Papers by Jie Tang

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

Challenge:	OpenWebAgent integrates large language models and large multimodal models to improve web automation.
Approach:	They propose to integrate large language models and large multimodal models into an open toolkit to optimize web automation.
Outcome:	The open toolkit integrates both large language models (LLMs) and large multimodal models (LMMs) it enables the development of powerful, task-oriented web agents, significantly enhancing user experience and operational efficiency on the web.

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

Are Intermediate Layers and Labels Really Necessary? A General Language Model Distillation Method (2023.findings-acl)

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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Shicheng Tan, Weng Lam Tam, Yuanchun Wang, Wenwen Gong, Shu Zhao, Peng Zhang, Jie Tang

Zero-Shot Information Extraction as a Unified Text-to-Triple Translation (2021.emnlp-main)

Challenge:	Existing knowledge distillation methods rely on intermediate layer features and golden labels, which require aligned model architecture and labeled data respectively.
Approach:	They propose a general language model distillation method that performs two-stage word prediction distillation and vocabulary compression, which is simple and shows extremely strong performance.
Outcome:	The proposed method outperforms 25 state-of-the-art methods on the SuperGLUE benchmark, achieving an average score that surpasses the best method by 3%.

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Chenguang Wang, Xiao Liu, Zui Chen, Haoyun Hong, Jie Tang, Dawn Song

In-the-wild Audio Spatialization with Flexible Text-guided Localization (2025.acl-long)

Challenge:	a number of information extraction tasks require task-specific training.
Approach:	They propose a text-to-triple translation framework for information extraction tasks . they propose enabling task-agnostic translation by leveraging latent knowledge of a pre-trained language model .
Outcome:	The proposed framework outperforms the existing methods on open information extraction tasks.

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Tianrui Pan, Jie Liu, Zewen Huang, Jie Tang, Gangshan Wu

KARL: Reinforcement Learning for LLM Agents on Multi-Turn Knowledge-Intensive Agentic Tasks (2026.acl-long)

Challenge:	Existing methods for mapping monaural audio to binaural signals lack flexibility and interactive control needed in complex multi-object user-interactive environments.
Approach:	They propose a text-guided audio spatialization framework that utilizes diverse text prompts to evaluate binaural audio models.
Outcome:	The proposed framework learns binaural differences guided by 3D spatial location and relative position prompts, enhanced with flipped-channel audio.

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Xueqiao Sun, Xiao Liu, Bowen Lv, Hanchen Zhang, Bohao Jing, Zehan Qi, Yifan Xu, Yuxiao Dong, Jie Tang

Revisiting Parallel Context Windows: A Frustratingly Simple Alternative and Chain-of-Thought Deterioration (2024.findings-acl)

Challenge:	Large Language Models have shown remarkable potential as autonomous agents, but their effectiveness in knowledge-intensive tasks remains limited by passive knowledge utilization.
Approach:	They propose a framework that enables LLM agents to dynamically explore structured knowledge sources through multi-turn interactions.
Outcome:	The proposed framework outperforms existing retrieval-augmented approaches on knowledge graph and database tasks while maximizing tool-use behaviors end-to-end.

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Kejuan Yang, Xiao Liu, Kaiwen Men, Aohan Zeng, Yuxiao Dong, Jie Tang

Parameter-Efficient Tuning Makes a Good Classification Head (2022.emnlp-main)

Challenge:	Existing methods for extending the maximum context lengths of language models are lacking a strong baseline for in-context few-shot classification and on more challenging Chain-of-Thought reasoning, such as HotpotQA, deteriorate question miscomprehension and false inference.
Approach:	They propose to harness window-wise attention and positional embedding techniques to extend the maximum context lengths of language models.
Outcome:	The proposed method is able to extend the maximum context lengths of language models, e.g., 2048 for LLaMA, by harnessing window-wise attention and positional embedding techniques.

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Zhuoyi Yang, Ming Ding, Yanhui Guo, Qingsong Lv, Jie Tang

GLM: General Language Model Pretraining with Autoregressive Blank Infilling (2022.acl-long)

Challenge:	In recent years, pretrained models revolutionized the paradigm of natural language understanding . but the final-layer output of the backbone, i.e. the input of the classification head, will change greatly during finetuning .
Approach:	They propose to append a randomly initialized classification head after the pretrained backbone and finetune the whole model.
Outcome:	The proposed classification head can be replaced with the randomly initialized heads for a stable performance gain.

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Zhengxiao Du, Yujie Qian, Xiao Liu, Ming Ding, Jiezhong Qiu, Zhilin Yang, Jie Tang

Data Efficient RLVR via Off-Policy Influence Guidance (2026.acl-long)

Challenge:	Existing pretraining frameworks do not perform well for all tasks of three main categories, such as natural language understanding (NLU), unconditional generation, and conditional generation.
Approach:	They propose a general language model based on autoregressive blank infilling to address this challenge.
Outcome:	The proposed model outperforms BERT, T5, and GPT on a wide range of tasks across NLU, conditional and unconditional generation tasks.

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Erle Zhu, Dazhi Jiang, Yuan Wang, Xujun Li, Jiale Cheng, Yuxian Gu, Yilin Niu, Aohan Zeng, Jie Tang, Minlie Huang, Hongning Wang

Blockwise Self-Attention for Long Document Understanding (2020.findings-emnlp)

Challenge:	Existing data selection methods for RLVR are heuristic-based, lacking theoretical guarantees and generalizability.
Approach:	They propose an off-policy influence estimation method that approximates data influence using offline trajectories.
Outcome:	The proposed method reduces the computational cost of policy rollouts and improves storage and computation efficiency.

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Jiezhong Qiu, Hao Ma, Omer Levy, Wen-tau Yih, Sinong Wang, Jie Tang

Lifting Optimized Binaries to Canonical Compiler IR via Structure-Aware Retrieval and Iterative Verification (2026.acl-long)

Challenge:	Recent advances in pre-training and fine-tuning methods have drastically reshaped the landscape of natural language processing research.
Approach:	They propose a lightweight BERT model that introduces sparse block structures into the attention matrix to reduce memory consumption and training/inference time.
Outcome:	The proposed model uses 18.7-36.1% less memory and 12.0-25.1% more time to learn compared to an advanced BERT-based model, RoBERTa.

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Xiaoao Zhu, Jie Ren, Zhiqiang Li, Jie Zheng, Zhanyong Tang, Zheng Wang

Information Retrieval

Challenge:	Existing methods for decompiling binary code are brittle due to compiler optimizations that distort control-flow and data-flow structure.
Approach:	They propose a system that lifts optimized binaries to canonical compiler intermediate representation (IR) BRIDGE uses control-flow-aware retrieval-augmented generation with feedback-driven verification .
Outcome:	The proposed system outperforms seven baselines on humanEval-Decompile and MBPP, lifting x86-64 and ARM64 binaries to LLVM IR.

CoMoL: Efficient Mixture of LoRA Experts via Dynamic Core Space Merging (2026.findings-acl)

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Jie Cao, Zhenxuan Fan, Zhuonan Wang, Tianwei Lin, Ziyuan Zhao, Rolan Yan, Wenqiao Zhang, Feifei Shao, Hongwei Wang, Jun Xiao, Siliang Tang

AutoDetect: Towards a Unified Framework for Automated Weakness Detection in Large Language Models (2024.findings-emnlp)

Challenge:	Existing PEFT methods suffer from limited parameter efficiency and coarse-grained adaptation due to proliferation of LoRA experts and instance-level routing.
Approach:	They propose a new MoE-LoRA framework that incorporates expert diversity, parameter efficiency, and fine-grained adaptation.
Outcome:	The proposed framework outperforms existing methods on multiple tasks while maintaining parameter efficiency.

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Jiale Cheng, Yida Lu, Xiaotao Gu, Pei Ke, Xiao Liu, Yuxiao Dong, Hongning Wang, Jie Tang, Minlie Huang

HiEdit: Lifelong Model Editing with Hierarchical Reinforcement Learning (2026.acl-long)

Challenge:	Large Language Models (LLMs) exhibit significant but subtle weaknesses, such as mistakes in instruction-following or coding tasks.
Approach:	They propose a framework to automatically expose weaknesses in Large Language Models (LLMs) they use three LLM-powered agents to perform comprehensive weakness identification .
Outcome:	The proposed framework shows that it is more effective than untargeted data augmentation methods like Self-Instruct to identify weaknesses in LLMs.

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Yangfan Wang, Tianyang Sun, Chen Tang, Jie Liu, Wei Cai, Jingchi Jiang

Challenge:	Existing approaches to lifelong model editing apply parameter perturbations to static and dense layers for all instances.
Approach:	They propose a hierarchical reinforcement learning framework that identifies the most knowledge-relevant layers for each editing instance.
Outcome:	The proposed framework boosts the performance of the competitive RLEdit by 8.48% with perturbing only half of the layers per edit.

Multi-Granularity Semantic Revision for Large Language Model Distillation (2026.acl-long)

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Xiaoyu Liu, Yun Zhang, Wei Li, Simiao Li, Xudong Huang, Hanting Chen, Yehui Tang, Jie Hu, Zhiwei Xiong, Yunhe Wang

LongRAG: A Dual-Perspective Retrieval-Augmented Generation Paradigm for Long-Context Question Answering (2024.emnlp-main)

Challenge:	Existing methods for generating large language models rely on student-generated outputs, which introduce generation errors and misguide the distillation process.
Approach:	They propose a multi-granularity semantic revision method for LLM distillation that corrects errors using teacher-generated tokens and re-generates the sequence to minimize errors.
Outcome:	The proposed method reduces errors and misguides distillation on student models and improves consistency between teacher and student outputs.

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Qingfei Zhao, Ruobing Wang, Yukuo Cen, Daren Zha, Shicheng Tan, Yuxiao Dong, Jie Tang

Natural Language Interfaces

Challenge:	Existing long-context Large Language Models (LLMs) struggle with the “lost in the middle” issue.
Approach:	They propose a general, dual-perspective, and robust LLM-based RAG system paradigm for LCQA to enhance RAG’s understanding of complex long-context knowledge.
Outcome:	The proposed system outperforms long-context LLMs, advanced RAG, and vanilla RAG on three multi-hop datasets.

MTP-RL: Acceleration of Reinforcement Learning Rollouts with Policy-Aligned Multi-Token Prediction (2026.findings-acl)

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Ke Wang, Aohan Zeng, Zhengxiao Du, Yuxuan Hu, Bohan Zhang, Xinyi Wang, Jie Tang, Jing Zhang

ZoomRAG: Hierarchical Random-walk Zooming across Multi-scale Information Graphs for Fast and Accurate RAG (2026.findings-acl)

Challenge:	Reinforcement learning (RL) is widely applied to boost the performance of pretrained models, yet its training efficiency is severely constrained by rollout generation.
Approach:	They propose a framework that accelerates the rollout phase for diverse models by equipping a pipeline to equip the multi-layer parameter-sharing MTP for all models and an advantage-aware MTP optimization strategy.
Outcome:	The proposed framework achieves stable growth of acceptance length during RL training, and also accelerates RL rollouts, achieving an average 23.1%–55.3% reduction in rollout time compared to baselines.

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Xianming Hu, Jingyang Chen, Bin Tang, Yihe Liu, Yihong Huang, Hongbo Zhao, Nuoyi Chen, Jie Zhang, Ping Li, Kai Zhang

Structured Data in NLP

Challenge:	retrieval-augmented generation (RAG) is a powerful tool for NLP applications . but it is challenging to encode large knowledge bases as compact offline structures .
Approach:	They propose a coarse-to-fine hierarchical graph inference method that uses random walks to retrieve information from a corpus of documents.
Outcome:	The proposed method reduces offline indexing costs and accelerates retrieval.

AlignBench: Benchmarking Chinese Alignment of Large Language Models (2024.acl-long)

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Xiao Liu, Xuanyu Lei, Shengyuan Wang, Yue Huang, Andrew Feng, Bosi Wen, Jiale Cheng, Pei Ke, Yifan Xu, Weng Lam Tam, Xiaohan Zhang, Lichao Sun, Xiaotao Gu, Hongning Wang, Jing Zhang, Minlie Huang, Yuxiao Dong, Jie Tang

A Survey of Post-Training Scaling in Large Language Models (2025.acl-long)

Challenge:	Effective evaluation of alignment for emerging Chinese LLMs is still significantly lacking, calling for real-scenario grounded, open-ended, challenging and automatic evaluations tailored for alignment.
Approach:	They propose a multi-dimensional benchmark for evaluating LLMs’ alignment in Chinese with 8 main categories, 683 real-scenario rooted queries and corresponding human verified references.
Outcome:	The benchmark uses a human-in-the-loop data curation pipeline, 683 real-scenario rooted queries and human verified references.

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Hanyu Lai, Xiao Liu, Junjie Gao, Jiale Cheng, Zehan Qi, Yifan Xu, Shuntian Yao, Dan Zhang, Jinhua Du, Zhenyu Hou, Xin Lv, Minlie Huang, Yuxiao Dong, Jie Tang

LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks (2025.acl-long)

Challenge:	Large language models (LLMs) have demonstrated proficiency in understanding and generating human natural languages.
Approach:	They propose a framework for scaling large language models using supervised fine-tuning, RLxF and test-time compute methodologies.
Outcome:	The proposed model can be used to understand and generate human natural languages.

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Yushi Bai, Shangqing Tu, Jiajie Zhang, Hao Peng, Xiaozhi Wang, Xin Lv, Shulin Cao, Jiazheng Xu, Lei Hou, Yuxiao Dong, Jie Tang, Juanzi Li

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

Challenge:	Existing benchmarks on longcontext large language models fail to reflect their deep understanding capabilities across diverse tasks.
Approach:	They propose a benchmark to assess the ability of long-context large language models to handle long-text problems.
Outcome:	The proposed model achieves 50.1% accuracy when directly answering the questions . human experts achieve only 53.7% accuracy under a 15-minute time constraint .

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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.

AgentTuning: Enabling Generalized Agent Abilities for LLMs (2024.findings-acl)

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Aohan Zeng, Mingdao Liu, Rui Lu, Bowen Wang, Xiao Liu, Yuxiao Dong, Jie Tang

The Rise of Darkness: Safety-Utility Trade-Offs in Role-Playing Dialogue Agents (2025.findings-acl)

Challenge:	Open large language models (LLMs) with great performance in various tasks are far inferior to commercial models such as ChatGPT and GPT-4 when acting as agents to tackle complex tasks in the real world.
Approach:	They propose a method to enhance the agent capabilities of LLMs while maintaining their general abilities.
Outcome:	The AgentLM-70B is comparable to GPT-3.5-turbo on unseen agent tasks, demonstrating generalized agent capabilities.

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Yihong Tang, Kehai Chen, Xuefeng Bai, Zheng-Yu Niu, Bo Wang, Jie Liu, Min Zhang

Dialogue Systems & Conversational Agents

Challenge:	Large Language Models (LLMs) demonstrate their utility in character simulations, but they pose a risk of generating unsafe content.
Approach:	They propose a method which dynamically adjusts safety-utility preferences based on the degree of risk coupling and guides the model to generate responses biased toward utility or safety.
Outcome:	The proposed method improves safety metrics while maintaining utility.

PlotGen-Bench: Evaluating VLMs on Generating Visualization Code from Diverse Plots across Multiple Libraries (2026.findings-acl)

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Yi Zhao, Zhen Yang, Shuaiqi Duan, Wenmeng Yu, Zhe Su, Jibing Gong, Jie Tang

MoA: Heterogeneous Mixture of Adapters for Parameter-Efficient Fine-Tuning of Large Language Models (2026.acl-long)

Challenge:	PlotGen-Bench evaluates vision-language models' ability to generate executable visualization code from plots under realistic and complex visualization requirements.
Approach:	They propose a benchmark to evaluate plot-to-code generation in vision-language models . they use Matplot, Matplos, Mat3D, Mat4D, and Mat4E to evaluate their performance .
Outcome:	The proposed benchmark covers 9 major categories, 30 subcategories, and 3 core tasks . it covers 2D, 3D and animated plots across 5 widely used visualization libraries.

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Jie Cao, Tianwei Lin, Bo Yuan, Rolan Yan, Hongyang He, Wenqiao Zhang, Juncheng Li, Dongping Zhang, Siliang Tang, Yueting Zhuang

ExpanRL: Hierarchical Reinforcement Learning for Course Concept Expansion in MOOCs (2020.aacl-main)

Challenge:	Existing methods for parameter-efficient fine-tuning (PEFT) are limited by computational costs and performance degradation.
Approach:	They propose a method that integrates Low-Rank Adaptation and Mixture-of-Experts (MoE) they propose combining expert load imbalance and representation collapse to improve LLM performance .
Outcome:	The proposed method outperforms homogeneous MoE-LoRA architectures in performance and parameter efficiency.

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Jifan Yu, Chenyu Wang, Gan Luo, Lei Hou, Juanzi Li, Jie Tang, Minlie Huang, Zhiyuan Liu

Challenge:	Existing methods for concept expansion in MOOCs are inefficient because of the diversity of MOOC courses and rapid updates.
Approach:	They propose an end-to-end hierarchical reinforcement learning (HRL) model for concept expansion in MOOCs that employs a two-level mechanism of seed selection and concept expansion.
Outcome:	The proposed model improves on nine real MOOC datasets and maintains competitive performance under different settings.

Exploring Memorization in Fine-tuned Language Models (2024.acl-long)

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Shenglai Zeng, Yaxin Li, Jie Ren, Yiding Liu, Han Xu, Pengfei He, Yue Xing, Shuaiqiang Wang, Jiliang Tang, Dawei Yin

Glyph: Scaling Context Windows via Visual-Text Compression (2026.acl-long)

Challenge:	Existing studies have shown that pre-trained langauge models tend to memorize and regenerate segments of their pre-training corpus when prompted appropriately.
Approach:	They conduct the first comprehensive analysis to explore language models’ memorization during fine-tuning across tasks.
Outcome:	The proposed analysis shows that memorization presents a strong disparity among different fine-tuning tasks.

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Jiale Cheng, Yusen Liu, Xinyu Zhang, Yulin Fei, Wenyi Hong, Ruiliang Lyu, Weihan Wang, Zhe Su, Xiaotao Gu, Xiao Liu, Yushi Bai, Jie Tang, Hongning Wang, Minlie Huang

The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG) (2024.findings-acl)

Challenge:	Large language models (LLMs) traditionally represent text as sequences of discrete tokens . a long-context scaling problem requires processing more tokens more efficiently .
Approach:	They propose a framework that renders long texts into compact visual pages and processes them with a vision-language model.
Outcome:	The proposed framework renders long texts into compact visual pages and processes them with a vision-language model.

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Shenglai Zeng, Jiankun Zhang, Pengfei He, Yiding Liu, Yue Xing, Han Xu, Jie Ren, Yi Chang, Shuaiqiang Wang, Dawei Yin, Jiliang Tang

ChatGLM-Math: Improving Math Problem-Solving in Large Language Models with a Self-Critique Pipeline (2024.findings-emnlp)

Challenge:	Retrieval-augmented generation (RAG) is a powerful technique to facilitate language model generation with proprietary and private data, where data privacy is . a privacy issue that is currently under-explored, is posed by RAG.
Approach:	They propose to use retrieval-augmented generation (RAG) to facilitate language model generation with proprietary and private data where data privacy is a pivotal concern.
Outcome:	The proposed attack methods demonstrate that RAG can mitigate the old risks, i.e., leakage of the LLMs’ training data.

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Yifan Xu, Xiao Liu, Xinghan Liu, Zhenyu Hou, Yueyan Li, Xiaohan Zhang, Zihan Wang, Aohan Zeng, Zhengxiao Du, Zhao Wenyi, Jie Tang, Yuxiao Dong

Numerical Reasoning

Challenge:	Large language models (LLMs) have shown excellent mastering of human language but struggle in real-world applications that require mathematical problem-solving.
Approach:	They propose a pipeline to train a general Math-Critique model from the LLM itself to provide feedback signals and employ rejective fine-tuning and direct preference optimization over the Llm's own generations for data collection.
Outcome:	The proposed pipeline outperforms existing LLMs that could be two times larger.

LogicGame: Benchmarking Rule-Based Reasoning Abilities of Large Language Models (2025.findings-acl)

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Jiayi Gui, Yiming Liu, Jiale Cheng, Xiaotao Gu, Xiao Liu, Hongning Wang, Yuxiao Dong, Jie Tang, Minlie Huang

HOSMEL: A Hot-Swappable Modularized Entity Linking Toolkit for Chinese (2022.acl-demo)

Challenge:	Large Language Models (LLMs) have demonstrated notable capabilities across various tasks, showcasing complex problem-solving abilities.
Approach:	They propose a benchmark to evaluate the rule-based logical reasoning capabilities of Large Language Models (LLMs) they create simulated scenarios in which models execute or plan operations to achieve specific outcomes.
Outcome:	The proposed benchmark evaluates the performance of large language models on a variety of scenarios with varying difficulty levels.

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Daniel Zhang-li, Jing Zhang, Jifan Yu, Xiaokang Zhang, Peng Zhang, Jie Tang, Juanzi Li

Knowledge Representation

Challenge:	Existing studies have explored the use of entity linking (EL) in downstream tasks.
Approach:	They propose a modularized entity linking toolkit for easy task adaptation.
Outcome:	The proposed toolkit achieves significantly better accuracy and less time and spaceconsumption than existing methods.

FFAEval: Evaluating Dialogue System via Free-For-All Ranking (2023.findings-emnlp)

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Zeyao Ma, Zijun Yao, Jing Zhang, Jifan Yu, Xiaohan Zhang, Juanzi Li, Jie Tang

Natural Language Interfaces

Challenge:	Existing evaluation metrics for open-domain dialogue systems show poor correlation with human assessment.
Approach:	They propose a free-for-all human evaluation framework that shares dialogue history with annotators for multi-turn scoring.
Outcome:	The proposed framework achieves a strong correlation with human assessment on English and Chinese dialogue systems.

DeCEMBERT: Learning from Noisy Instructional Videos via Dense Captions and Entropy Minimization (2021.naacl-main)

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Zineng Tang, Jie Lei, Mohit Bansal

MOOCCube: A Large-scale Data Repository for NLP Applications in MOOCs (2020.acl-main)

Challenge:	Existing methods to train models on unlabeled web videos are noisy and temporally misaligned . authors propose a method that adds captions and constrained attention loss to improve performance .
Approach:	They propose a method that adds captions from video frames as auxiliary text input to provide visual cues for learning better video and language associations.
Outcome:	The proposed method outperforms state-of-the-art methods on video-and-language tasks . it adds captions and constrained attention loss to improve model performance .

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Jifan Yu, Gan Luo, Tong Xiao, Qingyang Zhong, Yuquan Wang, Wenzheng Feng, Junyi Luo, Chenyu Wang, Lei Hou, Juanzi Li, Zhiyuan Liu, Jie Tang

Information Extraction & Text Mining

Challenge:	Massive open online courses (MOOCs) are a popular educational platform for advanced research.
Approach:	They propose to use MOOCCube to build a large-scale data repository of over 700 MOOC courses, 100k concepts, 8 million student behaviors with an external resource.
Outcome:	The proposed datasets show that they can facilitate research in MOOCs.

A General Framework to Enhance Fine-tuning-based LLM Unlearning (2025.findings-acl)

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Jie Ren, Zhenwei Dai, Xianfeng Tang, Hui Liu, Jingying Zeng, Zhen Li, Rahul Goutam, Suhang Wang, Yue Xing, Qi He, Hui Liu

Black-Box Prompt Optimization: Aligning Large Language Models without Model Training (2024.acl-long)

Challenge:	Existing approaches to remove copyrighted and privacy-sensitive data from Large Language Models (LLMs) have been proposed to remove specific data from LLMs without requiring full retraining.
Approach:	They propose a general framework that enhances the utility of fine-tuning-based methods by distinguishing target data and suppressing related generations.
Outcome:	The proposed framework improves the unlearning and utility of fine-tuning-based methods by distinguishing the target data and suppressing related generations.

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Jiale Cheng, Xiao Liu, Kehan Zheng, Pei Ke, Hongning Wang, Yuxiao Dong, Jie Tang, Minlie Huang

KCS: Diversify Multi-hop Question Generation with Knowledge Composition Sampling (2025.emnlp-main)

Challenge:	Large language models are often not well aligned with human intents, which requires additional training.
Approach:	They propose to use Black-Box Prompt Optimization (BPO) to perform alignments on large language models that are not well aligned with human intents.
Outcome:	The proposed model outperforms existing models and is model-agnostic.

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Yangfan Wang, Jie Liu, Chen Tang, Lian Yan, Jingchi Jiang

Text Generation

Challenge:	Existing approaches to multi-hop question answering focus on generating simple questions and neglecting the integration of essential knowledge, such as relevant sentences within documents.
Approach:	They propose a framework to expand the diversity of generated multi-hop questions by sampling varied knowledge compositions within a given context.
Outcome:	The proposed framework improves the overall accuracy of knowledge composition selection by 3.9% on hotpotQA and 2WikiMultihopQA datasets.

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

Beyond Literal Mapping: Benchmarking and Improving Non-Literal Translation Evaluation (2026.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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Yanzhi Tian, Cunxiang Wang, Zeming Liu, Heyan Huang, Wenbo Yu, Dawei Song, Jie Tang, Yuhang Guo

Machine Translation

Challenge:	Large Language Models (LLMs) have advanced machine translation (MT) a meta-evaluation dataset focused on non-literal translations is lacking . experimental results show the inaccuracies of traditional MT metrics and the limitations of LLM-as-a-Judge.
Approach:	They propose a meta-evaluation framework that leverages sub-agents to evaluate machine translation metrics.
Outcome:	The proposed framework improves on the knowledge cutoff and score inconsistency problem.

AndroidLab: Training and Systematic Benchmarking of Android Autonomous Agents (2025.acl-long)

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Yifan Xu, Xiao Liu, Xueqiao Sun, Siyi Cheng, Hao Yu, Hanyu Lai, Shudan Zhang, Dan Zhang, Jie Tang, Yuxiao Dong

Dynamic Scaling of Unit Tests for Code Reward Modeling (2025.acl-long)

Challenge:	Existing studies on Android agents lack systematic research on open-source and closed-source models.
Approach:	They propose a framework for Android agents that includes an operation environment and a reproducible benchmark.
Outcome:	The proposed framework lifts the success rate of open-source LLMs and LMMs from 4.59% to 21.50% for LLM and 1.93% to 13.28% for LMM.

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Zeyao Ma, Xiaokang Zhang, Jing Zhang, Jifan Yu, Sijia Luo, Jie Tang

Gamma-Guard: Lightweight Residual Adapters for Robust Guardrails in Large Language Models (2025.emnlp-main)

Challenge:	Existing large language models struggle to produce accurate responses on the first attempt for complex reasoning tasks like code generation.
Approach:	They propose a lightweight yet effective unit test generator that scales unit tests based on problem difficulty.
Outcome:	The proposed approach significantly improves performance on three benchmarks.

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Lijia Lv, Yuanshu Zhao, Guan Wang, Xuehai Tang, Wen Jie, Jizhong Han, Songlin Hu

Cognitive Graph for Multi-Hop Reading Comprehension at Scale (P19-1)

Challenge:	Large language models (LLMs) are widely deployed as zero-shot evaluators for answer grading, content moderation, and document ranking.
Approach:	They propose a system that trains LLMs with adapters to denoise embeddings and refocus attention.
Outcome:	The proposed model lifts adversarial accuracy from 5% to 95% a 90 percentage-point gain while reducing clean-data accuracy by just 8 percentage points.

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Ming Ding, Chang Zhou, Qibin Chen, Hongxia Yang, Jie Tang

Beyond Text: Unveiling Privacy Vulnerabilities in Multi-modal Retrieval-Augmented Generation (2025.emnlp-main)

Challenge:	a new framework for multi-hop reading comprehension question answering is needed to cross the chasm of reading comprehension between machine and human.
Approach:	They propose a CogQA framework for multi-hop reading comprehension question answering in web-scale documents that builds a cognitive graph in an iterative process by coordinating an implicit extraction module and an explicit reasoning module.
Outcome:	The proposed framework outperforms the best competitor in the hotpotQA dataset in F1 . it provides explainable reasoning paths and accurate answers, while giving accurate answers .

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Jiankun Zhang, Shenglai Zeng, Jie Ren, Tianqi Zheng, Hui Liu, Xianfeng Tang, Hui Liu, Yi Chang

A Robust Semantics-based Watermark for Large Language Model against Paraphrasing (2024.findings-naacl)

Challenge:	Large Multimodal Models (LMMs) can process text, images, and audio, but they introduce privacy vulnerabilities.
Approach:	They propose a compositional structured prompt attack to exploit MRAG privacy vulnerabilities . they show that LMMs can generate outputs resembling retrieved content .
Outcome:	The proposed approach generates outputs resembling retrieved content and exposes sensitive information.

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Jie Ren, Han Xu, Yiding Liu, Yingqian Cui, Shuaiqiang Wang, Dawei Yin, Jiliang Tang

Robustness in NLP

Challenge:	Existing methods to detect LLM-generated content use simple hashes of precedent tokens to partition vocabulary.
Approach:	They propose a semantics-based watermark framework to enhance the robustness against paraphrase.
Outcome:	The proposed framework is robust under different paraphrases and the semantic meaning of the sentences will be likely preserved under paraphrase.

Adversarial Preference Learning for Robust LLM Alignment (2025.findings-acl)

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Yuanfu Wang, Pengyu Wang, Chenyang Xi, Bo Tang, Junyi Zhu, Wenqiang Wei, Chen Chen, Chao Yang, Jingfeng Zhang, Chaochao Lu, Yijun Niu, Keming Mao, Zhiyu Li, Feiyu Xiong, Jie Hu, Mingchuan Yang

DataSciBench: An LLM Agent Benchmark for Data Science (2026.findings-acl)

Challenge:	Modern language models rely on Reinforcement Learning from Human Feedback (RLHF) to encourage safe behaviors, but they remain vulnerable to adversarial attacks due to three key limitations: (1) the inefficiency and high cost of human annotation; (2) the vast diversity of potential adversarials; and (3) the risk of feedback bias and reward hacking.
Approach:	They propose an iterative adversarial training method that incorporates three key innovations to address these challenges.
Outcome:	Experiments on Mistral-7B-Instruct-v0.3 show that the proposed method significantly enhances robustness and reduces harmful outputs from 5.88% to 0.43%.

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Dan Zhang, Sining Zhoubian, Min Cai, Fengzu Li, Lekang Yang, Wei Wang, Tianjiao Dong, Ziniu Hu, Jie Tang, Yisong Yue

Challenge:	Existing benchmarks focus on single task, simple evaluation metrics, and readily available ground truth (GT) DataSciBench is built on curated, natural, and challenging prompts with complex evaluation criteria and uncertain GT.
Approach:	They propose a benchmark for evaluating Large Language Models in data science that integrates LLM-based self-consistency and human verification to ensure accuracy.
Outcome:	The proposed framework outperforms open-source models in all metrics and offers rigorous insights into LLM strengths and weaknesses.

Transferable and Efficient Non-Factual Content Detection via Probe Training with Offline Consistency Checking (2024.acl-long)

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Xiaokang Zhang, Zijun Yao, Jing Zhang, Kaifeng Yun, Jifan Yu, Juanzi Li, Jie Tang

On the Generalization of Training-based ChatGPT Detection Methods (2024.findings-emnlp)

Challenge:	Existing factuality detection methods are not effective for large language models (LLMs).
Approach:	They propose a probing model that trains on offline consistency checking results.
Outcome:	The proposed model reduces the computational burden of generating multiple responses by online consistency verification and improves on factuality detection and question answering benchmarks.

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Han Xu, Jie Ren, Pengfei He, Shenglai Zeng, Yingqian Cui, Amy Liu, Hui Liu, Jiliang Tang

SWE-Dev: Building Software Engineering Agents with Training and Inference Scaling (2025.findings-acl)

Challenge:	Existing studies show that training-based methods are ineffective to detect LLM generated texts from unseen tasks or topics which are not collected during training.
Approach:	They propose to train classification models to distinguish LLMs from human texts by a distribution shift caused by prompts, text lengths, topics, and language tasks.
Outcome:	The proposed methods can detect LLMs from black-box models, but they suffer from distribution shifts due to a wide range of factors, including prompts, text lengths, topics, and language tasks.

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Haoran Wang, Zhenyu Hou, Yao Wei, Jie Tang, Yuxiao Dong

Challenge:	Large language models (LLMs) have advanced rapidly from conversational problem solving to addressing real-world tasks involving tool use, such as software engineering (SWE).
Approach:	They propose to build an LLM-based software engineering agent that synthesizes test cases and scales up agent trajectories to build training data.
Outcome:	The proposed model outperforms state-of-the-art models on the SWE-bench-Verified benchmark.

TreeRL: LLM Reinforcement Learning with On-Policy Tree Search (2025.acl-long)

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Zhenyu Hou, Ziniu Hu, Yujiang Li, Rui Lu, Jie Tang, Yuxiao Dong

SCAIR: Schema-Conditioned Agentic Iterative Reasoning for Enterprise Knowledge Graphs (2026.acl-industry)

Challenge:	Existing methods for On-Policy LLM RL typically train a separate process reward model, which suffers from distribution mismatch and reward hacking.
Approach:	They propose a reinforcement learning framework that directly incorporates on-policy tree search for RL training.
Outcome:	Experiments on math and code reasoning benchmarks show that tree search achieves superior performance compared to traditional ChainRL.

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Prateek Chaturvedi, Yuqicheng Zhu, Hongkuan Zhou, Dongzhuoran Zhou, Yunjie He, Steffen Staab, Fei Du, Jie Tang, Evgeny Kharlamov

Structured Data in NLP

Challenge:	Existing agentic approaches for Knowledge Graph-based Retrieval-Augmented Generation fail to generalize to real-world enterprise Knowledge graphs (KGs) dense, schema-driven, and operationally constrained, requiring a training-free framework.
Approach:	They propose a training-free framework that integrates structured planning with controlled iterative reasoning by injecting schema-conditioned structural priors and enforcing schemas during multi-hop reasoning.
Outcome:	The proposed framework significantly improves on a real-world enterprise-oriented benchmark constructed from a Configuration Management DataBase (CMDB).

LongSafety: Evaluating Long-Context Safety of Large Language Models (2025.acl-long)

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Yida Lu, Jiale Cheng, Zhexin Zhang, Shiyao Cui, Cunxiang Wang, Xiaotao Gu, Yuxiao Dong, Jie Tang, Hongning Wang, Minlie Huang

NaturalCodeBench: Examining Coding Performance Mismatch on HumanEval and Natural User Queries (2024.findings-acl)

Challenge:	Large Language Models (LLMs) have demonstrated remarkable capabilities in understanding and generating long sequences.
Approach:	They propose a benchmark to evaluate LLM safety in open-ended long-context tasks . they find that relevant context and extended input sequences can exacerbate safety risks .
Outcome:	The proposed benchmark identifies significant safety vulnerabilities in 16 LLMs . strong safety performance in short-context scenarios does not correlate with safety in long-contact tasks .

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Shudan Zhang, Hanlin Zhao, Xiao Liu, Qinkai Zheng, Zehan Qi, Xiaotao Gu, Yuxiao Dong, Jie Tang

Text Editing as Imitation Game (2022.findings-emnlp)

Challenge:	Large language models (LLMs) generate code for productive activities, but current benchmarks for code synthesis are oriented towards introductory tasks on algorithm and data science.
Approach:	They propose a code benchmark to mirror the complexity and variety of scenarios in real-world coding tasks.
Outcome:	The proposed benchmark improves on 39 large language models with close HumanEval scores and achieves an efficiency increase of more than 4 times.

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Ning Shi, Bin Tang, Bo Yuan, Longtao Huang, Yewen Pu, Jie Fu, Zhouhan Lin

Syntactic Text Processing

Challenge:	Text editing is an important domain of processing tasks to edit the text in a localized fashion, such as text simplification.
Approach:	They propose a nonautoregressive decoder for state-to-action demonstrations that parallels the decoding while retaining the dependencies between tokens.
Outcome:	The proposed model outperforms the autoregressive baselines on a suite of Arithmetic Equation benchmarks in terms of performance, efficiency, and robustness.

Mitigating the Privacy Issues in Retrieval-Augmented Generation (RAG) via Pure Synthetic Data (2025.emnlp-main)

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Shenglai Zeng, Jiankun Zhang, Pengfei He, Jie Ren, Tianqi Zheng, Hanqing Lu, Han Xu, Hui Liu, Yue Xing, Jiliang Tang

TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios (2025.findings-acl)

Challenge:	Existing literature suggests that RAG systems may face privacy issues when the retrieval process involves private data.
Approach:	They propose a two-stage synthetic data generation paradigm that uses attributes to preserve contextual information from the original data.
Outcome:	The proposed approach preserves key contextual information from the original data while reducing privacy risks.

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Xiaokang Zhang, Sijia Luo, Bohan Zhang, Zeyao Ma, Jing Zhang, Yang Li, Guanlin Li, Zijun Yao, Kangli Xu, Jinchang Zhou, Daniel Zhang-Li, Jifan Yu, Shu Zhao, Juanzi Li, Jie Tang

Middleware for LLMs: Tools Are Instrumental for Language Agents in Complex Environments (2024.emnlp-main)

Challenge:	TableLLM is a robust large language model capable of handling tabular data manipulation tasks.
Approach:	They propose a distant supervision method for training which includes a reasoning process extension strategy and a cross-way validation strategy.
Outcome:	The proposed model has 8 billion parameters and is capable of handling tabular data tasks.

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Yu Gu, Yiheng Shu, Hao Yu, Xiao Liu, Yuxiao Dong, Jie Tang, Jayanth Srinivasa, Hugo Latapie, Yu Su

AutoRE: Document-Level Relation Extraction with Large Language Models (2024.acl-demos)

Challenge:	Large language models (LLMs) are generalist agents capable of operating within complex environments.
Approach:	They propose a class of tools that can serve as a middleware layer shielding LLMs from environmental complexity.
Outcome:	The proposed tool can shield the LLM from environmental complexity in two representative complex environments.

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Lilong Xue, Dan Zhang, Yuxiao Dong, Jie Tang

Information Extraction & Text Mining

Challenge:	Existing methods for relation extraction are limited to Sentence-level Relation Extraction (SentRE) tasks.
Approach:	They propose an end-to-end DocRE model that adopts a novel RE extraction paradigm named RHF (Relation-Head-Facts) Unlike existing approaches, AutoRE does not rely on the assumption of known relation options, making it more reflective of real-world scenarios.
Outcome:	The proposed model surpasses TAG by 10.03% and 9.03% on the dev and test set.

LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding (2024.acl-long)

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Yushi Bai, Xin Lv, Jiajie Zhang, Hongchang Lyu, Jiankai Tang, Zhidian Huang, Zhengxiao Du, Xiao Liu, Aohan Zeng, Lei Hou, Yuxiao Dong, Jie Tang, Juanzi Li

Multilinguality

Challenge:	Large language models (LLMs) can only handle texts a few thousand tokens long, limiting their applications on longer sequence inputs, such as books, reports, and codebases.
Approach:	They propose a bilingual, multi-task benchmark for long context understanding that extends context windows and more sophisticated memory mechanisms to improve models' long context capabilities.
Outcome:	The proposed model outperforms open-source models but struggles on longer contexts.

Parameter-Efficient Prompt Tuning Makes Generalized and Calibrated Neural Text Retrievers (2023.findings-emnlp)

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Weng Tam, Xiao Liu, Kaixuan Ji, Lilong Xue, Jiahua Liu, Tao Li, Yuxiao Dong, Jie Tang

LongAlign: A Recipe for Long Context Alignment of Large Language Models (2024.findings-emnlp)

Challenge:	Prompt tuning is a technique that updates few parameters in pre-trained models for language understanding and generation tasks.
Approach:	They propose to leverage prompt tuning for neural text retrieval to improve generalization and cross-domain generalization.
Outcome:	The proposed approach can mitigate the two issues faced by fine-tuning retrieval methods and improve the out-of-domain zero-shot generalization of the retrieval models.

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Yushi Bai, Xin Lv, Jiajie Zhang, Yuze He, Ji Qi, Lei Hou, Jie Tang, Yuxiao Dong, Juanzi Li

Towards Knowledge-Based Recommender Dialog System (D19-1)

Challenge:	Existing studies to build long context language models focus on context extension and continual training on long text.
Approach:	They propose a recipe for instruction fine-tuning on input sequences of similar length . they adopt packing and sorted batching strategies to speed up supervised fine-uning .
Outcome:	The proposed model outperforms existing recipes for LLMs in long context tasks by 30% while maintaining proficiency in handling short, generic tasks.

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Qibin Chen, Junyang Lin, Yichang Zhang, Ming Ding, Yukuo Cen, Hongxia Yang, Jie Tang

Dialogue Systems & Conversational Agents

Challenge:	Existing frameworks that only provide information about user preferences can be inaccurate in e-commerce recommender systems.
Approach:	They propose a framework which integrates the recommender system and dialog generation system by introducing information about users’ preferences.
Outcome:	The proposed framework can achieve better performance in both dialog generation and recommendation compared with baselines.

HPSS: Heuristic Prompting Strategy Search for LLM Evaluators (2025.findings-acl)

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Bosi Wen, Pei Ke, Yufei Sun, Cunxiang Wang, Xiaotao Gu, Jinfeng Zhou, Jie Tang, Hongning Wang, Minlie Huang

CritiqueLLM: Towards an Informative Critique Generation Model for Evaluation of Large Language Model Generation (2024.acl-long)

Challenge:	Existing efforts to optimize text evaluation prompts neglect the combinatorial impact of multiple factors, leading to insufficient optimization of the evaluation pipeline.
Approach:	They propose to integrate 8 key factors for evaluation prompts and integrate them into an algorithm that searches for well-behaved prompting strategies for LLM evaluators.
Outcome:	The proposed method outperforms existing methods and human-designed evaluation prompts on four evaluation tasks.

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Pei Ke, Bosi Wen, Andrew Feng, Xiao Liu, Xuanyu Lei, Jiale Cheng, Shengyuan Wang, Aohan Zeng, Yuxiao Dong, Hongning Wang, Jie Tang, Minlie Huang

GKD: A General Knowledge Distillation Framework for Large-scale Pre-trained Language Model (2023.acl-industry)

Challenge:	Existing models for NLP evaluations lack the ability to generate informative critiques in pointwise grading and pairwise comparison especially without references.
Approach:	They propose a method which can acquire pointwise grading critiques with pseudo references and revise these critiques via multi-path prompting to obtain informative evaluation data in different tasks and settings.
Outcome:	The proposed method outperforms all open-source models and even GPT-4 in system-level correlations of pointwise grading.

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Shicheng Tan, Weng Lam Tam, Yuanchun Wang, Wenwen Gong, Shu Zhao, Peng Zhang, Jie Tang

CoT-based Synthesizer: Enhancing LLM Performance through Answer Synthesis (2025.acl-long)

Challenge:	Existing knowledge distillation frameworks for language models are limited by memory and the use of complex distillation methods on larger-scale PLMs.
Approach:	They propose a general knowledge distillation framework that supports distillation on larger-scale PLMs using various distillation methods.
Outcome:	The proposed framework can support distillation on larger-scale PLMs and 25 mainstream methods on 8 NVIDIA A100 (40GB) GPUs.

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Bohan Zhang, Xiaokang Zhang, Jing Zhang, Jifan Yu, Sijia Luo, Jie Tang

CharacterGLM: Customizing Social Characters with Large Language Models (2024.emnlp-industry)

Challenge:	Existing inference scaling methods rely heavily on the quality of candidate responses . however, they are unable to produce correct answers when all candidates are flawed .
Approach:	They propose a CoT-based inference scaling strategy that leverages CoT reasoning to synthesize superior answers by analyzing complementary information from multiple candidate responses.
Outcome:	The proposed method improves performance on four benchmark datasets with seven policy models.

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Jinfeng Zhou, Zhuang Chen, Dazhen Wan, Bosi Wen, Yi Song, Jifan Yu, Yongkang Huang, Pei Ke, Guanqun Bi, Libiao Peng, JiaMing Yang, Xiyao Xiao, Sahand Sabour, Xiaohan Zhang, Wenjing Hou, Yijia Zhang, Yuxiao Dong, Hongning Wang, Jie Tang, Minlie Huang

LLMBox: A Comprehensive Library for Large Language Models (2024.acl-demos)

Challenge:	Character-based dialogue systems (CharacterDial) allow users to customize social characters for social interactions.
Approach:	They will collect a large-scale Chinese corpus of characters with diverse categories and behaviors and develop CharacterGLM models to address these challenges.
Outcome:	Experiments show that CharacterGLM outperforms most popular open- and closed-source LLMs and performs comparable to GPT-4.

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Tianyi Tang, Hu Yiwen, Bingqian Li, Wenyang Luo, ZiJing Qin, Haoxiang Sun, Jiapeng Wang, Shiyi Xu, Xiaoxue Cheng, Geyang Guo, Han Peng, Bowen Zheng, Yiru Tang, Yingqian Min, Yushuo Chen, Jie Chen, Ranchi Zhao, Luran Ding, Yuhao Wang, Zican Dong, Xia Chunxuan, Junyi Li, Kun Zhou, Xin Zhao, Ji-Rong Wen

HoWToBench: Holistic Evaluation for LLM’s Capability in Human-level Writing using Tree of Writing (2026.acl-long)

Challenge:	a library to facilitate the development, use, and evaluation of large language models (LLMs) is presented.
Approach:	They propose a unified library to facilitate the development, use and evaluation of large language models (LLMs).
Outcome:	The proposed library is based on extensive experiments in a variety of evaluation settings.

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Andrew Zhuoer Feng, Cunxiang Wang, Yu Luo, Lin Fan, Irene Zhou, Zikang Wang, Xiaotao Gu, Jie Tang, Hongning Wang, Minlie Huang

Challenge:	Evaluating the writing capabilities of large language models remains a significant challenge due to the multidimensional nature of writing skills and the limitations of existing metrics.
Approach:	They propose to model the aggregation weights of sub-features in a tree-structured workflow and propose a Chinese writing benchmark that mitigates biases.
Outcome:	The proposed tree-of-writing (ToW) measures the writing capabilities of large language models (LLMs) in Chinese and shows that it mitigates biases and achieves a 0.93 Pearson correlation with human judgments.

Explain-then-translate: an analysis on improving program translation with self-generated explanations (2023.findings-emnlp)

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Zilu Tang, Mayank Agarwal, Alexander Shypula, Bailin Wang, Derry Wijaya, Jie Chen, Yoon Kim

Text Generation

Challenge:	Using self-generated natural language explanations improves zero-shot performance by 12% on average.
Approach:	They propose to use self-generated natural language explanations as an intermediate step for code-to-code translation with language models.
Outcome:	The proposed approach improves zero-shot performance by 12% on average . the proposed approach is not evaluated on a broader set of languages including low-resource languages.

AndroidGen: Building an Android Language Agent under Data Scarcity (2025.acl-long)

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Hanyu Lai, Junjie Gao, Xiao Liu, Yifan Xu, Shudan Zhang, Yuxiao Dong, Jie Tang

Course Concept Expansion in MOOCs with External Knowledge and Interactive Game (P19-1)

Challenge:	Existing LLMs lack high-quality data sources and lack robust data filtration strategies.
Approach:	They develop a framework to enhance the capabilities of LLM-based agents under data scarcity.
Outcome:	The proposed framework improves the capabilities of LLM-based agents under data scarcity.

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Jifan Yu, Chenyu Wang, Gan Luo, Lei Hou, Juanzi Li, Zhiyuan Liu, Jie Tang

Knowledge Representation

Challenge:	Existing methods to expand course concepts in MOOCs suffer from semantic drifts and lack of knowledge guidance.
Approach:	They propose to use a boundary search method to search for new concepts via external knowledge base and then use heterogeneous features to verify the results.
Outcome:	The proposed method improves on the datasets from Coursera and XuetangX.

P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks (2022.acl-short)

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Xiao Liu, Kaixuan Ji, Yicheng Fu, Weng Tam, Zhengxiao Du, Zhilin Yang, Jie Tang

Neural-DINF: A Neural Network based Framework for Measuring Document Influence (2020.acl-main)

Challenge:	Existing methods of prompt tuning cannot handle hard sequence labeling tasks.
Approach:	They propose to optimize prompt tuning to tune continuous prompts with a frozen language model.
Outcome:	The proposed method matches finetuning with prompt tuning while having only 0.1%-3% tuned parameters.

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Jie Tan, Changlin Yang, Ying Li, Siliang Tang, Chen Huang, Yueting Zhuang

Challenge:	Existing methods to measure scholarly impact of documents without citations only consider word frequency change.
Approach:	They propose a neural network framework that measures document influence without citations by using word frequency changes and word semantic shifts.
Outcome:	The proposed model outperforms existing models on document influence evaluation without citations.

CARE-STaR: Constraint-aware Self-taught Reasoner (2025.findings-acl)

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Zhiliang Li, Bo Tang, Yijun Niu, Beihong Jin, Qiwen Shi, Yuchen Feng, Zhiyu Li, Jie Hu, Mingchuan Yang, Feiyu Xiong

DeepStruct: Pretraining of Language Models for Structure Prediction (2022.findings-acl)

Challenge:	Recent research on instruction following has demonstrated that LLMs can handle complex instructions.
Approach:	They propose to assign constraints to different levels of constraints in instructions . they use chain-of-thought and self-taught reasoner methods to identify constraints .
Outcome:	The proposed method outperforms supervised fine-tuning (SFT) on three instruction-following benchmarks.

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Chenguang Wang, Xiao Liu, Zui Chen, Haoyun Hong, Jie Tang, Dawn Song

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

Challenge:	Pretrained language models perform structural understanding tasks that focus on understanding one aspect of the text.
Approach:	They propose a method for improving the structural understanding abilities of language models by pretraining them to generate structures from the text on task-agnostic corpora.
Outcome:	The proposed model performs state-of-the-art on 21 of 28 datasets.

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