheng, Qi Zhang, Tao Gui, Xuanjing Huang

Programming Languages in NLP

Challenge:	Existing work integrates reinforcement learning with compiler feedback to enhance code generation quality but the long code generated by LLMs makes RL exploration ineffective.
Approach:	They propose a framework that integrates reinforcement learning and compiler feedback to enhance code generation quality.
Outcome:	The proposed framework outperforms state-of-the-art approaches in corresponding benchmarks and integrates reinforcement learning with compiler feedback to improve code generation quality.

Rethinking Stateful Tool Use in Multi-Turn Dialogues: Benchmarks and Challenges (2025.findings-acl)

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Hongru Wang, Wenyu Huang, Yufei Wang, Yuanhao Xi, Jianqiao Lu, Huan Zhang, Nan Hu, Zeming Liu, Jeff Z. Pan, Kam-Fai Wong

Stand on The Shoulders of Giants: Building JailExpert from Previous Attack Experience (2025.emnlp-main)

Challenge:	Existing benchmarks that assess Language Models (LMs) as Language Agents (LAs) for tool use focus on stateless, single-turn interactions or partial evaluations, overlooking the inherent stateful nature of interactions in multi-turn applications.
Approach:	They propose a multi-turn dialogue dataset with stateful tool interactions considering the whole life cycle of tool use across six key tasks in three stages . they also build VirtualMobile – an embodied virtual mobile evaluation environment to simulate API calls and assess the robustness of the created APIs.
Outcome:	The proposed dataset evaluates 13 open- and closed-source LLMs and provides detailed analysis at each stage.

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Xi Wang, Songlei Jian, Shasha Li, Xiaopeng Li, Bin Ji, Ma Jun, Xiaodong Liu, Jing Wang, Jianfeng Zhang, Jie Yu, Feilong Bao, null Wangbaosheng

SlideCoder: Layout-aware RAG-enhanced Hierarchical Slide Generation from Design (2025.emnlp-main)

Challenge:	Existing methods to generate human-aligned content with a “jailbreak prompt” are inefficient and repetitive, causing inefficiency and a lack of experience.
Approach:	They propose a framework that integrates past attack experiences to aid current jailbreak attempts.
Outcome:	The proposed framework improves both attack effectiveness and efficiency compared to the current black-box jailbreak method.

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Wenxin Tang, Jingyu Xiao, Wenxuan Jiang, Xi Xiao, Yuhang Wang, Xuxin Tang, Qing Li, Yuehe Ma, Junliang Liu, Shisong Tang, Michael R. Lyu

A Survey of Large Language Model-Based Search Agents (2026.acl-long)

Challenge:	Existing natural language-based LLM generation methods struggle to capture visual and structural nuances of slide designs.
Approach:	They propose a layout-aware framework for generating editable slides from reference images . they propose python code that translates NL instructions into Python code to construct each slide .
Outcome:	The proposed framework outperforms state-of-the-art models by up to 40.5 points . it also outperformed open-source models with improved reverse-engineered data.

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Yunjia Xi, Jianghao Lin, Yongzhao Xiao, Zheli Zhou, Rong Shan, Te Gao, Jiachen Zhu, Weiwen Liu, Yong Yu, Weinan Zhang

MatchPrompt: Prompt-based Open Relation Extraction with Semantic Consistency Guided Clustering (2022.emnlp-main)

Challenge:	Large Language Models (LLMs) have revolutionized web search, but their integration is static and cannot handle complex contexts.
Approach:	They analyze existing research and analyze existing work from the perspectives of architecture, optimization, application, and evaluation.
Outcome:	The proposed models can comprehend user intentions and context and execute multi-turn retrieval with dynamic planning, extending search capabilities far beyond the web.

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Jiaxin Wang, Lingling Zhang, Jun Liu, Xi Liang, Yujie Zhong, Yaqiang Wu

Information Extraction & Text Mining

Challenge:	Existing methods for open relation extraction (OpenRE) focus on labeled and pre-defined instances, which are costly to acquire in reality.
Approach:	They propose a framework that can extract relations without pre-defined types from open-domain corpus with efficient knowledge transfer from a few pre-determined relational instances.
Outcome:	The proposed framework achieves the new SOTA results for OpenRE on different datasets.

Easy and Efficient Transformer: Scalable Inference Solution For Large NLP Model (2022.naacl-industry)

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Gongzheng Li, Yadong Xi, Jingzhen Ding, Duan Wang, Ziyang Luo, Rongsheng Zhang, Bai Liu, Changjie Fan, Xiaoxi Mao, Zeng Zhao

HopRAG: Multi-Hop Reasoning for Logic-Aware Retrieval-Augmented Generation (2025.findings-acl)

Challenge:	Recent studies show that transformer-based models are effective over many tasks, but they are expensive to deploy in the industrial application.
Approach:	They propose a transformer-based inference solution that optimizes kernels for long inputs and large hidden sizes and a flexible CUDA memory manager to reduce the memory footprint when deploying a large model.
Outcome:	The proposed solution achieves an average speedup of 1.40-4.20x on the transformer decoder layer with an A100 GPU.

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Hao Liu, Zhengren Wang, Xi Chen, Zhiyu Li, Feiyu Xiong, Qinhan Yu, Wentao Zhang

Information Retrieval

Challenge:	Traditional retrieval systems focus on lexical or semantic similarity rather than logical relevance.
Approach:	They propose a new RAG framework that augments retrieval with logical reasoning . hopRAG uses a retrieve-reason-prune mechanism to explore multi-hop neighbors .
Outcome:	The proposed framework outperforms conventional retrieval systems and state-of-the-art benchmarks on multi-hop QA tasks.

Into the Gray Zone: Domain Contexts Can Blur LLM Safety Boundaries (2026.acl-long)

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Ki Sen Hung, Xi Yang, Chang Liu, Haoran Li, Kejiang Chen, Changxuan Fan, Tsun On Kwok, Weiming Zhang, Xiaomeng Li, Yangqiu Song

M2Edit: Locate and Edit Multi-Granularity Knowledge in Multimodal Large Language Model (2025.emnlp-main)

Challenge:	a goal of LLM alignment is to balance usefulness with harmlessness, but this conflictes when knowledge serves both legitimate and malicious purposes.
Approach:	They propose a framework that combines safety-research contexts with adversarial interactions to exploit a vulnerability in Jargon queries.
Outcome:	a framework outperforms existing methods in analyzing Jargon queries, a study shows . it achieves 93% of attacks across seven models, while remaining useful, the authors say .

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Yang Zhou, Pengfei Cao, Yubo Chen, Qingbin Liu, Dianbo Sui, Xi Chen, Kang Liu, Jun Zhao

Are LLMs Rational Investors? A Study on the Financial Bias in LLMs (2025.findings-acl)

Challenge:	Existing knowledge editing methods for MLLMs lack multi-granularity knowledge . existing knowledge editing approaches lack multimodality knowledge and generalize to multimodal data.
Approach:	They propose a multimodal knowledge editing method which integrates key knowledge layers within MLLMs and collaboratively edits them.
Outcome:	The proposed method improves visual generality performance on knowledge data of different granularities.

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Yuhang Zhou, Yuchen Ni, Zhiheng Xi, Zhangyue Yin, Yu He, Gan Yunhui, Xiang Liu, Zhang Jian, Sen Liu, Xipeng Qiu, Yixin Cao, Guangnan Ye, Hongfeng Chai

Data to Defense: The Role of Curation in Aligning Large Language Models Against Safety Compromise (2025.emnlp-main)

Challenge:	Existing studies on biases within specific domains, such as finance, remain limited.
Approach:	They propose a framework to detect, detect, analyze and mitigate financial biases in large language models.
Outcome:	The proposed framework reduces bias by 68% for the most biased model, according to key metrics.

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Xiaoqun Liu, Jiacheng Liang, Luoxi Tang, Muchao Ye, Weicheng Ma, Zhaohan Xi

Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words (2022.coling-1)

Challenge:	Recent studies have identified a vulnerability in large language models (LLMs) during customization.
Approach:	They propose an adaptive data curation approach that allows any text to be curated to enhance its effectiveness in counteracting harmful samples during customization.
Outcome:	The proposed approach reduces compromising effects and generates 100% safe responses.

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Haochun Wang, Chi Liu, Nuwa Xi, Sendong Zhao, Meizhi Ju, Shiwei Zhang, Ziheng Zhang, Yefeng Zheng, Bing Qin, Ting Liu

QuadrupletBERT: An Efficient Model For Embedding-Based Large-Scale Retrieval (2021.naacl-main)

Challenge:	Pre-trained models perform poorly with limited data and rare biomedical words.
Approach:	They propose to use prompt to fine-tune pre-trained models for biomedical domain tuning with a simple approach.
Outcome:	The proposed method achieves up to 6% improvement in biomedical natural language inference task without any extra parameters or training steps using few-shot vanilla prompt settings.

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Peiyang Liu, Sen Wang, Xi Wang, Wei Ye, Shikun Zhang

KuiLeiXi: a Chinese Open-Ended Text Adventure Game (2021.acl-demo)

Challenge:	Existing methods for large-scale query-document retrieval are expensive and require sparse handcrafted features.
Approach:	They propose a quadrupletBERT model for effective and efficient retrieval using pre-trained language models like BERT.
Outcome:	The proposed model improves retrieval phase and leverages distances between simple negative and hard negative instances to obtain better embeddings.

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Yadong Xi, Xiaoxi Mao, Le Li, Lei Lin, Yanjiang Chen, Shuhan Yang, Xuhan Chen, Kailun Tao, Zhi Li, Gongzheng Li, Lin Jiang, Siyan Liu, Zeng Zhao, Minlie Huang, Changjie Fan, Zhipeng Hu