Papers by Jie Dong

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

MMedAgent: Learning to Use Medical Tools with Multi-modal Agent (2024.findings-emnlp)

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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Binxu Li, Tiankai Yan, Yuanting Pan, Jie Luo, Ruiyang Ji, Jiayuan Ding, Zhe Xu, Shilong Liu, Haoyu Dong, Zihao Lin, Yixin Wang

Multimodality

Challenge:	Multi-modal Large Language Models (MLLMs) exhibit limited generality and often fall short when compared to specialized models.
Approach:	They propose a multi-modal medical agent that picks the most suitable medical tools based on user inputs.
Outcome:	The proposed agent performs better than open-source models and the closed-source model, GPT-4o.

ConCISE: Confidence-guided Compression in Step-by-step Efficient Reasoning (2025.emnlp-main)

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Ziqing Qiao, Yongheng Deng, Jiali Zeng, Dong Wang, Lai Wei, Guanbo Wang, Fandong Meng, Jie Zhou, Ju Ren, Yaoxue Zhang

AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling (2024.acl-long)

Challenge:	Existing methods for fine-tuning-based compression suffer from verbose outputs, increasing computational overhead.
Approach:	They propose a framework to generate concise reasoning chains using Confidence Injection and Early Stopping.
Outcome:	The proposed framework reduces the length of the model by up to 50% while maintaining high task accuracy.

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Jun Zhan, Junqi Dai, Jiasheng Ye, Yunhua Zhou, Dong Zhang, Zhigeng Liu, Xin Zhang, Ruibin Yuan, Ge Zhang, Linyang Li, Hang Yan, Jie Fu, Tao Gui, Tianxiang Sun, Yu-Gang Jiang, Xipeng Qiu

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

Challenge:	Existing language models that use discrete representations for unified processing of various modalities are limited to text generation and do not include multimodal output.
Approach:	They propose a multimodal language model that utilizes discrete representations for unified processing of various modalities.
Outcome:	The proposed model can be trained stably without any alterations to existing models or training paradigms.

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

MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning (2020.acl-main)

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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Jie Lei, Liwei Wang, Yelong Shen, Dong Yu, Tamara Berg, Mohit Bansal

Visual Data in NLP

Challenge:	Existing methods for generating paragraph descriptions for videos require a coherent paragraph and a higher level of coherence.
Approach:	They propose a new method that generates a summarized memory state from video segments and sentence history to help better predict the next sentence.
Outcome:	The proposed method generates more coherent and less repetitive paragraph captions while maintaining relevance to the input video events.

Revisiting Interpolation Augmentation for Speech-to-Text Generation (2024.findings-acl)

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Chen Xu, Jie Wang, Xiaoqian Liu, Qian Dong, Chunliang Zhang, Tong Xiao, JingBo Zhu, Dapeng Man, Wu Yang

Text Generation

Challenge:	Existing approaches to speech-to-text generation tasks are limited by the lack of extensive labeled datasets.
Approach:	They propose to use interpolation augmentation to construct virtual training samples by transforming inputs and labels to enhance generalization in other domains.
Outcome:	The proposed approach significantly improves performance across diverse tasks, architectures, and data scales, offering a promising avenue for more robust S2T systems in resource-constrained settings.

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

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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.

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

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

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

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

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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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

Reasoning with Ontology Graph: Toward Type-Constrained Knowledge Graph Question Answering (2026.acl-long)

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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Yongxue Shan, Jie Peng, Zixuan Dong, Fei Hu, Xiaodong Wang

Knowledge Representation

Challenge:	Existing knowledge graph question answering methods rely on LLM-induced type systems with inconsistent granularity or perform multi-hop reasoning without explicit target-type constraints.
Approach:	They propose a type-constrained knowledge graph question answering framework that reasons over a relation-centric ontology graph.
Outcome:	The proposed framework achieves state-of-the-art and produces ontology-grounded reasoning chains with substantial Hit@1 gains.

U-CORE: A Unified Deep Cluster-wise Contrastive Framework for Open Relation Extraction (2023.tacl-1)

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Jie Zhou, Shenpo Dong, Yunxin Huang, Meihan Wu, Haili Li, Jingnan Wang, Hongkui Tu, Xiaodong Wang

Information Extraction & Text Mining

Challenge:	Existing methods for Relation Extraction (RE) are limited due to the overlap between predefined and undefined relations.
Approach:	They propose a unified framework for both Zero-shot and Unsupervised Relation Extraction tasks by leveraging techniques from Contrastive Learning and Clustering.
Outcome:	The proposed framework improves on three well-known datasets showing an average improvement of 7.35% ARI on Zero-shot ORE tasks and 15.24% ARI for Unsupervised ORE.

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

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

YuLan-Mini: Pushing the Limits of Open Data-efficient Language Model (2025.acl-long)

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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Hu Yiwen, Huatong Song, Jie Chen, Jia Deng, Jiapeng Wang, Kun Zhou, Yutao Zhu, Jinhao Jiang, Zican Dong, Yang Lu, Xu Miao, Xin Zhao, Ji-Rong Wen

DeepGen: Diverse Search Ad Generation and Real-Time Customization (2022.emnlp-demos)

Challenge:	prevailing pre-training approaches for large language models involve several complexities.
Approach:	They propose a low-cost training recipe and a robust optimization approach to mitigate training instability . they also propose synthesis, curriculum, and data selection pipelines to integrate data .
Outcome:	The proposed model achieves top-tier performance among models with similar parameter scale . it is comparable to industry-leading models that require significantly more data .

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Konstantin Golobokov, Junyi Chai, Victor Ye Dong, Mandy Gu, Bingyu Chi, Jie Cao, Yulan Yan, Yi Liu

Information Retrieval

Challenge:	Existing systems that generate ads manually are not effective in generating ad copy and generating millions of ads for large businesses.
Approach:	They propose a system that generates fluent ads from advertiser’s web pages in an abstractive fashion and solves practical issues such as factuality and inference speed.
Outcome:	The proposed system generates fluent ads from advertiser’s web pages in an abstractive fashion and solves practical issues such as factuality and inference speed.

Sticker-TTS: Learn to Utilize Historical Experience with a Sticker-driven Test-Time Scaling Framework (2025.emnlp-main)

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Jie Chen, Jinhao Jiang, Yingqian Min, Zican Dong, Shijie Wang, Xin Zhao, Ji-Rong Wen

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

Challenge:	Large reasoning models have exhibited strong performance on complex reasoning tasks, but current test-time scaling methods rely on redundant sampling and ignore historical experience utilization.
Approach:	They propose a test-time scaling framework that coordinates three collaborative LRMs to iteratively explore and refine solutions guided by historical attempts.
Outcome:	The proposed framework surpasses strong baselines on three mathematical reasoning benchmarks, including AIME-24, AIME-25, and OlymMATH.

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

Knowledge-to-Verification: Exploring RLVR for LLMs in Knowledge-Intensive Domains (2026.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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Zhonghang Yuan, Zhefan Wang, Fang Hu, Zihong Chen, Jinzhe Li, Gang Li, Jie Ying, Huanjun Kong, Songyang Zhang, Nanqing Dong

RSGT: Relational Structure Guided Temporal Relation Extraction (2022.coling-1)

Challenge:	Recent large language models (LLMs) have demonstrated remarkable progress in reasoning, but their applications on knowledge-intensive domains have not been explored due to the scarcity of high-quality verifiable data.
Approach:	They propose a framework that extends reinforcement learning with verifiable rewards (RLVR) to knowledge-intensive domains through automated verififiability data synthesis while enabling verification of the LLM's reasoning process.
Outcome:	Extensive experiments show that the proposed framework enhances the reasoning of large language models in knowledge-intensive domains without significantly compromising the model’s general capabilities.

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Jie Zhou, Shenpo Dong, Hongkui Tu, Xiaodong Wang, Yong Dou

Information Extraction & Text Mining

Challenge:	Temporal relation extraction (TRE) is crucial for natural language understanding.
Approach:	They propose a Temporal Relational Structure Guided Temporal Relations Extraction task to extract relational structure features that can fit for both inter-sentence and intra-sentent relations.
Outcome:	The proposed method improves on two well-known datasets, MATRES and TB-Dense, and can be used for clinical diagnosis and summarization.

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

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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.

ROGRAG: A Robustly Optimized GraphRAG Framework (2025.acl-demo)

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Zhefan Wang, Huanjun Kong, Jie Ying, Wanli Ouyang, Nanqing Dong

TASO: Task-Aligned Sparse Optimization for Parameter-Efficient Model Adaptation (2025.emnlp-main)

Challenge:	Existing pipelines for large language models struggle with specialized or emerging topics which are rarely seen in the training corpus.
Approach:	They propose a multi-stage retrieval mechanism that integrates dual-level with logic form retrieval methods to improve retrieval robustness without increasing computational cost.
Outcome:	The proposed framework outperforms Qwen2.5-7B-Instruct and outperformed mainstream methods on seedbench and significantly improves the performance of each component.

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Daiye Miao, Yufang Liu, Jie Wang, Changzhi Sun, Yunke Zhang, Demei Yan, Shaokang Dong, Qi Zhang, Yuanbin Wu

Green & Sustainable NLP

Challenge:	Existing studies have shown that LoRA introduces substantial parameter redundancy, which not only increases the number of trainable parameters but also hinders the effectiveness of fine-tuning.
Approach:	They propose a method that leverages importance information from the pretrained model’s weights to mitigate LoRA redundancy.
Outcome:	The proposed method significantly reduces the number of trainable parameters required for task adaptation while providing a task-aligned perspective for LoRA redundancy reduction.

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

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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

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

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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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

Green & Sustainable NLP

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.

RAST: Domain-Robust Dialogue Rewriting as Sequence Tagging (2021.emnlp-main)

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Jie Hao, Linfeng Song, Liwei Wang, Kun Xu, Zhaopeng Tu, Dong Yu

Robustness in NLP

Challenge:	Existing models for dialogue rewriting suffer from the robustness issue, i.e., performances drop dramatically when testing on a different dataset.
Approach:	They propose a sequence-tagging-based approach that reduces the search space while preserving the core of the task.
Outcome:	The proposed model significantly reduces the search space while still covering the core of the task.

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

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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

SeedBench: A Multi-task Benchmark for Evaluating Large Language Models in Seed Science (2025.acl-long)

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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Jie Ying, Zihong Chen, Zhefan Wang, Wanli Jiang, Chenyang Wang, Zhonghang Yuan, Haoyang Su, Huanjun Kong, Fan Yang, Nanqing Dong

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

Challenge:	Seed science is essential for modern agriculture, but its application in seed science remains limited due to a shortage of experts and limited availability of online resources.
Approach:	They evaluate 26 leading large language models and compare them against a set of benchmarks . they find that there is a gap between the power of LLMs and real-world seed science problems .
Outcome:	The new seed benchmark highlights the gap between the power of large language models and real-world seed science problems.

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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

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

GenProve: Learning to Generate Text with Fine-Grained Provenance (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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Jingxuan Wei, Xingyue Wang, Yanghaoyu Liao, Jie Dong, Yuchen Liu, Caijun Jia, Bihui Yu, Junnan Zhu

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

Challenge:	Existing methods for large language models (LLMs) are coarse-grained and fail to distinguish between direct quotes and complex reasoning.
Approach:	They propose a framework that combines supervised fine-tuning and group relative policy optimization to generate fluent answers while simultaneously producing sentence-level provenance triples.
Outcome:	The proposed framework outperforms 14 strong large language models in joint evaluation.

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

Token-level Adaptive Training for Neural Machine Translation (2020.emnlp-main)

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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Shuhao Gu, Jinchao Zhang, Fandong Meng, Yang Feng, Wanying Xie, Jie Zhou, Dong Yu

Multilinguality

Challenge:	Existing token imbalance phenomenon in natural language as different tokens appear with different frequencies, which leads to different learning difficulties for tokens in Neural machine translation (NMT).
Approach:	They propose to assign tokens with different frequencies to target tokens during training to encourage the model to pay more attention to low-frequency tokens.
Outcome:	The proposed model yields consistent improvements on ZH-EN, EN-RO, and EN-DE translation tasks, especially on sentences that contain more low-frequency tokens.

Low-Confidence Gold: Refining Low-Confidence Samples for Efficient Instruction Tuning (2025.findings-emnlp)

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Hongyi Cai, Jie Li, Mohammad Mahdinur Rahman, Wenzhen Dong

Enhancing One-Shot Pruned Pre-trained Language Models through Sparse-Dense-Sparse Mechanism (2025.coling-main)

Challenge:	Low-Confidence Gold (LCG) is a new filtering framework for Large Language Models that curates high-quality subsets while preserving data diversity.
Approach:	They propose a new filtering framework that employs centroid-based clustering and confidence-guided selection for identifying valuable instruction pairs.
Outcome:	The proposed framework improves performance on a subset of 6K samples while maintaining data diversity.

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Guanchen Li, Xiandong Zhao, Lian Liu, Zeping Li, Yixing Xu, Dong Li, Lu Tian, Jie He, Ashish Sirasao, Emad Barsoum