Papers by Qiang Liu

Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models (2024.findings-acl)

Challenge:	Existing methods focus on replicating dialogues in textual form, neglecting the role’s voice traits as a crucial effect in interaction, which tends to be more immersive experiences in realistic scenarios.
Approach:	They propose a first seamless speech-language personality interaction model to achieve immersive RPAs with low latency.
Outcome:	The proposed model exhibits role-specific personality traits and vocal traits throughout the interaction, enabling a mixture of speech and language responses.

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Junfei Wu, Qiang Liu, Ding Wang, Jinghao Zhang, Shu Wu, Liang Wang, Tieniu Tan

Harmless Transfer Learning for Item Embeddings (2022.findings-naacl)

Challenge:	Object hallucination has been an Achilles’ heel which hinders the broader applications of large vision-language models (LVLMs).
Approach:	They propose a logical closed loop-based framework for Object Hallucination Detection and Mitigation that uses logical consistency probing to raise questions with logical correlations to determine hallucinations.
Outcome:	The proposed method can be applied to all existing LVLMs and is effective and general.

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Chengyue Gong, Xiaocong Du, Dhruv Choudhary, Bhargav Bhushanam, Qiang Liu, Arun Kejariwal

Task Oriented In-Domain Data Augmentation (2024.emnlp-main)

Challenge:	Existing approaches to learn item embeddings for categorical features are limited by the frequency of items in real-world.
Approach:	They propose a method that transfers knowledge from frequent items to rare items by introducing an auxiliary transfer loss.
Outcome:	The proposed framework significantly boosts the performance on a variety of NLP and recommendation system tasks.

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Xiao Liang, Xinyu Hu, Simiao Zuo, Yeyun Gong, Qiang Lou, Yi Liu, Shao-Lun Huang, Jian Jiao

Numerical Reasoning

Challenge:	Existing methods for large language models suffer from two major issues: in-domain data are scarce compared with general domain-agnostic data.
Approach:	They propose a task-oriented in-domain data augmentation framework that uses in- domain data selection and task-orientated synthetic passage generation to adapt LLMs to two domains: advertisement and math.
Outcome:	The proposed framework improves LLM performance by 8% in the advertisement domain and 7.5% in the math domain.

AgentCourt: Simulating Court with Adversarial Evolvable Lawyer Agents (2025.findings-acl)

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Guhong Chen, Liyang Fan, Zihan Gong, Nan Xie, Zixuan Li, Ziqiang Liu, Chengming Li, Qiang Qu, Hamid Alinejad-Rokny, Shiwen Ni, Min Yang

RealChart2Code: Bridging the Gap in Real-World Chart-to-Code Generation via Multi-Task Evaluation (2026.acl-long)

Challenge:	Existing legal language models struggle with dynamic courtroom interactions, resulting in overfitting to standardized legal tasks.
Approach:	They propose a new adversarial evolutionary approach for agents that performs dynamic knowledge learning and evolution through structured adversarials in a simulated courtroom program.
Outcome:	The proposed approach outperforms existing LLM-based models in three critical dimensions: cognitive agility, professional knowledge, and logical rigor.

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Jiajun Zhang, Yuying Li, Zhixun Li, Xingyu Guo, Jingzhuo Wu, Leqi Zheng, Yiran Yang, Jianke Zhang, Qingbin Li, Shannan Yan, Changguo Jia, Junfei Wu, Zilei Wang, Qiang Liu, Liang Wang

Text Generation

Challenge:	Vision-Language Models (VLMs) have demonstrated impressive capabilities in code generation across various domains, but their ability to replicate complex, multi-panel visualizations remains largely unassessed.
Approach:	They propose a large-scale benchmark to evaluate chart generation from large- scale raw data and assess iterative code refinement in a multi-turn conversational setting.
Outcome:	The new benchmark evaluates 14 leading VLMs on real-world data and shows they struggle with complex plot structures and authentic data.

Chinese Lexical Substitution: Dataset and Method (2023.emnlp-main)

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Jipeng Qiang, Kang Liu, Ying Li, Yun Li, Yi Zhu, Yun-Hao Yuan, Xiaocheng Hu, Xiaoye Ouyang

Low-Resource NLP

Challenge:	Existing benchmarks for lexical substitution (LS) are limited and limited in coverage . despite extensive research on Lexical Substitution in various languages, there is limited evidence for LS in Chinese.
Approach:	They propose to use human and machine collaboration to construct a Chinese LS dataset . they combine four unsupervised LS methods to generate candidate substitutes .
Outcome:	The proposed method outperforms existing benchmarks on the Chinese lexical substitution task.

Beyond Frameworks: Unpacking Collaboration Strategies in Multi-Agent Systems (2025.acl-long)

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Haochun Wang, Sendong Zhao, Jingbo Wang, Zewen Qiang, Bing Qin, Ting Liu

Challenge:	Existing frameworks prioritize structural architectures and role assignments but neglect granular mechanics of agent collaboration.
Approach:	They propose to use centralized governance, instructor-led participation, ordered interaction patterns to optimize task accuracy and computational efficiency.
Outcome:	The proposed model improves task accuracy and computational efficiency under two context-dependent scenarios.

SINCon: Mitigate LLM-Generated Malicious Message Injection Attack for Rumor Detection (2025.acl-long)

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Mingqing Zhang, Qiang Liu, Xiang Tao, Shu Wu, Liang Wang

Personalized Text Generation with Contrastive Activation Steering (2025.acl-long)

Challenge:	Existing methods define important nodes as important and target them for attacks if the model treats nodes’ predictive influence more uniformly . Existing approaches target high predictive influence nodes but are vulnerable to malicious message injection attacks.
Approach:	They propose a defense mechanism that encourages the model to learn graph representations where nodes with varying importance have a more uniform influence on predictions.
Outcome:	Extensive experiments on the Twitter and Weibo datasets show that similarizing the predictive Influence of nodes with Contrastive Learning significantly enhances resistance against LLM-driven message injection attacks.

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Jinghao Zhang, Yuting Liu, Wenjie Wang, Qiang Liu, Shu Wu, Liang Wang, Tat-Seng Chua

Information Retrieval

Challenge:	Existing approaches to personalized text generation rely on retrieval-augmented generation and parameter-efficient fine-tuning.
Approach:	They propose a training-free framework that disentangles and represents personalized writing style as a vector in LLM’s activation-space.
Outcome:	The proposed framework achieves 8% relative improvement in personalized generation while reducing storage requirements by 1700 over PEFT method.

Mixture of Decoding: An Attention-Inspired Adaptive Decoding Strategy to Mitigate Hallucinations in Large Vision-Language Models (2025.findings-acl)

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Xinlong Chen, Yuanxing Zhang, Qiang Liu, Junfei Wu, Fuzheng Zhang, Tieniu Tan

KELE: Residual Knowledge Erasure for Enhanced Multi-hop Reasoning in Knowledge Editing (2025.findings-emnlp)

Challenge:	Large Vision-Language Models (LVLMs) have impressive capabilities across visual tasks, yet they remain hindered by the persistent challenge of hallucinations.
Approach:	They propose a novel approach that dynamically adapts decoding strategies by evaluating the correctness of the model’s attention on image tokens to distinguish the correct attention.
Outcome:	Extensive experiments show that the proposed approach outperforms existing decoding methods across multiple mainstream benchmarks, effectively mitigating hallucinations in LVLMs.

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Mengqi Zhang, Bowen Fang, Qiang Liu, Xiaotian Ye, Shu Wu, Pengjie Ren, Zhumin Chen, Liang Wang

Reasoning Like Program Executors (2022.emnlp-main)

Challenge:	Existing knowledge editing techniques show limitations when applied to multi-hop reasoning . residual single-hop knowledge causes edited models to revert to original answers .
Approach:	They propose a knowledge editing method that incorporates a Knowledge Erasure mechanism for Large language model Editing (KELE) they propose an erasure function for residual knowledge and an injection function for new knowledge .
Outcome:	The proposed method significantly improves multi-hop reasoning capability of edited models.

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Xinyu Pi, Qian Liu, Bei Chen, Morteza Ziyadi, Zeqi Lin, Qiang Fu, Yan Gao, Jian-Guang Lou, Weizhu Chen

VidCapBench: A Comprehensive Benchmark of Video Captioning for Controllable Text-to-Video Generation (2025.findings-acl)

Challenge:	Existing language models are inadequate in reasoning, according to studies . a new reasoning pre-training paradigm is based on pretraining language models with programs .
Approach:	They propose a reasoning pre-training paradigm that empowers language models to harvest reasoning knowledge possessed by program executors.
Outcome:	The proposed reasoning pre-training paradigm can boost models' reasoning skills . it can be instantiated by different kinds of program executors and run on a single database .

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Xinlong Chen, Yuanxing Zhang, Chongling Rao, Yushuo Guan, Jiaheng Liu, Fuzheng Zhang, Chengru Song, Qiang Liu, Di Zhang, Tieniu Tan

GenPilot: A Multi-Agent System for Test-Time Prompt Optimization in Image Generation (2025.findings-emnlp)

Challenge:	Existing studies have not identified a link between video caption evaluation and T2V generation.
Approach:	They propose a video caption evaluation scheme specifically designed for T2V generation that integrates video annotation with caption evaluation.
Outcome:	The proposed system is agnostic to any particular caption format and can be used for training.

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Wen Ye, Zhaocheng Liu, Gui Yuwei, Tingyu Yuan, Yunyue Su, Bowen Fang, Chaoyang Zhao, Qiang Liu, Liang Wang

EX-FEVER: A Dataset for Multi-hop Explainable Fact Verification (2024.findings-acl)

Challenge:	Existing methods for text-to-image synthesis lack systematic error analysis and refinement strategies, resulting in limited reliability and effectiveness.
Approach:	They propose a plug-and-play multi-agent system called GenPilot that integrates error analysis, clustering-based adaptive exploration, fine-grained verification and a memory module for iterative optimization.
Outcome:	The proposed method improves text consistency and structural coherence on images with a plug-and-play system.

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Huanhuan Ma, Weizhi Xu, Yifan Wei, Liuji Chen, Liang Wang, Qiang Liu, Shu Wu, Liang Wang

A Black-Box Attack on Code Models via Representation Nearest Neighbor Search (2023.findings-emnlp)

Challenge:	Existing studies on fact verification lack a high-quality dataset for explainability . existing systems lack evidence retrieval and veracity prediction, limiting the ability to verify a claim .
Approach:	They propose a dataset for multi-hop explainable fact verification that summarises and modifies Wikipedia documents.
Outcome:	The proposed dataset aims to improve the accuracy of multi-hop explainable fact verification systems.

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Jie Zhang, Wei Ma, Qiang Hu, Shangqing Liu, Xiaofei Xie, Yves Le Traon, Yang Liu

CARL: Constraint-Aware Reinforcement Learning for Planning with LLMs (2026.findings-acl)

Challenge:	Existing methods for generating adversarial code examples face challenges such as limted availability of substitute variables and the creation of adversarials with noticeable perturbations.
Approach:	They propose a search seed based on historical attacks to find adversarial substitutes . they employ a pre-trained variable name encoder to map the search seed to a continuous vector space .
Outcome:	The proposed approach outperforms baseline methods in terms of ASR and QT.

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Qiuyi Qi, Jinjian Zhang, Mutian Bao, Tian Liang, Guocong Li, Dongnan Liu, Wei Zhou, Jie Liu, Ming Kong, Linjian Mo, Feng Zhang, Qiang Zhu

Generate First, Then Sample: Enhancing Fake News Detection with LLM-Augmented Reinforced Sampling (2025.acl-long)

Challenge:	Existing approaches to constraint-aware planning fail to enhance the model’s intrinsic focus on constraints.
Approach:	They propose a constraint-aware reinforcement learning framework that encourages constraint focus and penalizes neglect of LLMs.
Outcome:	The proposed framework outperforms existing frameworks and state-of-the-art reasoning models in a number of real-world applications.

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Zhao Tong, Yimeng Gu, Huidong Liu, Qiang Liu, Shu Wu, Haichao Shi, Xiao-Yu Zhang

Chain-of-History Reasoning for Temporal Knowledge Graph Forecasting (2024.findings-acl)

Challenge:	Existing models have a performance gap of 20% between classifying fake news and real news, making them less suitable for practical deployment.
Approach:	They propose to adopt an LLM to generate fake news in three different styles, which are later incorporated into the training set to augment the representation of fake news.
Outcome:	The proposed model achieves state-of-the-art performance on two benchmark datasets and improves detection accuracy by 24.02% and 11.06% respectively.

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Yuwei Xia, Ding Wang, Qiang Liu, Liang Wang, Shu Wu, Xiao-Yu Zhang

Open Domain Question Answering with Conflicting Contexts (2025.findings-naacl)

Challenge:	Existing graph-based models excel at capturing structural information within TKGs but lack semantic comprehension abilities.
Approach:	They propose a plug-and-play module to enhance the performance of graph-based TKG models by exploring high-order histories step-by-step.
Outcome:	Experiments on three datasets and backbones show that CoH is effective in capturing high-order historical information for LLMs.

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Siyi Liu, Qiang Ning, Kishaloy Halder, Zheng Qi, Wei Xiao, Phu Mon Htut, Yi Zhang, Neha Anna John, Bonan Min, Yassine Benajiba, Dan Roth

Natural Language Interfaces

Challenge:	Open domain question answering systems often rely on information retrieved from large collections of text to answer questions.
Approach:	They evaluate and benchmark three powerful Large Language Models with a dataset . they find that 25% of unambiguous open domain questions can lead to conflicting contexts .
Outcome:	The proposed model can't be used to answer questions with conflicting contexts . it can be fine tuned to provide richer information into the model's training .

Logic-Thinker: Teaching Large Language Models to Think more Logically. (2025.findings-emnlp)

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Chengyao Wen, Qiang Cheng, Shaofei Wang, Zhizhen Liu, Deng Zhao, Lei Liang

Attention and Edge-Label Guided Graph Convolutional Networks for Named Entity Recognition (2022.emnlp-main)

Challenge:	Recent Large Reasoning Models (LRMs) have demonstrated the ability to generate long chains of thought (LongCoT) LongCoT still faces challenges such as redundancy and logical incoherence.
Approach:	They propose a neural-symbolic reasoning framework that generates chains of thought . they propose Logic-Thinker, which transforms symbolic solvers into chains of thoughts .
Outcome:	The proposed framework outperforms models fine-tuned with ThinkerCoT on logic reasoning tasks.

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Renjie Zhou, Zhongyi Xie, Jian Wan, Jilin Zhang, Yong Liao, Qiang Liu

Syntactic Text Processing

Challenge:	Named entity recognition (NER) is the recognition of entities with specific meanings in the text, mainly including person, organization, location, etc.
Approach:	They propose an edge-aware node joint update module and introduce a node-awful edge update module to explore hidden in structured information and solve the wrong dependency label information to some extent.
Outcome:	The proposed model can exploit the structured information on the dependency tree to improve the recognition of long entities.

CodeTool: Enhancing Programmatic Tool Invocation of LLMs via Process Supervision (2025.acl-long)

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YifeiLu YifeiLu, Fanghua Ye, Jian Li, Qiang Gao, Cheng Liu, Haibo Luo, Nan Du, Xiaolong Li, Feiliang Ren

Programming Languages in NLP

Challenge:	Existing approaches to tool invocation are often unnecessarily long and require lengthy reasoning paths.
Approach:	They propose a framework for stepwise code generation that improves LLM tool invocation . they incorporate two distinct process rewards: the On-the-spot and the Latent Reward .
Outcome:	The proposed framework improves LLM tool invocation by leveraging the concise nature of code.

SAFER: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutions (2020.acl-main)

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Mao Ye, Chengyue Gong, Qiang Liu

ParaLS: Lexical Substitution via Pretrained Paraphraser (2023.acl-long)

Challenge:	State-of-the-art NLP models can be fooled by human-unaware transformations such as synonymous word substitution.
Approach:	They propose a method that constructs a stochastic ensemble by applying random word substitutions on the input sentences and leverages the statistical properties to provably certify the robustness.
Outcome:	The proposed method outperforms state-of-the-art methods on IMDB and Amazon text classification tasks with practically meaningful certified accuracy.

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Jipeng Qiang, Kang Liu, Yun Li, Yunhao Yuan, Yi Zhu

MorphoBench: A Benchmark with Difficulty Adaptive to Model Reasoning (2026.findings-acl)

Challenge:	Lexical substitution (LS) is an extremely powerful technology that can be used as a backbone of various NLP applications such as writing assistance.
Approach:	They propose two simple decoding strategies that focus on the variations of the target word during decoding to generate substitutes from a paraphraser.
Outcome:	The proposed methods outperform state-of-the-art LS methods based on pre-trained language models on three benchmarks.

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Xukai Wang, Xuanbo Liu, Mingrui Chen, Haitian Zhong, Xuanlin Yang, Bohan Zeng, Jinbo Hu, Hao Liang, Junbo Niu, Xuchen Li, Ruitao Wu, Ruichuan An, Yang Shi, Liu Liu, Qiang Liu, Zhouchen Lin, Xu-Yao Zhang, Wentao Zhang, Bin Dong

UnrealLLM: Towards Highly Controllable and Interactable 3D Scene Generation by LLM-powered Procedural Content Generation (2025.findings-acl)

Challenge:	Existing benchmarks designed to evaluate the reasoning capabilities of large models are limited in scope and lack flexibility to adapt difficulty according to evolving reasoning capacities of models.
Approach:	They propose a benchmark that incorporates multidisciplinary questions to evaluate the reasoning capabilities of large models and can adjust and update question difficulty based on the reasoning abilities of advanced models.
Outcome:	The proposed benchmark incorporates multidisciplinary questions to evaluate the reasoning capabilities of large models and can adjust and update question difficulty based on the reasoning abilities of advanced models.

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SongTang SongTang, Kaiyong Zhao, Lei Wang, Yuliang Li, Xuebo Liu, Junyi Zou, Qiang Wang, Xiaowen Chu

Challenge:	UnrealLLM is a novel framework that connects natural language descriptions with the professional PCG system (Unreal Engine 5) to automate scene generation.
Approach:	They propose a novel multi-agent framework that connects natural language descriptions with the professional PCG system (Unreal Engine 5) to automate scene generation.
Outcome:	The proposed framework achieves competitive performance in technical metrics and aesthetic quality, offering unique advantages in generation scale and interactivity.

Forewarned is Forearmed: Pre-Synthesizing Jailbreak-like Instructions to Enhance LLM Safety Guardrail to Potential Attacks (2025.findings-emnlp)

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Sheng Liu, Qiang Sheng, Danding Wang, Yang Li, Guang Yang, Juan Cao

DeepRTL2: A Versatile Model for RTL-Related Tasks (2025.findings-acl)

Challenge:	Despite advances in improving large language model (LLM) to refuse to answer malicious instructions, LLMs remain vulnerable to jailbreak attacks where attackers generate instructions with distributions differing from safety alignment corpora.
Approach:	They propose a framework that leverages embedding space distribution analysis to generate jailbreak-like instructions.
Outcome:	The proposed framework shows significant decreases in attack success rate on Qwen2.5, Llama3.1, and Llma3.2 without compromising their utility.

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Yi Liu, Hongji Zhang, Yunhao Zhou, Zhengyuan Shi, Changran Xu, Qiang Xu

A Survey on MLLM-based Visually Rich Document Understanding: Methods, Challenges, and Emerging Trends (2026.findings-acl)

Challenge:	Integration of large language models into electronic design automation has been a key driver in eDA.
Approach:	They propose a family of large language models that unifies generation- and embedding-based tasks related to RTL.
Outcome:	The proposed model achieves state-of-the-art performance across all evaluated tasks.

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Yihao Ding, Siwen Luo, Yue Dai, Yanbei Jiang, Zechuan Li, Qiang Sun, Geoffrey Martin, Wei Liu, Yifan Peng

DocSpiral: A Platform for Integrated Assistive Document Annotation through Human-in-the-Spiral (2025.acl-demo)

Challenge:	Visually Rich Document Understanding (VRDU) frameworks are a key area of research . early approaches to VRDU relied on manually crafted rules and domain-specific heuristics . conventional deep learning approaches do not integrate the diverse modalities in documents .
Approach:	They review recent advances in MLLM-based Visually Rich Document Understanding (VRDU) their findings highlight emerging trends and promising research directions .
Outcome:	The proposed frameworks are scalable, reliable, and adaptable, the authors argue . their findings highlight emerging trends and promising research directions .

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Qiang Sun, Sirui Li, Tingting Bi, Du Q. Huynh, Mark Reynolds, Yuanyi Luo, Wei Liu

A2ATS: Retrieval-Based KV Cache Reduction via Windowed Rotary Position Embedding and Query-Aware Vector Quantization (2025.findings-acl)

Challenge:	Documents that are image-based are difficult to extract because of document variability.
Approach:	They propose a human-in-the-spiral assistive document annotation platform to extract structured data from document collections.
Outcome:	The proposed framework reduces annotation time by at least 41% while showing consistent performance gains over three iterations.

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Junhui He, Junna Xing, Nan Wang, Rui Xu, Shangyu Wu, Peng Zhou, Qiang Liu, Chun Jason Xue, Qingan Li

Toolscaler: Scalable Generative Tool Calling via Structure-Aware Semantic Tokenization (2025.findings-emnlp)

Challenge:	Long context large language models (LLMs) pose significant challenges for efficient serving due to the large memory footprint and high access overhead of KV cache.
Approach:	They propose a retrieval-based method to reduce the memory footprint of LLMs . they propose Windowed Rotary Position Embedding and query-aware vector quantization .
Outcome:	The proposed method can achieve lower performance degradation with lower overhead compared to existing methods . it can reduce the memory footprint and access overhead of long context large language models .

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Yunyue Su, Zhang Jinshuai, Bowen Fang, Wen Ye, Jinghao Zhang, Bowen Song, Weiqiang Wang, Qiang Liu, Liang Wang

Syntactic Text Processing

Challenge:	Extensive experiments demonstrate the effectiveness of SGTC across various tasks.
Approach:	They propose a generative tool invocation framework that introduces structure-aware semantic tokenization to encode tools as discrete code sequences.
Outcome:	The proposed framework reduces the size of the representation space and underutilizes collaborative signals among tools in downstream tasks.

Knowledge Graph Enhanced Large Language Model Editing (2024.emnlp-main)

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Mengqi Zhang, Xiaotian Ye, Qiang Liu, Pengjie Ren, Shu Wu, Zhumin Chen

Mathematical Word Problem Generation from Commonsense Knowledge Graph and Equations (2021.emnlp-main)

Challenge:	Existing methods for editing large language models struggle to track and incorporate changes in knowledge associated with edits, which limits the generalization ability of post-edit LLMs in processing edited knowledge.
Approach:	They propose a model editing method that leverages knowledge graphs to enhance LLM editing by capturing changes in associated knowledge by constructing an external graph.
Outcome:	The proposed method improves the generalization ability of LLMs in processing edited knowledge.

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Tianqiao Liu, Qiang Fang, Wenbiao Ding, Hang Li, Zhongqin Wu, Zitao Liu

Counterfactual Debiasing for Fact Verification (2023.acl-long)

Challenge:	Existing models for generating mathematical word problems are lacking in educational assessment.
Approach:	They propose an end-to-end neural model to generate diverse mathematical word problems from commonsense knowledge graph and equations.
Outcome:	The proposed model outperforms the SOTA models in terms of evaluation metrics and topic relevance.

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Weizhi Xu, Qiang Liu, Shu Wu, Liang Wang

Aspect and Sentiment Aware Abstractive Review Summarization (C18-1)

Challenge:	Existing methods for debiasing factchecking models learn such biases instead of understanding the semantic relationship between the claim and evidence.
Approach:	They propose a counterfactual framework CLEVER which is augmentation-free and mitigates biases on the inference stage.
Outcome:	The proposed method is augmentation-free and mitigates biases on the inference stage.

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Min Yang, Qiang Qu, Ying Shen, Qiao Liu, Wei Zhao, Jia Zhu

Summarization

Challenge:	Abstractive summarization is a task that generates short and concise summaries of user generated reviews.
Approach:	They propose an interactive attention mechanism to learn the representations of context and aspect words within reviews, acted as an encoder.
Outcome:	The proposed model achieves impressive results compared to other strong competitors on a real-life dataset.

Learning Latent Relations for Temporal Knowledge Graph Reasoning (2023.acl-long)

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Mengqi Zhang, Yuwei Xia, Qiang Liu, Shu Wu, Liang Wang

Structured Data in NLP

Challenge:	Existing methods for Temporal Knowledge Graph reasoning capture intra- and inter-time latent relations between entities that appear at different times.
Approach:	They propose a Latent relations Learning method for TKG reasoning that captures latent relations between entities at different times.
Outcome:	The proposed method exploits the intra- and inter-time latent relations of entities at different times.

MetaTKG: Learning Evolutionary Meta-Knowledge for Temporal Knowledge Graph Reasoning (2022.emnlp-main)

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Yuwei Xia, Mengqi Zhang, Qiang Liu, Shu Wu, Xiao-Yu Zhang

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

Challenge:	Existing models rely on historical information to learn embeddings for entities, but ignore the evolution of facts.
Approach:	They propose a Temporal Meta-learning framework to learn evolutionary meta-knowledge from TKGs.
Outcome:	The proposed method improves on four widely-used datasets and three backbones on a wide range of scenarios on tKGs.

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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.

DUBLIN: Visual Document Understanding By Language-Image Network (2023.emnlp-industry)

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Kriti Aggarwal, Aditi Khandelwal, Kumar Tanmay, Owais Khan Mohammed, Qiang Liu, Monojit Choudhury, Hardik Chauhan, Subhojit Som, Vishrav Chaudhary, Saurabh Tiwary

Visual Data in NLP

Challenge:	DUBLIN is a pixel-based visual document understanding model that does not rely on OCR.
Approach:	They propose a pixel-based visual document understanding model that does not rely on OCR.
Outcome:	The proposed model performs on extractive tasks such as DocVQA, InfoVQA and AI2D, and strong performance on abstraction datasets such as VisualMRC and text captioning.

Evaluating Models’ Local Decision Boundaries via Contrast Sets (2020.findings-emnlp)

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Matt Gardner, Yoav Artzi, Victoria Basmov, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hannaneh Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, Ally Zhang, Ben Zhou

Challenge:	Standard test sets for supervised learning evaluate in-distribution generalization but are misleading when a dataset has systematic gaps.
Approach:	They propose a more rigorous annotation paradigm for NLP that helps to close systematic gaps in the test data.
Outcome:	The proposed model performs significantly lower on contrast sets than on the original test sets—up to 25% in some cases.

OpenOmni: A Collaborative Open Source Tool for Building Future-Ready Multimodal Conversational Agents (2024.emnlp-demo)

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Qiang Sun, Yuanyi Luo, Sirui Li, Wenxiao Zhang, Wei Liu

SHARP: Steering Hallucination in LVLMs via Representation Engineering (2025.emnlp-main)

Challenge:	OpenOmni is an open-source, end-to-end pipeline benchmarking tool for multimodal conversational agents.
Approach:	They developed an open-source, end-to-end pipeline benchmarking tool to help solve these issues.
Outcome:	OpenOmni integrates speech-to-text, emotion detection, and large language models with the ability to integrate customized models.

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Junfei Wu, Yue Ding, Guofan Liu, Tianze Xia, Ziyue Huang, Dianbo Sui, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan

Plug-and-Play Data Module for Code RL: Adaptive Ambiguity Replay (2026.findings-acl)

Challenge:	Large Vision-Language Models (LVLMs) generate responses that are plausible but incorrect or unsupported—commonly referred to as hallucinations.
Approach:	They propose a representation-level intervention framework that modulates hallucination-related features during inference by probing their encoded features.
Outcome:	The proposed framework reduces hallucinations while maintaining the performance and generalization capabilities of Large Vision-Language Models (LVLMs).

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Jianqing Zhang, Wei Xia, Zhezheng Hao, Hong Wang, Hande Dong, Qiang Lin, Yang Liu, Jian Cao, Qiang Yang

Chinese Morph Resolution in E-commerce Live Streaming Scenarios (2025.naacl-industry)

Challenge:	Existing approaches to reinforcement learning (RL) rely on static, in-epoch metrics that overlook training dynamics, often introducing low-utility or outdated data.
Approach:	They propose a plug-and-play module that prioritizes cross-epoch ambiguous samples to neutralize the noise from stale experiences.
Outcome:	Extensive experiments on nine LLMs show that Adaptive Ambiguity Replay outperforms state-of-the-art baselines on real-world code editing tasks.

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Jiahao Zhu, Jipeng Qiang, Ran Bai, Chenyu Liu, Xiaoye Ouyang

Challenge:	Live morph resolution task is used to detect e-commerce live streaming violations . morphs are used to evade scrutiny and engage in false advertising .
Approach:	They propose a task to detect morph violations in live streaming scenarios . they use large language models to generate additional training data .
Outcome:	The proposed method improves performance and improves live streaming regulation.

RIVAL: Reinforcement Learning with Iterative and Adversarial Optimization for Machine Translation (2025.findings-emnlp)

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Tianjiao Li, Mengran Yu, Chenyu Shi, Yanjun Zhao, Xiaojing Liu, Qi Zhang, Xuanjing Huang, Qiang Zhang, Jiayin Wang

Text Generation

Challenge:	Using reinforcement learning from human feedback, large language models perform poorly when applied to colloquial subtitle translation tasks.
Approach:	They propose an adversarial training framework that iteratively updates the offline reward model and the online LLM to improve training outcomes.
Outcome:	The proposed training framework significantly improves upon translation baselines.

LPZero: Language Model Zero-cost Proxy Search from Zero (2024.findings-emnlp)

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Peijie Dong, Lujun Li, Xiang Liu, Zhenheng Tang, Xuebo Liu, Qiang Wang, Xiaowen Chu

Rethinking Entropy Interventions in RLVR: An Entropy Change Perspective (2026.acl-long)

Challenge:	Existing zero-cost (ZC) proxies rely on expert knowledge and incur significant trial-and-error costs.
Approach:	They propose a framework that automatically designs zero-cost (ZC) proxies for various tasks and incorporates genetic programming to find the optimal symbolic composition.
Outcome:	The proposed framework achieves higher ranking consistency than human-designed proxies on NLP tasks.

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Zhezheng Hao, Hong Wang, Haoyang Liu, Jian Luo, Jiarui Yu, Hande Dong, Qiang Lin, Can Wang, Jiawei Chen

Evaluating the Expressive Appropriateness of Speech in Rich Contexts (2026.acl-long)

Challenge:	Large Language Models (LLMs) have remarkable reasoning capabilities in complex tasks such as mathematics and coding.
Approach:	They propose an entropy-modulation method that adaptively reweighs tokens based on theoretically-estimated entropic variations.
Outcome:	The proposed method outperforms state-of-the-art methods in six mathematical reasoning and three coding benchmarks.

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Tianrui Wang, Ziyang Ma, Yizhou Peng, Haoyu Wang, Zhikang Niu, Zikang Huang, Yihao Wu, Yi-Wen Chao, Yu Jiang, Yuheng Lu, Guanrou Yang, Xuanchen Li, Hexin Liu, Chunyu Qiang, Cheng Gong, Yifan Yang, Tianchi Liu, Junyu Wang, Nana Hou, Meng Ge, Fuming You, Yang Wei, Zhongqian Sun, Hu Haifeng, Xiaobao Wang, Eng Siong Chng, Xie Chen, Longbiao Wang, Jianwu Dang

Speech & Audio in NLP

Challenge:	Existing methods for evaluating expressive speech focus on word accuracy, naturalness, signal quality, or emotional intensity at the utterance level.
Approach:	They propose a framework for Evaluating Expressive Appropriateness in speech that assesses whether a speech sample aligns with the underlying communicative intent implied by its discourse-level narrative context.
Outcome:	The proposed framework outperforms existing speech evaluation and analysis systems on a human-annotated test set.

Stealthy Attack on Large Language Model based Recommendation (2024.acl-long)

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Jinghao Zhang, Yuting Liu, Qiang Liu, Shu Wu, Guibing Guo, Liang Wang

REACT: Representation Extraction And Controllable Tuning to Overcome Overfitting in LLM Knowledge Editing (2025.emnlp-main)

Challenge:	Recent advances in recommender systems have been overlooked due to their emphasis on textual content.
Approach:	They propose to introduce large language models into recommendation models to exploit the semantic understanding and strong transferability of LLMs.
Outcome:	The proposed approach significantly boosts an item’s exposure by altering its textual content during the testing phase, without requiring direct interference with the model’s training process.

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Haitian Zhong, Yuhuan Liu, Ziyang Xu, Guofan Liu, Qiang Liu, Shu Wu, Zhe Zhao, Liang Wang, Tieniu Tan

From Completion to Editing: Unlocking Context-Aware Code Infilling via Search-and-Replace Instruction Tuning (2026.acl-long)

Challenge:	Large language model editing methods suffer from overfitting, where factual updates can propagate beyond their intended scope, overemphasizing the edited target even when it’s contextually inappropriate.
Approach:	They propose a framework for precise and controllable knowledge editing that utilizes two-phase representations and a linear transformation to compute a directional "belief shift" vector.
Outcome:	The proposed framework significantly reduces overfitting across nearly all evaluation metrics and on COUNTERFACT and MQuAKE.

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Jiajun Zhang, Zeyu Cui, Jiaxi Yang, Lei Zhang, Yuheng Jing, Zeyao Ma, Tianyi Bai, Zilei Wang, Qiang Liu, Liang Wang, Binyuan Hui, Junyang Lin

Programming Languages in NLP

Challenge:	Fill-in-the-Middle (FIM) models suffer from performance degradation and prohibitive latency.
Approach:	They propose a search-and-replace infilling framework that integrates agentic verification and editing into a single-pass inference process.
Outcome:	The proposed framework harmonizes completion tasks with the instruction-following priors of Chat LLMs, extending the paradigm from static infilling to dynamic context-aware editing.

Do Code Semantics Help? A Comprehensive Study on Execution Trace-Based Information for Code Large Language Models (2025.findings-emnlp)

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Jian Jornbowrl Wang, Xiaofei Xie, Qiang Hu, Shangqing Liu, Yi Li

STAPO: Selective Trajectory-Aware Policy Optimization for LLM Agent Training (2026.acl-long)

Challenge:	Code Large Language Models have limited ability to reason about runtime behavior and understand functionality . authors present a generic framework to support integrating semantic information to code task-relevant prompts .
Approach:	a study examines the role of trace-based semantic information in boosting supervised fine-tuning and post-phase inference of Code LLMs.
Outcome:	a new framework integrates semantic information to code task-relevant prompts . the proposed framework shows that trace-based semantic information boosts reasoning ability .

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Qiuyi Qi, Tian Liang, Mutian Bao, Jinjian Zhang, Dongnan Liu, Wei Zhou, Linjian Mo, Ming Kong, Jie Liu, Feng Zhang, Qiang Zhu

CFinBench: A Comprehensive Chinese Financial Benchmark for Large Language Models (2025.naacl-long)

Challenge:	Prior work has explored step-level supervision using Shannon-entropy-based uncertainty signals, which conflate inherent state complexity with agent confidence.
Approach:	They propose a hierarchical group-based RL framework that leverages normalized entropy to locate outlier steps associated with trajectory neglect and optimizes them via a mechanism of trajectory-aware reward and trajectory-independent penalty.
Outcome:	Experiments on ALFWorld, WebShop, and Search-Augmented QA show that STAPO achieves state-of-the-art performance while substantially alleviating trajectory neglect.

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Ying Nie, Binwei Yan, Tianyu Guo, Hao Liu, Haoyu Wang, Wei He, Binfan Zheng, Weihao Wang, Qiang Li, Weijian Sun, Yunhe Wang, Dacheng Tao

CPRM: A LLM-based Continual Pre-training Framework for Relevance Modeling in Commercial Search (2025.naacl-industry)

Challenge:	Large language models (LLMs) have achieved remarkable performance on various NLP tasks, yet their potential in more challenging task like finance, has not been fully explored.
Approach:	They propose a benchmark to assess the financial knowledge of large language models (LLMs) in China.
Outcome:	The proposed benchmark is the most comprehensive evaluation benchmark to date for LLMs in finance.

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Kaixin Wu, Yixin Ji, Zeyuan Chen, Qiang Wang, Cunxiang Wang, Hong Liu, Baijun Ji, Xu Jia, Zhongyi Liu, Jinjie Gu, Yuan Zhou, Linjian Mo

Divide-Then-Align: Honest Alignment based on the Knowledge Boundary of RAG (2025.acl-long)

Challenge:	Relevance modeling between queries and items is a key component of commercial search engines.
Approach:	They propose a framework for continual pre-training of LLMs to enhance domain knowledge . they employ queries and multi-field item to jointly pre-train for enhancing domain knowledge.
Outcome:	The proposed model achieves convincing performance compared to strong baselines.

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Xin Sun, Jianan Xie, Zhongqi Chen, Qiang Liu, Shu Wu, Yuehe Chen, Bowen Song, Zilei Wang, Weiqiang Wang, Liang Wang

Information Retrieval

Challenge:	Large language models (LLMs) augmented with retrieval systems have significantly advanced natural language processing tasks by integrating external knowledge sources.
Approach:	They propose a method that conditions large language models to generate answers even in the absence of reliable knowledge.
Outcome:	The proposed approach balances accuracy with appropriate abstention, enhancing the reliability and trustworthiness of retrieval-augmented systems.

Noise-Robust Semi-Supervised Learning for Distantly Supervised Relation Extraction (2023.findings-emnlp)

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Xin Sun, Qiang Liu, Shu Wu, Zilei Wang, Liang Wang

RLShield: Dynamic Jailbreak Detection for LLMs via Reinforced Adaptive Learning (2026.findings-acl)

Challenge:	Distantly supervised relation extraction (DSRE) methods are not capable of extracting relation labels for individual sentences.
Approach:	They propose a semi-supervised learning relation extraction framework for sentence-level DSRE . they discard only the labels of the noisy samples and utilize them as unlabeled samples .
Outcome:	The proposed framework achieves significant performance enhancements on two real-world datasets.

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Zhao Tong, Pengfei Yang, Yimeng Gu, Haichao Shi, Qiang Liu, Xingcheng Xu, Shu Wu, Xiao-Yu Zhang