Papers by Wenjie Xu

Optimizing Deeper Transformers on Small Datasets (2021.acl-long)

Challenge:	Existing text-to-SQL semantic parsers cannot achieve high accuracy in cross-database setting . TURING is a NLDB system that can be used to democratize data-driven insights for non-technical users .
Approach:	They propose a TURING system that provides high-precision natural language explanations of SQL queries in a beam.
Outcome:	The proposed system achieves 75.1% execution accuracy and 78.3% top-5 beam execution accuracy on the Spider validation set.

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Peng Xu, Dhruv Kumar, Wei Yang, Wenjie Zi, Keyi Tang, Chenyang Huang, Jackie Chi Kit Cheung, Simon J.D. Prince, Yanshuai Cao

Programming Languages in NLP

Challenge:	a common belief that training deep transformers from scratch requires large datasets is wrong . however, with proper initialization and optimization, the benefits of very deep transformer can carry over to challenging tasks with small datasets.
Approach:	They train 48 layers of transformers from pre-trained RoBERTa and 24 relation-aware layers from scratch.
Outcome:	The proposed scheme achieves state-of-the-art performance on a text-to-sql parsing benchmark . it uses 24 fine-tuned layers from pre-trained RoBERTa and 24 relation-aware layers from scratch .

Personalized Generation In Large Model Era: A Survey (2025.acl-long)

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Yiyan Xu, Jinghao Zhang, Alireza Salemi, Xinting Hu, Wenjie Wang, Fuli Feng, Hamed Zamani, Xiangnan He, Tat-Seng Chua

A Streamlined Span-based Factorization Method for Few Shot Named Entity Recognition (2024.lrec-main)

Challenge:	Recent advances in large generative models have catalyzed a paradigm shift in content generation to Personalized Generation (PGen).
Approach:	They propose a multi-level taxonomy that systematically formalizes PGen's key components, core objectives, and abstract workflows.
Outcome:	The proposed taxonomy bridging PGen research across multiple modalities highlights open challenges and promising directions for future exploration.

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Wenjie Xu, Yidan Chen, Jianquan Ouyang

Low-Resource NLP

Challenge:	Existing approaches to few-shot named entity recognition require large amounts of labeled data.
Approach:	They propose a streamlined span-based factorization method that solves few-shot NER problem . they propose to decompose the span-level alignment problem into several refined procedures .
Outcome:	The proposed method achieves an average F1 score improvement of 12 points on the FewNERD dataset and 10 points on SNIPS dataset.

Towards Explainable Temporal Reasoning in Large Language Models: A Structure-Aware Generative Framework (2025.findings-acl)

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Zihao Jiang, Ben Liu, Miao Peng, Wenjie Xu, Yao Xiao, Zhenyan Shan, Min Peng

Contrastive Preference Learning for Neural Machine Translation (2024.findings-naacl)

Challenge:	Existing studies on temporal reasoning models neglect the explainable reasoning processes underlying the results.
Approach:	They propose a structure-aware generative framework that integrates Graph structures with text for Explainable TEmporal Reasoning.
Outcome:	The proposed framework achieves state-of-the-art performance while also demonstrating robust generalization capabilities.

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Jianfei He, Shichao Sun, Sen Peng, Jie Xu, Xiaohua Jia, Wenjie Li

Machine Translation

Challenge:	Existing discrepancies between token-level objective and overall sequence-level quality of a model are causing exposure bias and other issues in NMT.
Approach:	They propose a contrastive preference model that integrates an indicator function to fine-tune a pre-trained model in Neural Machine Translation.
Outcome:	The proposed model outperforms the traditional Plackett-Luce model on three language pairs and also outperFORMs token-level and sequence-level baseline models.

Media Source Matters More Than Content: Unveiling Political Bias in LLM-Generated Citations (2025.emnlp-main)

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Sunhao Dai, Zhanshuo Cao, Wenjie Wang, Liang Pang, Jun Xu, See-Kiong Ng, Tat-Seng Chua

Information Retrieval

Challenge:	generative search engines rely on in-line citations as the key gateway to original webpages . a recent study shows that LLMs tend to cite left-leaning sources at higher rates compared to traditional retrieval systems .
Approach:	They construct a dataset of news articles labeled with left- or right-leaning stances . they find that LLMs tend to cite left-leansing sources at higher rates than traditional retrieval systems .
Outcome:	The proposed dataset shows that LLMs tend to cite left-leaning sources at higher rates than traditional retrieval systems.

SMiLE: Schema-augmented Multi-level Contrastive Learning for Knowledge Graph Link Prediction (2022.findings-emnlp)

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Miao Peng, Ben Liu, Qianqian Xie, Wenjie Xu, Hua Wang, Min Peng

Subtle Errors in Reasoning: Preference Learning via Error-injected Self-editing (2025.acl-long)

Challenge:	Existing methods for linking knowledge graphs lack contextual information in entity neighborhoods, which leads to false prediction results.
Approach:	They propose a Schema-augmented Multi-level contrastive LEarning framework to conduct knowledge graph link prediction using a knowledge graph schema.
Outcome:	The proposed framework is based on a knowledge graph schema and is compared against state-of-the-art datasets.

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Kaishuai Xu, Tiezheng Yu, Wenjun Hou, Yi Cheng, Chak Tou Leong, Liangyou Li, Xin Jiang, Lifeng Shang, Qun Liu, Wenjie Li

ORGAN: Observation-Guided Radiology Report Generation via Tree Reasoning (2023.acl-long)

Challenge:	Existing studies to improve mathematical ability typically involve applying preference learning to step-wise solution pairs, but they overlook critical subtle errors.
Approach:	They propose a preference learning framework that injects predefined subtle errors into pivotal tokens to construct hard pairs for error mitigation.
Outcome:	Extensive experiments show that the proposed framework improves on Qwen2-7B-Instruct and MATH with 4.5K training samples.

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Wenjun Hou, Kaishuai Xu, Yi Cheng, Wenjie Li, Jiang Liu

Enhancing Tool Retrieval with Iterative Feedback from Large Language Models (2024.findings-emnlp)

Challenge:	Existing methods to generate radiology reports only rely on high-level plans, but they lack important information.
Approach:	They propose an Observation-guided radiology Report Generation framework which generates free-text descriptions for a set of radiographs.
Outcome:	The proposed framework outperforms state-of-the-art methods regarding text quality and clinical efficacy.

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Qiancheng Xu, Yongqi Li, Heming Xia, Wenjie Li

Deja vu: Contrastive Historical Modeling with Prefix-tuning for Temporal Knowledge Graph Reasoning (2024.findings-naacl)

Challenge:	Existing methods have shown that large language models can handle a certain amount of tools through in-context learning or fine-tuning.
Approach:	They propose to enhance tool retrieval with iterative feedback from the large language model by prompting the tool usage model to provide feedback for the tool retriever model in multi-round.
Outcome:	The proposed approach achieves advanced performance in both in-domain evaluation and out-of-domain assessment.

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Miao Peng, Ben Liu, Wenjie Xu, Zihao Jiang, Jiahui Zhu, Min Peng

Structured Data in NLP

Challenge:	Existing text-based methods for Temporal Knowledge Graph Reasoning struggle to balance textual knowledge and temporal information with expensive purpose-built training strategies.
Approach:	They propose a Contrastive historical modeling framework with prefix-tuning for TEmporal Reasoning that feeds history-contextualized text into the pseudo-Siamese encoders to strike a textual-temporal balance.
Outcome:	The proposed framework achieves superior performance on four transductive and three few-shot inductive TKGR benchmarks.

How to Ask Good Questions? Try to Leverage Paraphrases (2020.acl-main)

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Xin Jia, Wenjie Zhou, Xu Sun, Yunfang Wu

RECAP: Towards Precise Radiology Report Generation via Dynamic Disease Progression Reasoning (2023.findings-emnlp)

Challenge:	Existing methods to generate human-like questions rely on paraphrases to generate good questions.
Approach:	They propose to integrate paraphrase knowledge into question generation to generate human-like questions by combining paraphrases with a back-translation method.
Outcome:	The proposed model achieves obvious performance gain over several strong baselines and human evaluation validates that it can ask questions of high quality by leveraging paraphrase knowledge.

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Wenjun Hou, Yi Cheng, Kaishuai Xu, Wenjie Li, Jiang Liu

Medical Dialogue Generation via Dual Flow Modeling (2023.findings-acl)

Challenge:	Recent studies have focused on producing concise observations while neglecting the precise attributes that determine the severity of diseases.
Approach:	They propose a model that generates precise radiology reports via dynamic disease progression reasoning by combining historical and spatiotemporal information.
Outcome:	Experiments on two publicly available datasets show the proposed model can generate precise and accurate radiology reports with dynamic disease progression reasoning.

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Kaishuai Xu, Wenjun Hou, Yi Cheng, Jian Wang, Wenjie Li

Dialogue Systems & Conversational Agents

Challenge:	Medical dialogue systems (MDS) aim to provide patients with medical services, such as diagnosis and prescription.
Approach:	They propose a Dual Flow enhanced Medical (DFMed) dialogue generation framework that extracts the medical entities and doctor's dialogue acts used in the dialogue history and models their transitions with an entity-centric graph flow and a sequential act flow.
Outcome:	The proposed framework exceeds baselines in both automatic and manual evaluations on two datasets.

Revisiting the Self-Consistency Challenges in Multi-Choice Question Formats for Large Language Model Evaluation (2024.lrec-main)

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Wenjie Zhou, Qiang Wang, Mingzhou Xu, Ming Chen, Xiangyu Duan

Cross-modality Information Check for Detecting Jailbreaking in Multimodal Large Language Models (2024.findings-emnlp)

Challenge:	Multi-choice questions (MCQs) are a common method for assessing the world knowledge of large language models.
Approach:	They propose three knowledge-equivalent question variants to assess LLMs' world knowledge . they propose option position shuffle, option label replacement, and conversion to a True/False format .
Outcome:	The proposed questions are shuffle, label replacement, and True/False format.

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Yue Xu, Xiuyuan Qi, Zhan Qin, Wenjie Wang

FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition (2021.findings-emnlp)

Challenge:	Multimodal Large Language Models (MLLMs) are susceptible to jailbreak attacks, authors say . multimodal information increases the risk of attacks, but also provides additional data .
Approach:	They propose a jailbreaking detector that detects maliciously perturbed image inputs . cross-modality information detector is designed to detect cross-modal similarity between harmful queries and adversarial images.
Outcome:	a new tool can detect maliciously perturbed image inputs without modification or computation cost.

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Yichong Leng, Xu Tan, Rui Wang, Linchen Zhu, Jin Xu, Wenjie Liu, Linquan Liu, Xiang-Yang Li, Tao Qin, Edward Lin, Tie-Yan Liu

PEToolLLM: Towards Personalized Tool Learning in Large Language Models (2025.findings-acl)

Challenge:	Error correction is widely used in automatic speech recognition (ASR) to post-process the generated sentence.
Approach:	They propose a fast correction model that takes multiple ASR candidates as input for better correction accuracy.
Outcome:	The proposed model can reduce the word error rate (WER) with multiple candidates by 3.2% and 2.6%.

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Qiancheng Xu, Yongqi Li, Heming Xia, Fan Liu, Min Yang, Wenjie Li

A Study of Implicit Ranking Unfairness in Large Language Models (2024.findings-emnlp)

Challenge:	Existing tool learning studies focus on general-purpose tool-use capability, but ignore the importance of personalized tool-user preferences.
Approach:	They propose a framework to adapt Large Language Models to personalized tool learning task, which is trained through supervised fine-tuning and direct preference optimization.
Outcome:	Extensive experiments on PEToolBench show that the proposed framework outperforms existing LLMs in the personalized tool learning task.

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Chen Xu, Wenjie Wang, Yuxin Li, Liang Pang, Jun Xu, Tat-Seng Chua

Controllable LLM Reasoning via Sparse Autoencoder-Based Steering (2026.acl-long)

Challenge:	Large language models (LLMs) have demonstrated superior ability to serve as ranking models, but they will exhibit discriminatory ranking behaviors based on users’ sensitive attributes (gender).
Approach:	They propose an evaluation method to investigate the severity of implicit ranking unfairness and a pair-wise regression method to conduct fair-aware data augmentation for LLM fine-tuning.
Outcome:	The proposed method outperforms existing methods in ranking fairness, achieving this with only a small reduction in accuracy.

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Yi Fang, Wenjie Wang, Mingfeng Xue, Boyi Deng, Fengli Xu, Dayiheng Liu, Fuli Feng

Foresight Optimization for Strategic Reasoning in Large Language Models (2026.acl-long)

Challenge:	Existing methods struggle to control fine-grained reasoning strategies due to conceptual entanglement in LRMs’ hidden states.
Approach:	They propose to decompose strategy-entangled hidden states into a disentangled feature space by using Sparse Autoencoders to identify the few strategy-specific features from the vast pool of SAE features.
Outcome:	The proposed method outperforms existing methods by 15% in control effectiveness.

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Jessie Wang, Jiawen Duan, Jian Wang, Kaitao Song, Chunpu Xu, Johnny K. W. Ho, YU Fenggang, Johan F. Hoorn, Wenjie Li

ParaZh-22M: A Large-Scale Chinese Parabank via Machine Translation (2022.coling-1)

Challenge:	Existing reasoning enhancement methods do not capture foresight in LLMs.
Approach:	They propose to integrate opponent modeling principles into policy optimization to enhance strategic reasoning in LLMs by integrating opponent modeling into policy.
Outcome:	The proposed method outperforms existing reasoning-based LLMs in out-of-domain scenarios and shows that it significantly enhances strategic reasoning across LLM of varying sizes and origins.

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Wenjie Hao, Hongfei Xu, Deyi Xiong, Hongying Zan, Lingling Mu

Defending LLMs against Jailbreak Attacks via Template-Based ICL with a Defensive Suffix (2026.findings-acl)

Challenge:	Paraphrasing is an important data augmentation approach for natural language processing (NLP).
Approach:	They propose to extract sentence-level paraphrases from multiple Chinese translations and construct a larger Chinese parabank with 22M sentence pairs.
Outcome:	The proposed parabank is the largest to date in Chinese, but limited by one-to-many translation data.

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Ruiyang Ni, Changlong Li, Shuaibiao Han, Zhiyu Yi, Perley Xu, Wenjie Ruan

MIND Your Reasoning: A Meta-Cognitive Intuitive-Reflective Network for Dual-Reasoning in Multimodal Stance Detection (2026.acl-long)

Challenge:	State-of-the-art large language models (LLMs) are vulnerable to jailbreak attacks, such as GCG and AutoDAN.
Approach:	They propose to take the advances of online In-Context Learning and an offline defensive suffix and optimize them using an iterative algorithm and an online stochastic random search to identify the most effective ICL demonstrations.
Outcome:	The proposed method reduces attack success rate to nearly 0% while maintaining the model’s utility on benign tasks and incurring only negligible* computational overhead.

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Bingbing Wang, Zhengda Jin, Bin Liang, Wenjie Li, Jing Li, Ruifeng Xu, Min Zhang

Sentiment Analysis

Challenge:	Existing methods operate by learning to fuse modalities, leading to frequent misjudgments.
Approach:	They propose a paradigm shift from learning to fuse to *learning the reason's process' inspired by the dual-process theory of human cognition, MIND operationalizes a self-improving loop.
Outcome:	The proposed model significantly outperforms baseline models and exhibits strong generalization.

Retrieval-Augmented Language Models are Mimetic Theorem Provers (2025.findings-emnlp)

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Wenjie Yang, Ruiyuan Huang, Jiaxing Guo, Zicheng Lyu, Tongshan Xu, Shengzhong Zhang, Lun Du, Da Zheng, Zengfeng Huang

A Federated Framework for LLM-based Recommendation (2025.findings-naacl)

Challenge:	Large language models often fail to provide rigorous proof-based reasoning for research-level mathematics.
Approach:	They propose a simple yet effective RAG framework that augments retrieved proofs with queries and document contexts to improve retrieval performance.
Outcome:	The proposed framework improves retrieval performance by 34.19% . dual RAG can be used to prove research-level theorems in theoretical machine learning .

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Jujia Zhao, Wenjie Wang, Chen Xu, See-Kiong Ng, Tat-Seng Chua

OpenResearcher: Unleashing AI for Accelerated Scientific Research (2024.emnlp-demo)

Challenge:	Large Language Models (LLMs) have demonstrated potential in building generative recommendation systems through fine-tuning user behavior data.
Approach:	They propose a federated framework for LLM-based recommendation that combines dynamic parameter aggregation and learning speed for different clients.
Outcome:	The proposed framework achieves a more balanced client performance and improved overall performance in a computational and storage-efficient way while safeguarding user privacy well.

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Yuxiang Zheng, Shichao Sun, Lin Qiu, Dongyu Ru, Cheng Jiayang, Xuefeng Li, Jifan Lin, Binjie Wang, Yun Luo, Renjie Pan, Yang Xu, Qingkai Min, Zizhao Zhang, Yiwen Wang, Wenjie Li, Pengfei Liu

Information Retrieval

Challenge:	Global scientific publications are growing annually by about 4%-5% (Pinedo et al., 2024).
Approach:	They introduce an AI-assisted platform that answers diverse questions from researchers using Retrieval-Augmented Generation (RAG) they develop various tools to understand queries, search from the scientific literature, filter retrieved information, provide accurate and comprehensive answers, and self-refine answers.
Outcome:	OpenResearcher is built on Retrieval-Augmented Generation (RAG) to integrate Large Language Models (LLMs) with up-to-date, domain-specific knowledge.

KBioXLM: A Knowledge-anchored Biomedical Multilingual Pretrained Language Model (2023.findings-emnlp)

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Lei Geng, Xu Yan, Ziqiang Cao, Juntao Li, Wenjie Li, Sujian Li, Xinjie Zhou, Yang Yang, Jun Zhang

Multilinguality

Challenge:	Existing models for multilingual biomedical training are monolingual, resulting in limited cross-lingual capability.
Approach:	They propose a model that transforms a multilingual biomedical corpus into a biomedically domain using a knowledge-anchored approach.
Outcome:	The proposed model outperforms monolingual and multilingual models in cross-lingual scenarios.

ICON: Improving Inter-Report Consistency in Radiology Report Generation via Lesion-aware Mixup Augmentation (2024.findings-emnlp)

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Wenjun Hou, Yi Cheng, Kaishuai Xu, Yan Hu, Wenjie Li, Jiang Liu

Muffin: Mitigating Unhelpfulness in Emotional Support Conversations with Multifaceted AI Feedback (2024.findings-acl)

Challenge:	Existing approaches to radiology report generation lack inter-report consistency, exhibiting biases towards common patterns and susceptibility to lesion variants.
Approach:	They propose a method which improves the inter-report consistency of radiology report generation by extracting lesions from input images and examining their characteristics.
Outcome:	The proposed system captures similarities in semantically equivalent lesions and can be used to generate reports for two semantically identical cases.

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Jiashuo Wang, Chunpu Xu, Chak Tou Leong, Wenjie Li, Jing Li

Dialogue Systems & Conversational Agents

Challenge:	Existing studies have shown that emotional support conversation models generate unhelpful responses that can hinder their effectiveness.
Approach:	They propose a model-agnostic framework called Mitigating unhelpfulness with multifaceted AI feedback for emot io nal support (Muffin) it uses a multifaceted feedback module to assess helpfulness model responses across various facets of emotional support and contrasts helpful and unhelpful responses generated by the model.
Outcome:	The proposed framework reduces the likelihood of unhelpful responses by comparing helpful and unhelpfully responses generated by previous models to improve response fluency and relevance.

MMAPG: A Training-Free Framework for Multimodal Multi-hop Question Answering via Adaptive Planning Graphs (2025.emnlp-main)

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Yiheng Hu, Xiaoyang Wang, Qing Liu, Xiwei Xu, Qian Fu, Wenjie Zhang, Liming Zhu

Query and Output: Generating Words by Querying Distributed Word Representations for Paraphrase Generation (N18-1)

Challenge:	Existing multimodal question answering models rely on sequential retrieval and reasoning, but this single-path paradigm makes them vulnerable to errors due to misleading intermediate steps.
Approach:	They propose a multimodal multi-hop question answering framework guided by an Adaptive Planning Graph . they propose modality-specific strategies that dynamically adapt to distinct data types .
Outcome:	The proposed framework outperforms existing models that rely on training.

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Shuming Ma, Xu Sun, Wei Li, Sujian Li, Wenjie Li, Xuancheng Ren

RAR2: Retrieval-Augmented Medical Reasoning via Thought-Driven Retrieval (2025.findings-emnlp)

Challenge:	Existing models tend to memorize words instead of learning meaning of words . existing models tend not to model semantic information, resulting in incorrect sentences .
Approach:	They propose a novel model that generates words by querying distributed word representations . they evaluate model on two paraphrase-oriented tasks, namely text simplification and short abstractive summarization .
Outcome:	The proposed model outperforms the baseline model on two paraphrase-oriented tasks . it achieves state-of-the-art performance on these benchmark datasets .

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Kaishuai Xu, Wenjun Hou, Yi Cheng, Wenjie Li

KNN-SSD: Enabling Dynamic Self-Speculative Decoding via Nearest Neighbor Layer Set Optimization (2026.findings-eacl)

Challenge:	Existing methods focus on refining queries without modeling the reasoning process, limiting their ability to retrieve and integrate clinically relevant knowledge.
Approach:	They propose a joint learning framework that improves Reasoning-Augmented Retrieval and Retri-Agmented Reasoning.
Outcome:	The proposed model outperforms RAG baselines on biomedical question answering datasets.

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Mingbo Song, Heming Xia, Jun Zhang, Chak Tou Leong, Qiancheng Xu, Wenjie Li, Sujian Li

Challenge:	Large language models (LLMs) have proven highly capable in handling downstream tasks, but the token-by-token generation in autoregressive decoding results in quadratic computational complexity.
Approach:	They propose a method that proposes skipping certain layers to construct a draft model, which eliminates the need for additional parameters or training.
Outcome:	The proposed method achieves 1.31.6 speedup in LLM inference while being sensitive to domain shifts.

Towards Dynamic Theory of Mind: Evaluating LLM Adaptation to Temporal Evolution of Human States (2025.acl-long)

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Yang Xiao, Jiashuo Wang, Qiancheng Xu, Changhe Song, Chunpu Xu, Yi Cheng, Wenjie Li, Pengfei Liu

Linguistics & Cognitive NLP

Challenge:	Existing benchmarks assess basic Theory of Mind abilities but neglect temporal evolution of mental states in real-world social contexts.
Approach:	They propose a benchmark specifically designed to evaluate Large Language Models' ability to understand and track the temporal progression of mental states across interconnected scenarios.
Outcome:	The proposed benchmarks underperform humans by 44.7% and show that they can model the dynamic nature of human mental states better than existing models.

LinkPrompt: Natural and Universal Adversarial Attacks on Prompt-based Language Models (2024.naacl-long)

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Yue Xu, Wenjie Wang

The Evolution of Thought: Tracking LLM Overthinking via Reasoning Dynamics Analysis (2026.acl-long)

Challenge:	Prompt-based learning is a new language model training paradigm that adapts Pre-trained Language Models (PLMs) to downstream tasks.
Approach:	They propose a prompt-based learning paradigm that adapts Pre-trained Language Models to downstream tasks . they use a gradient-based beam search algorithm to generate adversarial triggers .
Outcome:	The proposed model improves performance on various natural language processing tasks by optimizing the prompt template.

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Zihao Wei, Liang Pang, Jiahao Liu, Wenjie Shi, Jingcheng Deng, Shicheng Xu, Zenghao Duan, Jingang Wang, Fei Sun, Huawei Shen, Xueqi Cheng

Learning to Align Multi-Faceted Evaluation: A Unified and Robust Framework (2025.findings-acl)

Challenge:	Explicit reasoning trajectories increase performance but often trigger overthinking . despite its importance, this study examines how each step of reasoning affects the final outcome .
Approach:	They propose a Reasoning Completion Point Detector that detects the RCP by monitoring rank dynamics of termination tokens.
Outcome:	The proposed method reduces token usage by up to 44% while preserving accuracy.

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Kaishuai Xu, Tiezheng Yu, Yi Cheng, Wenjun Hou, Liangyou Li, Xin Jiang, Lifeng Shang, Qun Liu, Wenjie Li

Robustness in NLP

Challenge:	Existing methods for fine-tuning open-source LLMs are limited to text-based analysis under predefined general criteria.
Approach:	They propose a framework that fine-tunes LLMs to replicate the evaluation explanations and judgments of proprietary models.
Outcome:	The proposed evaluation framework outperforms existing fine-tuned evaluation methods in effectiveness and robustness.

Pre-trained Language Model with Prompts for Temporal Knowledge Graph Completion (2023.findings-acl)

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Wenjie Xu, Ben Liu, Miao Peng, Xu Jia, Min Peng

TInR: Exploring Tool-Internalized Reasoning in Large Language Models (2026.acl-long)

Challenge:	Existing methods for temporal knowledge graph completion (TKGC) focus on extracting information from timestamps and insufficiently utilizing implied information in relations.
Approach:	They propose a temporal knowledge graph completion model with prompts that converts quadruples into pre-trained language inputs and prompts to make coherent sentences with implicit semantic information.
Outcome:	The proposed model can make coherent sentences with implicit semantic information.

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Qiancheng Xu, Yongqi Li, Fan Liu, Hongru Wang, Min Yang, Wenjie Li

Mask the Correct Tokens: An Embarrassingly Simple Approach for Error Correction (2022.emnlp-main)

Challenge:	Existing methods rely on external tool documentation during reasoning, leading to tool mastery difficulty, tool size constraints, and inference inefficiency.
Approach:	They propose a tool-internalized reasoning framework for unified reasoning and tool usage that integrates external tools into Large Language Models (LLMs) to address these issues, they propose 'tool-internet-based' reasoning.
Outcome:	The proposed method achieves superior performance across in-domain and out-of-domain settings, highlighting its effectiveness and efficiency.

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Kai Shen, Yichong Leng, Xu Tan, Siliang Tang, Yuan Zhang, Wenjie Liu, Edward Lin

Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach (P18-1)

Challenge:	Text error correction methods usually use the source (incorrect) sentence as encoder input and generate the target (correct) sentences through the decoder.
Approach:	They propose a method to correct errors in text sequences by randomly masking out the correct tokens in the source sentence.
Outcome:	The proposed method improves accuracy on Mandarin and English datasets with autoregressive and non-autoregressive generation models.

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Jingjing Xu, Xu Sun, Qi Zeng, Xiaodong Zhang, Xuancheng Ren, Houfeng Wang, Wenjie Li

Machine Translation

Challenge:	Existing studies for sentiment-to-sentiment "translation" only change the underlying sentiment and fail to keep the semantic content.
Approach:	They propose a cycled reinforcement learning method that combines neutralization module and emotionalization module.
Outcome:	The proposed method outperforms state-of-the-art systems on Yelp and Amazon review datasets.

Variational Autoregressive Decoder for Neural Response Generation (D18-1)

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Jiachen Du, Wenjie Li, Yulan He, Ruifeng Xu, Lidong Bing, Xuan Wang

HydraRAG: Structured Cross-Source Enhanced Large Language Model Reasoning (2025.emnlp-main)

Challenge:	Existing variational Bayesian models generate responses from a single latent variable, which is not sufficient to model high variability in responses.
Approach:	They propose a conditional variable auto-encoder that sequentially introduces latent variables to condition the generation of each word in the response sequence.
Outcome:	Empirical results show that the proposed model improves on state-of-the-art models on Opensubtitle and Reddit datasets.

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Xingyu Tan, Xiaoyang Wang, Qing Liu, Xiwei Xu, Xin Yuan, Liming Zhu, Wenjie Zhang

AgencyBench: Benchmarking the Frontiers of Autonomous Agents in 1M-Token Real-World Contexts (2026.acl-long)

Challenge:	Current RAG system retrieves evidence from knowledge graphs and text documents but has limitations in multi-hop reasoning, multi-entity questions, and source verification.
Approach:	They propose a training-free framework that unifies graph topology, document semantics, and source reliability to support deep, faithful reasoning in large language models.
Outcome:	The proposed framework outperforms the current hybrid model-based model-driven system by 20.3% and 30.1% on seven benchmark datasets.

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Keyu Li, Junhao Shi, Yang Xiao, Mohan Jiang, Jie Sun, Yunze Wu, Dayuan Fu, Shijie Xia, Xiaojie Cai, Tianze Xu, Weiye Si, Wenjie Li, Dequan Wang, Pengfei Liu

Challenge:	Existing benchmarks focus on single agentic capability, failing to capture long-horizon real-world scenarios.
Approach:	They propose a benchmark that evaluates 6 agentic capabilities across 32 real-world scenarios.
Outcome:	Experiments show that closed-source models outperform open-source model (48.4% vs 32.1%) integrating models with advanced scaffolds to form autonomous agents is a paradigm shift.

Multi-Modal Sarcasm Detection via Cross-Modal Graph Convolutional Network (2022.acl-long)

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Bin Liang, Chenwei Lou, Xiang Li, Min Yang, Lin Gui, Yulan He, Wenjie Pei, Ruifeng Xu

RADAR: Enhancing Radiology Report Generation with Supplementary Knowledge Injection (2025.acl-long)

Challenge:	Existing studies on multimodal sarcasm detection using textual and visual information have been limited to text-only approaches.
Approach:	They propose to construct a cross-modal graph for each multi-modal instance to explicitly draw the ironic relations between textual and visual modalities.
Outcome:	The proposed model achieves state-of-the-art in multi-modal sarcasm detection.

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Wenjun Hou, Yi Cheng, Kaishuai Xu, Heng Li, Yan Hu, Wenjie Li, Jiang Liu