Papers by Jiawei Liu

On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation (2021.acl-long)

Challenge:	Large language models (LLMs) are currently used to evaluate scientific papers by assigning an absolute score to each paper independently.
Approach:	They propose a comparison-native framework for paper evaluation that integrates comparison into both data construction and model learning.
Outcome:	The proposed framework achieves an average relative improvement of 21.8% over the strong baseline DeepReview-14B, while exhibiting robust generalization to five previously unseen datasets.

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Ruidan He, Linlin Liu, Hai Ye, Qingyu Tan, Bosheng Ding, Liying Cheng, Jiawei Low, Lidong Bing, Luo Si

HATA: Trainable and Hardware-Efficient Hash-Aware Top-k Attention for Scalable Large Model Inference (2025.findings-acl)

Challenge:	Existing studies have shown that adapter-based tuning is more parameter-efficient than fine-tuning.
Approach:	They propose to add adapter modules to a pretrained language model and update the parameters of adapter module when learning on a downstream task.
Outcome:	The proposed method outperforms fine-tuning on low-resource and cross-lingual tasks and settings.

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Ping Gong, Jiawei Yi, Shengnan Wang, Juncheng Zhang, Zewen Jin, Ouxiang Zhou, Ruibo Liu, Guanbin Xu, Youhui Bai, Bowen Ye, Kun Yuan, Tong Yang, Gong Zhang, Renhai Chen, Feng Wu, Cheng Li

Green & Sustainable NLP

Challenge:	Existing top-k attention methods struggle to strike a balance between efficiency and accuracy.
Approach:	They propose a top-k attention approach that integrates low-overhead techniques into the Top-k Attention process to achieve 7.2 speedup compared to vanilla full attention.
Outcome:	The proposed approach achieves 7.2 speedup compared to current top-k attention methods while maintaining model accuracy.

Flames: Benchmarking Value Alignment of LLMs in Chinese (2024.naacl-long)

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Kexin Huang, Xiangyang Liu, Qianyu Guo, Tianxiang Sun, Jiawei Sun, Yaru Wang, Zeyang Zhou, Yixu Wang, Yan Teng, Xipeng Qiu, Yingchun Wang, Dahua Lin

Understanding the Difficulty of Training Transformers (2020.emnlp-main)

Challenge:	Existing benchmarks for large language models (LLMs) do not accurately uncover safety vulnerabilities in LLMs.
Approach:	They propose a value alignment benchmark called Flames that encompasses both harmlessness principles and a unique morality dimension that integrates specific Chinese values such as harmony.
Outcome:	The proposed model performs poorly on Flames, particularly in safety and fairness dimensions.

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Liyuan Liu, Xiaodong Liu, Jianfeng Gao, Weizhu Chen, Jiawei Han

Towards a Unified Multi-Dimensional Evaluator for Text Generation (2022.emnlp-main)

Challenge:	Admin (Adaptive model initialization) is more stable, converges faster, and leads to better performance.
Approach:	They propose a model initialization algorithm to stabilize early training and unleash its full potential in the late stage.
Outcome:	The proposed model initialization method stabilizes early training and unleashes full potential in late stage.

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Ming Zhong, Yang Liu, Da Yin, Yuning Mao, Yizhu Jiao, Pengfei Liu, Chenguang Zhu, Heng Ji, Jiawei Han

Text Generation

Challenge:	Existing evaluation frameworks for natural language generation are dominated by similarity-based metrics.
Approach:	They propose a multi-dimensional evaluator for natural language generation that integrates multiple dimensions into one evaluer.
Outcome:	The proposed evaluator improves on three typical NLG tasks and improves with external knowledge.

ACR: Adaptive Context Refactoring via Context Refactoring Operators for Multi-Turn Dialogue (2026.findings-acl)

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Jiawei Shen, Jia Zhu, Hanghui Guo, Weijie Shi, Yue Cui, Qingyu Niu, Guoqing Ma, Jingjiang Liu, Yidan Liang, Yilin Wang, Shimin Di, Jiajie Xu

Dialogue Systems & Conversational Agents

Challenge:	Existing approaches to multi-turn dialogues lack contextual consistency and dependencies, and models struggle to maintain factual faithfulness as interaction turns increase.
Approach:	They propose an adaptive context refactoring framework that monitors and reshapes the interaction history to mitigate contextual inertia and state drift.
Outcome:	The proposed model outperforms baselines while reducing token consumption.

Adaptive Attentional Network for Few-Shot Knowledge Graph Completion (2020.emnlp-main)

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Jiawei Sheng, Shu Guo, Zhenyu Chen, Juwei Yue, Lihong Wang, Tingwen Liu, Hongbo Xu

Refining Source Representations with Relation Networks for Neural Machine Translation (C18-1)

Challenge:	Recent attempts to learn static representations of entities and references ignore their dynamic properties.
Approach:	They propose to learn static representations of entities and references ignoring their dynamic properties . a neighbor encoder learns entities' roles while a query-aware aggregator learns references' contributions .
Outcome:	The proposed approach achieves state-of-the-art results with different few-shot sizes.

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Wen Zhang, Jiawei Hu, Yang Feng, Qun Liu

CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training (2022.findings-naacl)

Challenge:	Existing neural machine translation frameworks that forget distant information and disregard relationship between source and target words are not effective.
Approach:	They propose to use relation networks to learn better representations of the source . they propose to associate source words with each other to help retain their relationships .
Outcome:	Experiments show that the proposed approach outperforms the encoder-decoder framework on several datasets.

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Xin Wang, Yasheng Wang, Yao Wan, Jiawei Wang, Pingyi Zhou, Li Li, Hao Wu, Jin Liu

Facet-Aware Evaluation for Extractive Summarization (2020.acl-main)

Challenge:	Recent studies have focused on code representation learning, which aims to represent the semantics of source code into distributed vectors.
Approach:	They propose to integrate different views with the natural-language description of source code into a unified framework with Multi-View contrastive Pre-training.
Outcome:	The proposed model outperforms state-of-the-art models on three downstream tasks over five datasets.

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Yuning Mao, Liyuan Liu, Qi Zhu, Xiang Ren, Jiawei Han

Text Generation

Challenge:	lexical overlap is a common evaluation metric for extractive summarization, but recent studies reveal its limitations.
Approach:	They propose a facet-aware evaluation setup for better assessment of information coverage in extractive summaries.
Outcome:	The proposed evaluation setup improves human correlation with extractive summarization datasets and improves comparative analysis.

ReTRE: Benchmarking LLM Transfer Robustness with Structure-Preserving Variants (2026.acl-long)

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ZhongDong Li, Weijie Shi, Yue Cui, Haolun MA, Yuanjun Liu, Jiawei Li, An Liu, Jia Zhu, Jiajie Xu

AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science (2025.findings-emnlp)

Challenge:	Learning transfer theory emphasizes that applying acquired knowledge to novel manifestations is a key signal of deep understanding
Approach:	They propose a benchmark that probes transfer robustness along two rewrite levels: Near Transfer and Far Transfer.
Outcome:	The proposed benchmark demonstrates that large language models are robust when faced with novel manifestations of the same problem.

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An Luo, Xun Xian, Jin Du, Fangqiao Tian, Ganghua Wang, Ming Zhong, Shengchun Zhao, Xuan Bi, Zirui Liu, Jiawei Zhou, Jayanth Srinivasa, Ashish Kundu, Charles Fleming, Mingyi Hong, Jie Ding

Toward Structured Knowledge Reasoning: Contrastive Retrieval-Augmented Generation on Experience (2025.findings-acl)

Challenge:	Large language models (LLMs) have advanced the automation of data science workflows, yet it remains unclear whether they can critically leverage external domain knowledge as human data scientists do in practice.
Approach:	They propose a benchmark to evaluate how large language models handle external domain knowledge in tabular prediction tasks.
Outcome:	The proposed model evaluates whether it can critically leverage external domain knowledge as human data scientists do in practice.

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Jiawei Gu, Ziting Xian, Yuanzhen Xie, Ye Liu, Enjie Liu, Ruichao Zhong, Mochi Gao, Yunzhi Tan, Bo Hu, Zang Li

Reaction Miner: An Integrated System for Chemical Reaction Extraction from Textual Data (2023.emnlp-demo)

Challenge:	Large language models struggle to infer implicit relationships embedded in tabular formats . authors introduce a framework that builds experience memory representations and enhances generalization through contrastive In-Context Learning (ICL).
Approach:	They propose a framework that builds experience memory representations and enhances generalization through contrastive In-Context Learning to simulate human-like knowledge transfer.
Outcome:	Experiments on Text-to-SQL and TableQA show CoRE significantly improves performance . it achieves gains of 3.44% and 4.24%, with up to 17.2% on challenging tasks .

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Ming Zhong, Siru Ouyang, Yizhu Jiao, Priyanka Kargupta, Leo Luo, Yanzhen Shen, Bobby Zhou, Xianrui Zhong, Xuan Liu, Hongxiang Li, Jinfeng Xiao, Minhao Jiang, Vivian Hu, Xuan Wang, Heng Ji, Martin Burke, Huimin Zhao, Jiawei Han

From Model-centered to Human-Centered: Revision Distance as a Metric for Text Evaluation in LLMs-based Applications (2024.findings-acl)

Challenge:	Reaction Miner is a system designed to extract chemical reactions from raw scientific PDFs.
Approach:	They propose a system that extracts chemical reactions directly from raw scientific PDFs.
Outcome:	The proposed system can extract chemical reactions from raw scientific PDFs.

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Yongqiang Ma, Lizhi Qing, Jiawei Liu, Yangyang Kang, Yue Zhang, Wei Lu, Xiaozhong Liu, Qikai Cheng

Navigating the Infinite Dynamic Web Space: Effective In-Context Exploration via Cognitive Multi-Agent Collaboration (2026.eacl-long)

Challenge:	Existing evaluation metrics for large language models yield numerical scores that ignore user experience.
Approach:	They propose a metric that suggests revision edits that mimic the human writing process . their results show that the metric offers more insightful feedback and distinguishes between texts .
Outcome:	The proposed metric can provide a self-explained text evaluation result in a human-understandable manner beyond the context-independent score.

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Guozhao Mo, Yanjiang Liu, Yafei Shi, Jiawei Chen, Yang Li, Yaojie Lu, Hongyu Lin, Ben He, Le Sun, Bo Zheng, Xianpei Han

Linguistics & Cognitive NLP

Challenge:	Existing methods for dynamic web navigation rely on greedy strategies or value estimation, struggle to achieve effective backtracking and are heavily dependent on proprietary models.
Approach:	They propose a cognitive multi-agent collaboration framework that enhances cyberspace exploration capability through In-Context Exploration.
Outcome:	The proposed framework surpasses the proprietary model Claude-3.5 Sonnet on the WebArena benchmark.

Conditional Dichotomy Quantification via Geometric Embedding (2025.acl-long)

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Shaobo Cui, Wenqing Liu, Yiyang Feng, Jiawei Zhou, Boi Faltings

Representation Learning

Challenge:	Existing methods that rely on semantic similarity fail to capture the nuanced oppositional dynamics essential for these applications.
Approach:	They propose a task that formalizes the measurement of conditional dichotomy by using a dichotomian framework.
Outcome:	The proposed framework provides carefully constructed datasets covering debate, defeasible inference, and causal reasoning scenarios.

Few-shot Named Entity Recognition with Self-describing Networks (2022.acl-long)

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Jiawei Chen, Qing Liu, Hongyu Lin, Xianpei Han, Le Sun

The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions (2023.emnlp-main)

Challenge:	Existing few-shot named entity recognition (NER) models capture information from limited instances while transferring useful knowledge from external resources.
Approach:	They propose a self-describing mechanism for few-shot NER which can universally describe mentions using concepts and automatically map novel entity types to concepts.
Outcome:	The proposed model can universally describe mentions using concepts and automatically map novel entity types to concepts and adaptively recognize entities on-demand.

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Siru Ouyang, Shuohang Wang, Yang Liu, Ming Zhong, Yizhu Jiao, Dan Iter, Reid Pryzant, Chenguang Zhu, Heng Ji, Jiawei Han

Efficient Contextualized Representation: Language Model Pruning for Sequence Labeling (D18-1)

Challenge:	Recent advances in large language models (LLMs) have produced models that exhibit remarkable performance across a variety of NLP tasks.
Approach:	They analyze a large-scale collection of user-GPT conversations to identify a significant gap between academic research in NLP and the needs of real-world NLP applications.
Outcome:	The proposed model outperforms existing models in a large-scale collection of user-GPT conversations and identifies a significant gap between the tasks that users frequently request from LLMs and the tasks commonly studied in academic research.

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Liyuan Liu, Xiang Ren, Jingbo Shang, Xiaotao Gu, Jian Peng, Jiawei Han

VPL: Visual Proxy Learning Framework for Zero-Shot Medical Image Diagnosis (2024.findings-emnlp)

Challenge:	Existing efforts to train pre-trained language models have brought significant improvements to various NLP applications.
Approach:	They propose to compress bulky LMs while preserving useful information for a specific task.
Outcome:	The proposed method can detach any layer without affecting others, and stretch shallow and wide LMs to be deep and narrow.

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Jiaxiang Liu, Tianxiang Hu, Huimin Xiong, Jiawei Du, Yang Feng, Jian Wu, Joey Zhou, Zuozhu Liu

Extractive Summarization via ChatGPT for Faithful Summary Generation (2023.findings-emnlp)

Challenge:	Insufficient medical text precision and the modal disparity between text and vision spaces pose challenges for vision-language models like CLIP.
Approach:	They propose a visual proxy learning framework that combines a text refinement module and a stable Sinkhorn algorithm to enhance the diagnostic performance.
Outcome:	The proposed model outperforms the state-of-the-art CLIP inference by 1.69% to 15.31% on five datasets covering various diseases.

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Revealing and Mitigating the Challenge of Detecting Character Knowledge Errors in LLM Role-Playing (2025.emnlp-main)

Challenge:	Abstractive summarization methods struggle with generating ungrammatical or even nonfactual contents.
Approach:	They evaluate ChatGPT's performance on extractive summarization and compare it with traditional fine-tuning methods on benchmark datasets.
Outcome:	The proposed pipeline performs better than abstractive methods on summary faithfulness and in-context learning.

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Wenyuan Zhang, Shuaiyi Nie, Jiawei Sheng, Zefeng Zhang, Xinghua Zhang, Yongquan He, Tingwen Liu

Chain-of-Thought Prompting Obscures Hallucination Cues in Large Language Models: An Empirical Evaluation (2025.findings-emnlp)

Challenge:	Existing studies on large language models (LLMs) fail to detect character knowledge errors, leading to low-quality automatic corpus construction.
Approach:	They propose to use a large language model to detect known knowledge errors and an agent-based reasoning method to improve error detection.
Outcome:	The proposed method improves the ability of LLMs to detect errors in known knowledge errors and unknown knowledge errors while playing roles.

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Jiahao Cheng, Tiancheng Su, Jia Yuan, Guoxiu He, Jiawei Liu, Xinqi Tao, Jingwen Xie, Huaxia Li

Event Causality Extraction with Event Argument Correlations (2022.coling-1)

Challenge:	Chain-of-Thought (CoT) prompting can mitigate hallucinations by encouraging step-by-step reasoning, but its impact on halluciation detection remains underexplored.
Approach:	They conduct an empirical evaluation of CoT prompting in Large Language Models (LLMs) to examine their impact on hallucination detection methods.
Outcome:	The proposed method significantly affects the internal states and token probability distributions of the LLM.

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Shiyao Cui, Jiawei Sheng, Xin Cong, Quangang Li, Tingwen Liu, Jinqiao Shi

Syntactic Text Processing

Challenge:	Event Causality Identification (ECI) ignores crucial event structure and cause-effect component information, making it struggle for downstream applications.
Approach:	They propose a task to extract event causality pairs with their structured event information from plain text.
Outcome:	The proposed method captures the intra- and inter-event argument correlations for ECE and provides several future directions.

\mathcal XFT: Unlocking the Power of Code Instruction Tuning by Simply Merging Upcycled Mixture-of-Experts (2024.acl-long)

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Yifeng Ding, Jiawei Liu, Yuxiang Wei, Lingming Zhang

CLaSp: In-Context Layer Skip for Self-Speculative Decoding (2025.acl-long)

Challenge:	Existing studies focus on the data perspectives of instruction tuning, leaving room for exploring advanced training schemes.
Approach:	They argue that prior works overlook the possibility of improving code instruction tuning by advancing existing training schemes.
Outcome:	The proposed model is dense because all parameters are activated to predict the next token (assuming it is a decoder-only LLM).

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Longze Chen, Renke Shan, Huiming Wang, Lu Wang, Ziqiang Liu, Run Luo, Jiawei Wang, Hamid Alinejad-Rokny, Min Yang

Challenge:	Existing methods for drafting Large Language Models require additional modules to be trained, which can be challenging to implement and ensure compatibility across various LLMs.
Approach:	They propose an in-context layer-skipping strategy for self-speculative decoding that uses a plug-and-play mechanism to skip intermediate layers of the verify model to construct a compressed draft model.
Outcome:	The proposed method achieves a speedup of 1.3 1.7 on LLaMA3 series models without altering the original distribution of the generated text.

RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios (2022.naacl-demo)

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Xinya Du, Zixuan Zhang, Sha Li, Pengfei Yu, Hongwei Wang, Tuan Lai, Xudong Lin, Ziqi Wang, Iris Liu, Ben Zhou, Haoyang Wen, Manling Li, Darryl Hannan, Jie Lei, Hyounghun Kim, Rotem Dror, Haoyu Wang, Michael Regan, Qi Zeng, Qing Lyu, Charles Yu, Carl Edwards, Xiaomeng Jin, Yizhu Jiao, Ghazaleh Kazeminejad, Zhenhailong Wang, Chris Callison-Burch, Mohit Bansal, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Martha Palmer, Heng Ji

SummIt: Iterative Text Summarization via ChatGPT (2023.findings-emnlp)

Challenge:	Existing methods for event prediction are incomplete and noisy.
Approach:	They propose to use news-related event schemas to extract newsworthy events . they build a demo website and include a video demonstrating the framework .
Outcome:	The proposed framework can be applied to a wide variety of newsworthy scenarios.

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Toward Secure Tuning: Mitigating Security Risks from Instruction Fine-Tuning (2026.acl-long)

Challenge:	Existing text summarization systems generate summaries in a single step, but are often inadequate due to the issue of hallucination and the lack of accuracy.
Approach:	They propose an iterative text summarization framework based on large language models like ChatGPT that refines the generated summary iterativly through self-evaluation and feedback.
Outcome:	The proposed framework refines the generated summary iteratively through self-evaluation and feedback, closely resembling the iteration humans undertake when drafting and revising summaries.

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Yanrui Du, Fenglei Fan, Sendong Zhao, Jiawei Cao, Ming Ma, Danyang Zhao, Shuren Qi, Ting Liu, Bing Qin

DependEval: Benchmarking LLMs for Repository Dependency Understanding (2025.findings-acl)

Challenge:	Instruction Fine-Tuning (IFT) has emerged as a critical technique for customizing Large Language Models (LLMs) however, recent studies have revealed that IFT can compromise the built-in security mechanisms of LLMs, posing significant security risks.
Approach:	They propose a method that shifts learning burden onto security-robust parameters and propose 'warm-up' phase that preferentially trains Mods_Rob to learn low-level features with minimal security risk.
Outcome:	The proposed method reduces security risks without sacrificing performance gains across knowledge-intensive datasets.

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Junjia Du, Yadi Liu, Hongcheng Guo, Jiawei Wang, Haojian Huang, Yunyi Ni, Zhoujun Li

Challenge:	a benchmark is designed to evaluate the repository-level dependency understanding of large language models (LLMs) based on 2683 repositories from real-world websites.
Approach:	They propose a benchmark to evaluate repository dependency understanding for large language models . DEPENDEVAL evaluates models on three core tasks across 8 programming languages .
Outcome:	The benchmark evaluates models on three core tasks across 8 programming languages from real-world repositories.

Fundamental Capabilities of Large Language Models and their Applications in Domain Scenarios: A Survey (2024.acl-long)

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Jiawei Li, Yizhe Yang, Yu Bai, Xiaofeng Zhou, Yinghao Li, Huashan Sun, Yuhang Liu, Xingpeng Si, Yuhao Ye, Yixiao Wu, 林一冠林一冠, Bin Xu, Ren Bowen, Chong Feng, Yang Gao, Heyan Huang

Inner Thinking Transformer: Leveraging Dynamic Depth Scaling to Foster Adaptive Internal Thinking (2025.acl-long)

Challenge:	Existing benchmark-based evaluations cannot accurately reflect the performance of real-world applications.
Approach:	They propose a reliable strategy for domains to choose more robust LLMs for real-world applications.
Outcome:	The proposed strategy addresses the challenges faced by domains to choose more robust LLMs for real-world applications.

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Yilong Chen, Junyuan Shang, Zhenyu Zhang, Yanxi Xie, Jiawei Sheng, Tingwen Liu, Shuohuan Wang, Yu Sun, Hua Wu, Haifeng Wang

T2A-Feedback: Improving Basic Capabilities of Text-to-Audio Generation via Fine-grained AI Feedback (2025.acl-long)

Challenge:	Large language models face inherent performance bottlenecks under parameter constraints . challenging tokens induce abrupt gradient spikes across layers, exposing stress points .
Approach:	They propose an inner thinking transformer that reimagines layer computations as implicit thinking steps.
Outcome:	Empirical results show that ITT outperforms Transformer/Loop variants in 11 benchmarks.

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Zehan Wang, Ke Lei, Chen Zhu, Jiawei Huang, Sashuai Zhou, Luping Liu, Xize Cheng, Shengpeng Ji, Zhenhui Ye, Tao Jin, Zhou Zhao

COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation (2021.naacl-demos)

Challenge:	Text-to-audio (T2A) models still struggle to satisfy human preferences for prompt-following and acoustic quality when generating complex multi-event audio.
Approach:	They propose to use AI feedback learning to enhance basic capabilities of text-to-audio models . they use a large audio preference dataset to evaluate the model's capabilities .
Outcome:	The proposed model improves in simple and complex scenarios with AI feedback learning.

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Qingyun Wang, Manling Li, Xuan Wang, Nikolaus Parulian, Guangxing Han, Jiawei Ma, Jingxuan Tu, Ying Lin, Ranran Haoran Zhang, Weili Liu, Aabhas Chauhan, Yingjun Guan, Bangzheng Li, Ruisong Li, Xiangchen Song, Yi Fung, Heng Ji, Jiawei Han, Shih-Fu Chang, James Pustejovsky, Jasmine Rah, David Liem, Ahmed ELsayed, Martha Palmer, Clare Voss, Cynthia Schneider, Boyan Onyshkevych

Structured Data in NLP

Challenge:	a new framework to digest relevant biomedical knowledge is needed to combat COVID-19 . quantity of research results is a bottleneck, and false information promoted in publications .
Approach:	a team of researchers has developed a framework to extract multimedia knowledge elements from scientific literature to combat COVID-19.
Outcome:	a new framework extracts fine-grained multimedia knowledge elements from scientific literature . it provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence . the framework is based on a case study of drug repurposing .

CriticLean: Critic-Guided Reinforcement Learning for Mathematical Formalization (2026.acl-long)

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Zhongyuan Peng, Yifan Yao, Kaijing Ma, Shuyue Guo, Yizhe Li, Yichi Zhang, Chenchen Zhang, Yifan Zhang, Zhouliang Yu, Luming Li, Minghao Liu, Yihang Xia, Jiawei Shen, Yuchen Wu, Yixin Cao, Zhaoxiang Zhang, Wenhao Huang, Jiaheng Liu, Ge Zhang

GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022.emnlp-demos)

Challenge:	Existing approaches to formalizing mathematical statements face limitations in accuracy, especially in the context of complex, highlevel problems that involve sophisticated mathematical reasoning.
Approach:	They propose a CriticLean framework that elevates the role of the critic from a passive validator to an active learning component and introduce a benchmark to measure models’ ability to distinguish semantically correct from incorrect formalizations.
Outcome:	The proposed framework outperforms open- and closed-source benchmarks and shows that it significantly outperformed existing models.

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Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina Mcmillan-major, Anna Shvets, Ashish Upadhyay, Bernd Bohnet, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez Beltrachini, Leonardo F . R. Ribeiro, Lewis Tunstall, Li Zhang, Mahim Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou

Text Generation

Challenge:	Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work.
Approach:	They propose to use the Generation, Evaluation, and Metrics Benchmark to integrate new evaluation methods into existing evaluations.
Outcome:	The proposed evaluation infrastructure bridges the gap between the advantages of leaderboards and in-depth and evolving evaluations by allowing model developers to benefit from each other's work.

RAG4ITOps: A Supervised Fine-Tunable and Comprehensive RAG Framework for IT Operations and Maintenance (2024.emnlp-industry)

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Tianyang Zhang, Zhuoxuan Jiang, Shengguang Bai, Tianrui Zhang, Lin Lin, Yang Liu, Jiawei Ren

Natural Language Interfaces

Challenge:	Large Language Models (LLMs) have improved the open-domain QA’s performance, but how to efficiently handle enterprise-exclusive corpora and build domain-specific QA systems are still not studied for industrial applications.
Approach:	They propose a general and comprehensive framework based on Retrieval Augmented Generation (RAG) and facilitate the whole business process of establishing QA systems for IT operations and maintenance.
Outcome:	The proposed framework achieves superior results on two kinds of QA tasks.

CasEE: A Joint Learning Framework with Cascade Decoding for Overlapping Event Extraction (2021.findings-acl)

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Jiawei Sheng, Shu Guo, Bowen Yu, Qian Li, Yiming Hei, Lihong Wang, Tingwen Liu, Hongbo Xu

Chem-FINESE: Validating Fine-Grained Few-shot Entity Extraction through Text Reconstruction (2024.findings-eacl)

Challenge:	Existing methods assume that events appear in sentences without overlaps . overlapping event extraction is a challenging task in natural language understanding .
Approach:	They propose a joint learning framework with cascade decoding for overlapping event extraction . they sequentially perform type detection, trigger extraction and argument extraction based on the specific former prediction .
Outcome:	The proposed framework improves on a public event extraction benchmark . it sequentially performs type detection, trigger extraction and argument extraction .

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Qingyun Wang, Zixuan Zhang, Hongxiang Li, Xuan Liu, Jiawei Han, Huimin Zhao, Heng Ji

Long-form Hallucination Detection with Self-elicitation (2025.findings-acl)

Challenge:	Existing frameworks for fine-grained few-shot entity extraction are difficult to implement in the chemical domain due to the information overload of scientific papers.
Approach:	They propose a sequence-to-sequence based few-shot entity extraction approach . it uses a seq2seq entity extractor and a self-validation module to reconstruct original input sentence .
Outcome:	The proposed framework achieves 8.26% and 6.84% performance gains on two datasets.

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Zihang Liu, Jiawei Guo, Hao Zhang, Hongyang Chen, Jiajun Bu, Haishuai Wang

HEGEL: Hypergraph Transformer for Long Document Summarization (2022.emnlp-main)

Challenge:	Existing methods for hallucination detection tend to decompose text into isolated statements, unable to understand contextual semantics.
Approach:	They propose a framework to leverage self-generated thoughts derived from prior statements as catalysts to elicit the expression of intrinsic knowledge and understand contextual semantics.
Outcome:	The proposed framework enables self-elicitation to elicit expressions of knowledge and understand semantics.

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Improving Distantly-Supervised Named Entity Recognition with Self-Collaborative Denoising Learning (2021.findings-emnlp)

Challenge:	Abstract: Extractive summarization for long documents is challenging due to the extended structured input context.
Approach:	They propose a hypergraph neural network for extractive summarization by capturing cross-sentence relations.
Outcome:	The proposed model can capture cross-sentence relations and latent topics and keywords coreference, and section structure, and can be applied to scientific papers.

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Xinghua Zhang, Bowen Yu, Tingwen Liu, Zhenyu Zhang, Jiawei Sheng, Xue Mengge, Hongbo Xu

On the Transformer Growth for Progressive BERT Training (2021.naacl-main)

Challenge:	Named entity recognition (NER) is a method of detecting entity spans and classifying them into predefined categories.
Approach:	They propose a method to iteratively perform noisy label refinery by using self-collaborative denoising learning.
Outcome:	The proposed learning paradigm exploits reliable labels and communicates with unreliable annotations by collaborative denoising.

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Xiaotao Gu, Liyuan Liu, Hongkun Yu, Jing Li, Chen Chen, Jiawei Han

Dynosaur: A Dynamic Growth Paradigm for Instruction-Tuning Data Curation (2023.emnlp-main)

Challenge:	Existing methods only conduct network growth in a single dimension, but compound growth operators are beneficial for multiple dimensions.
Approach:	They propose a method to train BERT progressively using a Transformer model and explore alternative growth operators in each dimension via controlled comparison.
Outcome:	The proposed method speeds up BERT pre-training by 73.6% and 82.2% for the base and large models respectively while achieving comparable performances.

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Da Yin, Xiao Liu, Fan Yin, Ming Zhong, Hritik Bansal, Jiawei Han, Kai-Wei Chang

EVIDENCEMINER: Textual Evidence Discovery for Life Sciences (2020.acl-demos)

Challenge:	Existing methods for instruction tuning do not include associating instructions with existing datasets.
Approach:	They propose a dynamic growth paradigm for the automatic curation of instruction-tuning data . they use existing datasets to automatically construct instruction-uning datasets .
Outcome:	The proposed model reduces the API cost for generating instructions and provides high-quality data.

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Xuan Wang, Yingjun Guan, Weili Liu, Aabhas Chauhan, Enyi Jiang, Qi Li, David Liem, Dibakar Sigdel, John Caufield, Peipei Ping, Jiawei Han

Information Retrieval

Challenge:	EVIDENCEMINER is a web-based system that allows users to query a natural language statement and retrieve textual evidence from a background corpora for life sciences.
Approach:	They propose a web-based system that lets users query a natural language statement and automatically retrieves textual evidence from a background corpora for life sciences.
Outcome:	EVIDENCEMINER is a web-based system that lets users query a natural language statement and automatically retrieves textual evidence from a background corpora for life sciences.

From Language to Driving: A Dual-Loop SLM-Enhanced Framework for Multi-Planner Scheduling via a Domain-Specific Language (2026.acl-long)

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Jiawei Liu, Xun Gong, Muli Yang, Xingrui Yu, Fen Fang, Xulei Yang, Ivor Tsang, Yunfeng hu, Hong Chen, Qing Guo

Challenge:	Recent large language model-based AD research offers new avenues to address this challenge.
Approach:	They propose a small language model (SLM) for high-level semantic reasoning and schedule generation, while an inner loop performs low-level, high-frequency schedule execution and vehicle control.
Outcome:	The proposed framework improves instruction completion rates while maintaining high safety and compliance relative to multiple baselines.

Training ELECTRA Augmented with Multi-word Selection (2021.findings-acl)

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Jiaming Shen, Jialu Liu, Tianqi Liu, Cong Yu, Jiawei Han

CrossWeigh: Training Named Entity Tagger from Imperfect Annotations (D19-1)

Challenge:	Existing pre-training methods for NLP tasks require massive computation resources.
Approach:	They propose a method that trains a discriminator to detect replaced tokens and select original tokens from candidate sets.
Outcome:	The proposed method improves ELECTRA based on multi-task learning on GLUE and SQUAD datasets.

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Zihan Wang, Jingbo Shang, Liyuan Liu, Lihao Lu, Jiacheng Liu, Jiawei Han

RSDA: Restoring Stale Data Affinity via Dynamic Renovation Strategy for Mitigating Data Scarcity (2026.acl-long)

Challenge:	Named entity recognition (NER) models can identify labels in 5.38% of test sentences . a framework to handle label mistakes during NER model training is proposed .
Approach:	They propose a framework to manually correct label mistakes in named entity recognition (NER) they aim to improve the accuracy of models by re-evaluating popular models on corrected test sets .
Outcome:	The proposed framework can detect label mistakes in 5.38% of test sentences . the proposed framework improves on three datasets with a high-performance model .

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Yidan Liang, Jia Zhu, Weijie Shi, Hanghui Guo, Yue Cui, Jiawei Shen, Guoqing Ma, Jingjiang Liu, Qingyu Niu, Yilin Wang, Shimin Di, Jiajie Xu

Generation-Augmented Retrieval for Open-Domain Question Answering (2021.acl-long)

Challenge:	High-quality data is the cornerstone of advancing large language models, but the supply of premium data is nearing depletion, while vast stale corpora remain underutilized.
Approach:	They propose a framework to restore stale data affinity by quantifying the latent value of samples and employing a dynamic renovation strategy selection mechanism to determine the optimal component-level strategy.
Outcome:	The proposed framework achieves performance improvements using less than 10% of the data volume, underscoring that the latent potential of stale corpora remains largely untapped.

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Yuning Mao, Pengcheng He, Xiaodong Liu, Yelong Shen, Jianfeng Gao, Jiawei Han, Weizhu Chen

Natural Language Interfaces

Challenge:	Existing approaches to answer open-domain questions use sparse representations and sparsity.
Approach:	They propose a method which augments a query by generating relevant contexts from heuristically discovered contexts without external supervision.
Outcome:	The proposed approach outperforms state-of-the-art dense retrieval methods on natural questions and triviaQA datasets.

KCVR: Knowledge-Centric Video Reconstruction for Structured Pedagogical Summarization via Dynamic Graph Planning (2026.acl-long)

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Jingjiang Liu, Jia Zhu, Hanghui Guo, Weijie Shi, Yue Cui, Xiaokang Jin, Yilin Wang, Qingyu Niu, Jiawei Shen, Guoqing Ma, Yidan Liang, Shimin Di, Jiajie Xu

Reliability-aware Dynamic Feature Composition for Name Tagging (P19-1)

Challenge:	Existing summarization methods compress content for gist browsing, but they break prerequisite logic in instructional videos.
Approach:	They propose a framework that decouples epistemic planning from content generation.
Outcome:	The proposed framework outperforms strong end-to-end baselines on Knowledge Progression Consistency and Learning Objective Coverage.

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Ying Lin, Liyuan Liu, Heng Ji, Dong Yu, Jiawei Han

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

Challenge:	Word embeddings are used to encode semantic information, but their quality is not consistent across the vocabulary due to the long-tail distribution of word frequency.
Approach:	They propose a reliability-aware name tagging model that uses word frequency to indicate word quality . they propose to use word frequency-based reliability signals to dynamically select and compose features .
Outcome:	The proposed model outperforms the baseline model on OntoNotes 5.0 and up to 5% gain on cross-genre data sets.

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

MedCoT: Medical Chain of Thought via Hierarchical Expert (2024.emnlp-main)

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

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Jiaxiang Liu, Yuan Wang, Jiawei Du, Joey Zhou, Zuozhu Liu

Improving Distantly-Supervised Named Entity Recognition with Self-Collaborative Denoising Learning (2021.emnlp-main)

Challenge:	Existing methods for medical visual question answering lack robustness and reasoning paths for real-world medical diagnostics.
Approach:	They propose a hierarchical expert verification reasoning chain method to enhance interpretability and accuracy in medical visual question answering.
Outcome:	The proposed method outperforms existing methods on four standard Med-VQA datasets.

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Xinghua Zhang, Bowen Yu, Tingwen Liu, Zhenyu Zhang, Jiawei Sheng, Xue Mengge, Hongbo Xu

A Role-Selected Sharing Network for Joint Machine-Human Chatting Handoff and Service Satisfaction Analysis (2021.emnlp-main)

Challenge:	Named entity recognition (NER) is a method of detecting entity spans and classifying them into predefined categories.
Approach:	They propose a method to iteratively perform noisy label refinery by using self-collaborative denoising learning.
Outcome:	The proposed learning paradigm exploits reliable labels and communicates with unreliable annotations by collaborative denoising.

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Jiawei Liu, Kaisong Song, Yangyang Kang, Guoxiu He, Zhuoren Jiang, Changlong Sun, Wei Lu, Xiaozhong Liu

Dialogue Systems & Conversational Agents

Challenge:	Recent efforts to predict chatbot failure hatches vital apprehensions due to complexity of human conversation.
Approach:	They propose a model that integrates dialogue satisfaction estimation and handoff prediction in one multi-task learning framework.
Outcome:	The proposed model integrates dialogue satisfaction estimation and handoff prediction in one multi-task learning framework.

Sparse-to-Dense: A Free Lunch for Lossless Acceleration of Video Understanding in LLMs (2025.acl-short)

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Xuan Zhang, Cunxiao Du, Sicheng Yu, Jiawei Wu, Fengzhuo Zhang, Wei Gao, Qian Liu

CMIG: Conceptual Metaphor Theory-Inspired Framework for Metaphorical Image Generation (2026.findings-acl)

Challenge:	Recent advances in Video Large Language Models (Video-LLMs) have achieved exceptional performance on tasks like video question answering and captioning.
Approach:	They propose a decoding strategy that leverages sparse top-K attention and dense full attention to accelerate Video-LLMs without loss.
Outcome:	The proposed approach achieves a 1.94 walltime speedup in video processing.

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Qingbao Huang, Cheng Yang, Jiawei Yao, Zhiyue Liu, Yi Cai, Xingmao Zhang

CoLLiE: Collaborative Training of Large Language Models in an Efficient Way (2023.emnlp-demo)

Challenge:	Existing text-to-image systems often produce visually plausible but semantically literal outputs.
Approach:	They propose a structured prompting framework inspired by Conceptual Metaphor Theory . they propose to identify source–target mappings, filter projectable source attributes and select a visual realization strategy in a reproducible reasoning workflow.
Outcome:	The proposed framework improves semantic alignment and controllability on metaphor prompts.

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Kai Lv, Shuo Zhang, Tianle Gu, Shuhao Xing, Jiawei Hong, Keyu Chen, Xiaoran Liu, Yuqing Yang, Honglin Guo, Tengxiao Liu, Yu Sun, Qipeng Guo, Hang Yan, Xipeng Qiu

Optimal Transport Guided Correlation Assignment for Multimodal Entity Linking (2024.findings-acl)

Challenge:	Large language models (LLMs) are increasingly pivotal in a wide range of tasks . however, the resources required for training these models necessitate efficient solutions .
Approach:	They propose a library that facilitates collaborative training of large language models . they use 3D parallelism, parameter-efficient fine-tuning methods and optimizers .
Outcome:	The proposed library has proven superior training efficiency in comparison with prevalent solutions in pre-training and fine-tuning scenarios.

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Zefeng Zhang, Jiawei Sheng, Zhang Chuang, Liangyunzhi Liangyunzhi, Wenyuan Zhang, Siqi Wang, Tingwen Liu

Learning Named Entity Tagger using Domain-Specific Dictionary (D18-1)

Challenge:	Existing methods to link ambiguous mentions to entities in multimodal knowledge graphs rely on partial correlations.
Approach:	They propose a framework that leverages multi-element correlations to bridge modality gap and enable fine-grained semantic matching by exploiting correlation between multimodal features and entities.
Outcome:	The proposed framework outperforms state-of-the-art models and confirms the effectiveness of the proposed method.

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Jingbo Shang, Liyuan Liu, Xiaotao Gu, Xiang Ren, Teng Ren, Jiawei Han

PSP: Pre-trained Soft Prompts for Few-Shot Abstractive Summarization (2022.coling-1)

Challenge:	Existing methods to build reliable named entity recognition systems require large amounts of manually-labeled training data.
Approach:	They propose a revised fuzzy CRF layer to handle tokens with multiple possible labels to address noisy distant supervision.
Outcome:	The proposed model can handle tokens with multiple possible labels under the traditional framework and improves on the existing model with a new Tie or Break scheme.

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Xiaochen Liu, Yang Gao, Yu Bai, Jiawei Li, Yinan Hu, Heyan Huang, Boxing Chen

Incorporating Latent Meanings of Morphological Compositions to Enhance Word Embeddings (P18-1)

Challenge:	Experimental results show that our method outperforms full-model tuning in few-shot abstractive summarization tasks.
Approach:	They propose a soft prompts architecture with prompt pre-training and prompt fine-tuning paradigm to support few-shot abstractive summarization.
Outcome:	The proposed model outperforms Prompt Tuning and Profix-Tuning on CNN/DailyMail and XSum datasets and outperfies Profix Tuning by a large margin.

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Yang Xu, Jiawei Liu, Wei Yang, Liusheng Huang

HyperMem: Hypergraph Memory for Long-Term Conversations (2026.acl-long)

Challenge:	Existing word embedding methods learn semantic information at word level while neglecting meaningful inner structures of words like morphemes.
Approach:	They propose to use latent meanings of morphological compositions of words to train word embeddings.
Outcome:	The proposed models outperform baseline models on word similarity, syntactic analogy and text classification tasks.

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Juwei Yue, Chuanrui Hu, Jiawei Sheng, Zuyi Zhou, Wenyuan Zhang, Tingwen Liu, Li Guo, Yafeng Deng

Natural Language Interfaces

Challenge:	Existing approaches to long-term memory management rely on pairwise relations, causing fragmented retrieval.
Approach:	They propose a hypergraph-based hierarchical memory architecture that explicitly models high-order associations using hyperedges.
Outcome:	Experiments show that HyperMem achieves state-of-the-art performance with 92.73% accuracy for long-term conversations.

Unsupervised Multi-Granularity Summarization (2022.findings-emnlp)

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Ming Zhong, Yang Liu, Suyu Ge, Yuning Mao, Yizhu Jiao, Xingxing Zhang, Yichong Xu, Chenguang Zhu, Michael Zeng, Jiawei Han

EquivPruner: Boosting Efficiency and Quality in LLM-Based Search via Action Pruning (2026.acl-long)

Challenge:	Experimental results confirm the substantial superiority of GranuSum on multi-granularity summarization over strong baselines.
Approach:	They propose to rank events by their salience and annotate a benchmark for GranuSum that contains multiple summaries at different granularities for each document cluster.
Outcome:	The proposed framework is capable of producing multi-granular summaries in unsupervised manner over strong baselines.

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Jiawei Liu, Qisi Chen, Jianshu Zhang, Quan Liu, Defu Lian

LCO: LLM-based Constraint Optimization for Safer Agentic LLMs in Real-world Tasks (2026.findings-acl)

Challenge:	Existing semantic similarity methods struggle to accurately identify semantically equivalent steps in domain-specific contexts like mathematical reasoning.
Approach:	They propose a simple yet effective approach that identifies and prunes semantically equivalent actions during LLM reasoning search.
Outcome:	The proposed approach reduces token consumption while improving reasoning efficiency and accuracy on Qwen2.5-Math-7B-Instruct on GSM8K.

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Jiayong Wan, Jiawei Chen, Zhaoxia Yin, Liu Shuyuan, Hang Su

Adaptive and Robust Translation from Natural Language to Multi-model Query Languages (2025.acl-long)

Challenge:	Existing defense methods are insufficient to address in-context reward hacking (ICRH), where LLMs iteratively optimize their behavior to maximize proxy objectives, resulting in harmful side effects.
Approach:	They propose a framework that reduces in-context reward hacking (ICRH) through repeated interactions with the environment.
Outcome:	The proposed framework reduces ICRH without model fine-tuning while maintaining task performance.

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Gengyuan Shi, Chaokun Wang, Liu Yabin, Jiawei Ren

DiffuSum: Generation Enhanced Extractive Summarization with Diffusion (2023.findings-acl)

Challenge:	Multi-model databases and polystore systems are increasingly studied for managing multi-model data holistically.
Approach:	They propose an adaptive Text-to-MMQL framework that includes a schema embedding module and an MMQl representation strategy to generate concise intermediate query formats with error correction in generated queries.
Outcome:	The proposed framework achieves over 9% accuracy improvement over baseline methods.

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LIME: Less Is More for MLLM Evaluation (2025.findings-acl)

Challenge:	Existing methods for extractive summarization are formulated as a sequence labeling problem by making individual sentence label predictions.
Approach:	They propose a novel paradigm for extractive summarization by directly generating summary sentences with diffusion models and extracting sentences based on sentence representation matching.
Outcome:	The proposed framework achieves state-of-the-art extractive results on CNN/DailyMail with ROUGE scores of 44.83/22.56/40.56.

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King Zhu, Qianbo Zang, Shian Jia, Siwei Wu, Feiteng Fang, Yizhi Li, Shuyue Guo, Tianyu Zheng, Jiawei Guo, Bo Li, Haoning Wu, Xingwei Qu, Jian Yang, Ruibo Liu, Xiang Yue, Jiaheng Liu, Chenghua Lin, Hamid Alinejad-Rokny, Min Yang, Shiwen Ni, Wenhao Huang, Ge Zhang

Harnessing and Evaluating the Intrinsic Extrapolation Ability of Large Language Models for Vehicle Trajectory Prediction (2025.naacl-long)

Challenge:	Existing MLLM benchmarks and unified evaluation frameworks cannot accurately and efficiently reflect the ability of MLMLs.
Approach:	They propose a semi-automated benchmark curated using a pipeline that filters out uninformative samples and eliminates answer leakage by focusing on tasks that require image-based understanding.
Outcome:	The proposed benchmark reduces the number of samples by 76% and evaluation time by 77% while it can more effectively distinguish different models’ abilities.

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Jiawei Liu, Yanjiao Liu, Xun Gong, Tingting Wang, Hong Chen, Yunfeng Hu

Improving Reasoning Capabilities in Small Models through Mixture-of-layers Distillation with Stepwise Attention on Key Information (2025.emnlp-main)

Challenge:	Emergent abilities of large language models (LLMs) have advanced their application in autonomous vehicle research.
Approach:	They propose a framework that leverages LLMs’ built-in extrapolation capabilities for vehicle trajectory prediction, enabling them to understand traffic agents' behavior and interactions over time.
Outcome:	The proposed framework enables off-the-shelf, frozen LLMs to achieve competitive trajectory prediction performance with qualitative analyses revealing their enhanced understanding of complex, multi-agent traffic scenarios.

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Yao Chen, Jiawei Sheng, Wenyuan Zhang, Tingwen Liu

Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering (2022.acl-long)

Challenge:	Existing methods focus on transferring teacher-generated rationales to student models, but do not explore teachers’ dynamic attention towards critical information during reasoning.
Approach:	They propose a method that transfers the teacher’s stepwise attention on key information to the student model and a Mixture of Layers module that allows dynamic alignment between the teacher and student.
Outcome:	The proposed framework achieves consistent performance improvements across multiple mathematical and commonsense reasoning datasets.

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Jiawei Zhou, Xiaoguang Li, Lifeng Shang, Lan Luo, Ke Zhan, Enrui Hu, Xinyu Zhang, Hao Jiang, Zhao Cao, Fan Yu, Xin Jiang, Qun Liu, Lei Chen

Re3: Relevance & Recency Retrieval for Mitigating Temporal Hallucination (2026.acl-long)

Challenge:	Existing methods to train dense passage retrieval have a large data gap between upstream and downstream relevance.
Approach:	They propose a method to pre-train the dense retriever with the text relevance induced by hyperlinks within Web documents.
Outcome:	The proposed method outperforms existing methods under different scenarios and in the open-domain question answering domain.

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Jiawei Cao, Jie Ouyang, Mingyue Cheng, Zhaomeng Zhou, Chunli Liu, Yupeng Li, Zirui Liu, Shijin Wang