Papers by Jian Tang

Evidence-Aware Inferential Text Generation with Vector Quantised Variational AutoEncoder (2020.acl-main)

Challenge:	recurrent neural networks struggle to match the performance of Transformers due to limitations in parallelization and scalability.
Approach:	They propose a model architecture that combines the efficient parallelizable training of transformers with the efficient inference of RNNs.
Outcome:	The proposed model performs on par with similarly sized RNNs, suggesting future work can leverage this architecture to create more efficient models.

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Daya Guo, Duyu Tang, Nan Duan, Jian Yin, Daxin Jiang, Ming Zhou

Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering (2022.acl-long)

Challenge:	Existing approaches for inferential text generation ignore context that is not explicitly provided . Existing models ignore background knowledge that provides crucial evidence to generate inferences .
Approach:	They propose an approach that automatically finds evidence for an event from a large text corpus and leverages it to guide the generation of inferential texts.
Outcome:	The proposed model generates inferential texts from a large text corpus and uses evidence to guide it.

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Jing Zhang, Xiaokang Zhang, Jifan Yu, Jian Tang, Jie Tang, Cuiping Li, Hong Chen

UnCo: Uncertainty-Driven Collaborative Framework of Large and Small Models for Grounded Multimodal NER (2025.emnlp-main)

Challenge:	Existing retrieval methods for knowledge base question answering are either heuristic or interwoven with the reasoning, causing reasoning on the partial subgraphs.
Approach:	They propose a subgraph retrieval framework that decouples the retrieval from the subsequent reasoning process and trains subgraphs for easier reasoning.
Outcome:	The proposed framework improves retrieval and QA performance over existing methods.

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Jielong Tang, Yang Yang, Jianxing Yu, Zhen-Xing Wang, Haoyuan Liang, Liang Yao, Jian Yin

LoopCoder: Scaling Code Intelligence via Looped Language Models (2026.findings-acl)

Challenge:	Existing methods to identify unseen multimodal entities struggle with limited knowledge and generalization.
Approach:	They propose a framework that leverages the strengths of small fine-tuned models and MLLMs to generate unambiguous predictions.
Outcome:	Extensive experiments show that the proposed framework retains the in-domain knowledge of small models while utilizing the capabilities of MLLMs to handle unseen entities.

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Jian Yang, Wei Zhang, Shuyue Guo, Yizhi LI, Linzheng Chai, Zhengmao Ye, Shukai Liu, Yuyang Song, Jiajun Wu, Che Liu, Tianyu Zheng, Siwei Wu, Leo L, Xudong Ma, Chuan Hao, Ran Tao, Yan Xing, Jianzhou Wang, Mingjie Tang, Aishan Liu, Zhoujun Li, Xianglong Liu, Weifeng Lv, Bryan Dai

Question Generation from SQL Queries Improves Neural Semantic Parsing (D18-1)

Challenge:	Large language models have mastered syntax-level code generation, but complex algorithmic reasoning remains a challenge.
Approach:	They propose a recurrent inductive bias that aligns with the recursive nature of programming logic.
Outcome:	The proposed model achieves comparable performance to standard dense models with more parameters.

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Daya Guo, Yibo Sun, Duyu Tang, Nan Duan, Jian Yin, Hong Chi, James Cao, Peng Chen, Ming Zhou

Text Generation

Challenge:	Using question generation, we learn a semantic parser with 30% of the supervised training data.
Approach:	They propose to use question generation to learn a semantic parser with less supervised training data.
Outcome:	The proposed method improves the state-of-the-art model with less training data.

Generating then Refining for Reliable Knowledge Base Question Answering (2026.acl-long)

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Jianqi Gao, Hang Yu, Jian Cao, Ranran Bu, Jinghua Tang, Nengjun Zhu, Yonggang Zhang

Towards Bridging the Reward-Generation Gap in Direct Alignment Algorithms (2026.findings-acl)

Challenge:	Existing knowledge base question answering methods generate LFs that are non-executable due to semantic hallucination issue of large language models.
Approach:	They propose a "generate-verify-refine" framework for reliable LF generation . they propose ARI-KBQA to generate query paths based on hop-by-hop reasoning .
Outcome:	The proposed framework significantly improves model performance with a reduced search space . ARI-KBQA can generate LFs that are non-executable due to semantic hallucination issue .

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Zeguan Xiao, Yun Chen, Jian Yang, Guanhua Chen, Ke Tang

Challenge:	Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks, including instruction following, mathematical problem solving, and coding generation.
Approach:	They propose a method that truncates both preferred and dispreferred responses to match the shorter one’s length.
Outcome:	The proposed approach improves over standard implementations and achieves 11.8 points in AlpacaEval 2 and overall improvements across downstream tasks.

KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation (2021.tacl-1)

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Xiaozhi Wang, Tianyu Gao, Zhaocheng Zhu, Zhengyan Zhang, Zhiyuan Liu, Juanzi Li, Jian Tang

HSCR: Hierarchical Self-Contrastive Rewarding for Aligning Medical Vision Language Models (2025.acl-long)

Challenge:	Existing language representation models (PLMs) cannot capture factual knowledge from text.
Approach:	They propose a unified model for Knowledge Embedding and Pre-trained LanguagERepresentation which integrates factual knowledge into PLMs and produces effective text-enhanced KE with the strong PLM.
Outcome:	The proposed model improves on existing pre-trained language representation models and improves their performance on various NLP tasks.

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Songtao Jiang, Yan Zhang, Yeying Jin, Zhihang Tang, Yangyang Wu, Yang Feng, Jian Wu, Zuozhu Liu

AOT*: Efficient Synthesis Planning via LLM-Empowered AND-OR Tree Search (2026.findings-acl)

Challenge:	Existing methods for medical vision-language models overlook modality misalignment . HSCR generates high-quality preference data with higher sampling probability .
Approach:	They propose a hierarchical self-contrastive reward approach that addresses two challenges in alignment . they leverage the inherent capability of Med-VLMs to generate dispreferred responses .
Outcome:	The proposed approach improves accuracy and trustworthiness of medical vision-label models with 2,000 training entries.

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Xiaozhuang Song, Xuanhao Pan, Xinjian Zhao, Hangting Ye, Shufei Zhang, Jian Tang, Tianshu Yu

Green & Sustainable NLP

Challenge:	Experimental evaluation shows that AOT* achieves competitive solve rates using 3-5 fewer iterations than existing LLM-based approaches.
Approach:	They propose a framework that integrates LLM-generated chemical synthesis pathways with systematic AND-OR tree search.
Outcome:	Experimental results show that AOT* improves search efficiency and solves faster than existing approaches.

MBA-RAG: a Bandit Approach for Adaptive Retrieval-Augmented Generation through Question Complexity (2025.coling-main)

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Xiaqiang Tang, Qiang Gao, Jian Li, Nan Du, Qi Li, Sihong Xie

Green & Sustainable NLP

Challenge:	Existing RAG frameworks either indiscriminately perform retrieval or rely on rigid single-label classifiers to select retrieval methods.
Approach:	They propose a framework that dynamically selects the most suitable retrieval strategy based on query complexity.
Outcome:	The proposed framework achieves state-of-the-art results on multiple single-hop and multi-hop datasets while reducing retrieval costs.

RPC-Bench: A Fine-grained Benchmark for Research Paper Comprehension (2026.acl-long)

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Yelin Chen, Fanjin Zhang, Suping Sun, Yunhe Pang, Yuanchun Wang, Jian Song, XiaoYan Li, Lei Hou, Shu Zhao, Jie Tang, Juanzi Li

Challenge:	Existing benchmarks for understanding research papers offer limited fine-grained evaluation at scale.
Approach:	They propose a large-scale question-answering benchmark built from review–rebuttal exchanges of high-quality computer science papers.
Outcome:	The proposed model is based on human-verified QA pairs and contains 15K questions.

Certified Robustness to Word Substitution Attack with Differential Privacy (2021.naacl-main)

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Wenjie Wang, Pengfei Tang, Jian Lou, Li Xiong

Dissecting Failure Dynamics in Large Language Model Reasoning (2026.acl-long)

Challenge:	Recent studies have shown that adversarial examples can be easily fooled by DNNs, making the robustness and security of NLP models significantly important.
Approach:	They propose a differential privacy-based algorithm to achieve certified robustness against word substitution at- tacks in text classification via differential privacy.
Outcome:	The proposed model achieves higher accuracy and more than 30X efficiency improvement over existing defense algorithms.

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Wei Zhu, Jian Zhang, Lixing Yu, Kun Yue, Zhiwen Tang

Visual Attention Reasoning via Hierarchical Search and Self-Verification (2026.acl-long)

Challenge:	Large Language Models achieve strong performance through extended inference-time deliberation, yet how their reasoning failures arise remains poorly understood.
Approach:	They propose a framework that probes and redirects critical transitions using uncertainty signals.
Outcome:	Empirical evaluations show that GUARD improves reasoning performance . GUard probes critical transitions and redirects them using uncertainty signals .

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Wei Cai, Jian Zhao, Yuchen Yuan, Tianle Zhang, Ming Zhu, Haichuan Tang, Xuelong Li

Reasoning Over Semantic-Level Graph for Fact Checking (2020.acl-main)

Challenge:	Multimodal Large Language Models (MLLMs) often hallucinate due to fragile, linear reasoning and weak visual grounding.
Approach:	They propose a framework that reformulates reasoning as a hierarchical search with self-verification and replaces linear Chain-of-Thought with a tree-search policy capable of backtracking to correct logical errors.
Outcome:	The proposed framework outperforms state-of-the-art methods on hallucination and safety benchmarks.

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Wanjun Zhong, Jingjing Xu, Duyu Tang, Zenan Xu, Nan Duan, Ming Zhou, Jiahai Wang, Jian Yin

Reasoning

Challenge:	Existing methods for fact checking use string concatenation or fusing features of isolated evidence sentences.
Approach:	They propose a method suitable for reasoning about the semantic-level structure of evidence . they use graph convolutional network and graph attention network to exploit the structure .
Outcome:	The proposed method improves claim verification accuracy and FEVER score on a benchmark dataset.

FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding (2022.acl-long)

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Yanan Zheng, Jing Zhou, Yujie Qian, Ming Ding, Chonghua Liao, Li Jian, Ruslan Salakhutdinov, Jie Tang, Sebastian Ruder, Zhilin Yang

CogniBench: A Legal-inspired Framework and Dataset for Assessing Cognitive Faithfulness of Large Language Models (2025.acl-long)

Challenge:	Existing evaluation protocols for few-shot natural language understanding (NLU) tasks are inconsistent and hinder fair comparison and measuring progress.
Approach:	They propose an evaluation framework that improves previous evaluation procedures in three key aspects, i.e., test performance, dev-test correlation, and stability.
Outcome:	The proposed framework improves evaluation procedures in three key aspects, i.e., performance, dev-test correlation, and stability.

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Xiaqiang Tang, Jian Li, Keyu Hu, Nan Du, Xiaolong Li, Xi Zhang, Weigao Sun, Sihong Xie

Coupling Retrieval and Meta-Learning for Context-Dependent Semantic Parsing (P19-1)

Challenge:	Existing benchmarks focus on “factual statements” that rephrase source materials, but ignore “cognitive statements” . evaluating and detecting "faithfulness hallucinations" remains challenging .
Approach:	They propose a framework to assess faithfulness of cognitive statements and introduce a dataset to scale easily across models.
Outcome:	The proposed framework assesses faithfulness of cognitive statements and scales easily across models.

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Daya Guo, Duyu Tang, Nan Duan, Ming Zhou, Jian Yin

Learning Low-Resource End-To-End Goal-Oriented Dialog for Fast and Reliable System Deployment (2020.acl-main)

Challenge:	a context-aware retrieval model and a meta-learning paradigm are used for context-dependent semantic parsing .
Approach:	They propose a retrieval model and a meta-learner to incorporate retrieved datapoints as context-dependent semantic parsing evidence.
Outcome:	The proposed approach performs better than retrieve-and-edit baselines on CONCODE and CSQA datasets.

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Yinpei Dai, Hangyu Li, Chengguang Tang, Yongbin Li, Jian Sun, Xiaodan Zhu

Dialogue Systems & Conversational Agents

Challenge:	Existing end-to-end dialog systems perform less effectively when data is scarce.
Approach:	They propose a Meta-Dialog System which combines meta-learning and human-machine collaboration to improve dialog learning by a new extended-bAbI dataset and a transformed MultiWOZ dataset.
Outcome:	The proposed system outperforms non-meta-learning baselines on a new extended-bAbI dataset and a transformed MultiWOZ dataset for low-resource goal-oriented dialog learning.

Analytical Reasoning of Text (2022.findings-naacl)

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Wanjun Zhong, Siyuan Wang, Duyu Tang, Zenan Xu, Daya Guo, Yining Chen, Jiahai Wang, Jian Yin, Ming Zhou, Nan Duan

Reasoning

Challenge:	Existing models with implicit reasoning ability struggle to solve analytical reasoning of text.
Approach:	They propose an approach to analyze text and use it to perform reasoning over it.
Outcome:	The proposed approach outperforms pre-trained models on an analysis of the Law School Admission Test dataset.

Neural Deepfake Detection with Factual Structure of Text (2020.emnlp-main)

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Wanjun Zhong, Duyu Tang, Zenan Xu, Ruize Wang, Nan Duan, Ming Zhou, Jiahai Wang, Jian Yin

Structured Data in NLP

Challenge:	Existing approaches to deepfake detection typically represent documents with coarse-grained representations, but they struggle to capture factual structures of documents.
Approach:	They propose a graph-based model that captures factual structures of documents for deepfake detection.
Outcome:	The proposed model improves strong base models built with RoBERTa on two public deepfake datasets.

LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Network (2020.acl-main)

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Wanjun Zhong, Duyu Tang, Zhangyin Feng, Nan Duan, Ming Zhou, Ming Gong, Linjun Shou, Daxin Jiang, Jiahai Wang, Jian Yin

OAgents: An Empirical Study of Building Effective Agents (2025.findings-emnlp)

Challenge:	Existing methods for fact checking textual statements are not yet available.
Approach:	They propose a neural network approach capable of leveraging logical operations for fact checking . they use a textual statement and semi-structured tables to generate a program from it .
Outcome:	The proposed approach achieves state-of-the-art performance on TABFACT dataset . it derives a program (a.k.a. logical form) of the statement in semantic parsing manner .

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He Zhu, Tianrui Qin, King Zhu, Heyuan Huang, Yeyi Guan, Jinxiang Xia, Hanhao Li, Yi Yao, Ningning Wang, Pai Liu, Tianhao Peng, Xin Gui, Li Xiaowan, Yuhui Liu, Xiangru Tang, Jian Yang, Ge Zhang, Xitong Gao, Yuchen Eleanor Jiang, Changwang Zhang, Jun Wang, Jiaheng Liu, Wangchunshu Zhou

Dual Activation-Weight Sparsity: A Training-Free Framework for Efficient Large Language Model Compression (2026.acl-long)

Challenge:	a recent study shows that agent research practices are far from standard, rigorous . lack of a standard evaluation protocol makes previous works not reproducible, authors say .
Approach:	They conduct an empirical study on the GAIA benchmark to investigate agent design choices . they find that lack of a standard evaluation protocol makes previous works not reproducible .
Outcome:	The proposed framework achieves state-of-the-art performance among open-source projects.

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Luoyang Sun, Guangyan Li, Cheng Deng, Haifeng Zhang, Jian Zhao, Yongqiang Tang, Wensheng Zhang, Jun Wang

CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark (2022.acl-long)

Challenge:	Large language models (LLMs) excel at natural language tasks but face deployment bottlenecks due to computational demands.
Approach:	They propose a training-free framework that exploits activation and weight sparsity . they use a three-tier routing strategy that uses magnitude-based pruning .
Outcome:	Experiments on Llama and Mistral models show that DAWS outperforms activation-weight sparsity pruning methods.

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Ningyu Zhang, Mosha Chen, Zhen Bi, Xiaozhuan Liang, Lei Li, Xin Shang, Kangping Yin, Chuanqi Tan, Jian Xu, Fei Huang, Luo Si, Yuan Ni, Guotong Xie, Zhifang Sui, Baobao Chang, Hui Zong, Zheng Yuan, Linfeng Li, Jun Yan, Hongying Zan, Kunli Zhang, Buzhou Tang, Qingcai Chen

Challenge:	a new benchmark for biomedical language understanding is being developed in Chinese . most benchmarks are limited to English, which makes it difficult to replicate success in other languages.
Approach:	They propose to use Chinese biomedical language understanding evaluation benchmarks to evaluate Chinese models.
Outcome:	The proposed benchmarks show that the current models perform worse than the human ceiling.

AI for Science in the Era of Large Language Models (2024.emnlp-tutorials)

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Zhenyu Bi, Minghao Xu, Jian Tang, Xuan Wang

DrDiff: Dynamic Routing Diffusion with Hierarchical Attention for Breaking the Efficiency-Quality Trade-off (2025.emnlp-main)

Challenge:	Recent advances in large language models (LLMs) have demonstrated significant prowess in tasks involving natural language, such as translating languages, constructing chatbots, and answering questions.
Approach:	This tutorial explores the application of large language models to three crucial categories of scientific data: 1) textual data, 2) biomedical sequences, and 3) brain signals.
Outcome:	This tutorial will explore the application of large language models to three crucial categories of scientific data.

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Jusheng Zhang, Yijia Fan, Kaitong Cai, Zimeng Huang, Xiaofei Sun, Jian Wang, Chengpei Tang, Keze Wang

Green & Sustainable NLP

Challenge:	et al., 2019; Brown e.t al, 2023; Touvron e t al; 2024; OpenAI, 2024) Large Language Models (LLMs) have demonstrated remarkable capabilities in knowledge encoding and contextual understanding during their pretraining phase.
Approach:	They propose a dynamic expert scheduling mechanism that allocates computational resources based on text complexity and a hierarchical sparse attention mechanism that adjusts attention patterns according to a variety of input lengths.
Outcome:	The proposed framework overpowers existing methods on long-text generation benchmarks.

Answering Complex Geographic Questions by Adaptive Reasoning with Visual Context and External Commonsense Knowledge (2025.acl-long)

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Fan Li, Jianxing Yu, Jielong Tang, Wenqing Chen, Hanjiang Lai, Yanghui Rao, Jian Yin

Reasoning

Challenge:	a new task of answering geographic reasoning questions based on the given image is proposed . the task requires identifying the objects in the image and understanding the background context .
Approach:	They propose a task of answering geographic reasoning questions based on the given image . they analyze the image and describe its fine-grained content by text and keywords .
Outcome:	The proposed method can be used to answer geographic reasoning questions based on an image . it can be applied to a large-scale dataset with 41,329 samples .

FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning (2022.acl-long)

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Jing Zhou, Yanan Zheng, Jie Tang, Li Jian, Zhilin Yang