Papers by Jing Zhao

LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing (2024.emnlp-main)

Challenge:	Multi-level implicit discourse relation recognition (MIDRR) aims at identifying hierarchical discourse relations among arguments.
Approach:	They propose a prompt-based multi-level implicit discourse relation recognition framework that leverages parameter-efficient prompt tuning to drive inputted arguments to match the pre-trained space.
Outcome:	The proposed framework achieves comparable results on PDTB 2.0 and 3.0 using about 0.1% trainable parameters compared with baselines.

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Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing, Cheng Jiayang, Zhaowei Wang, Ying Su, Raj Shah, Ruohao Guo, Jing Gu, Haoran Li, Kangda Wei, Zihao Wang, Lu Cheng, Surangika Ranathunga, Meng Fang, Jie Fu, Fei Liu, Ruihong Huang, Eduardo Blanco, Yixin Cao, Rui Zhang, Philip Yu, Wenpeng Yin

Challenge:	a comparative analysis of paper (meta-)reviews by large language models (LLMs) aims to identify and distinguish LLMs from human activities .
Approach:	They present a comparative analysis to identify and distinguish LLM activities from human activities.
Outcome:	The proposed analysis aims to improve recognition of instances when someone implicitly uses LLMs for reviewing activities.

Adaptive Policy with Wait-k Model for Simultaneous Translation (2023.emnlp-main)

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Libo Zhao, Kai Fan, Wei Luo, Wu Jing, Shushu Wang, Ziqian Zeng, Zhongqiang Huang

Machine Translation

Challenge:	Existing approaches to simultaneous machine translation require a robust read/write policy . a standalone multi-path wait-k model performs competitively with adaptive policies .
Approach:	They propose a more flexible approach by decoupling the adaptive policy model from the translation model.
Outcome:	The proposed approach outperforms baseline approaches in translation tasks.

CroAno : A Crowd Annotation Platform for Improving Label Consistency of Chinese NER Dataset (2021.emnlp-demo)

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Baoli Zhang, Zhucong Li, Zhen Gan, Yubo Chen, Jing Wan, Kang Liu, Jun Zhao, Shengping Liu, Yafei Shi

Information Extraction & Text Mining

Challenge:	Existing crowd annotation tools for named entity recognition (NER) focus on efficiency and don't consider consistency of datasets.
Approach:	They propose a crowd annotation platform for Chinese named entity recognition (NER) CroAno provides a systematic solution for improving label consistency of Chinese NER datasets.
Outcome:	The proposed platform improves label consistency of Chinese NER datasets.

Beyond Literal Descriptions: Understanding and Locating Open-World Objects Aligned with Human Intentions (2024.findings-acl)

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Wenxuan Wang, Yisi Zhang, Xingjian He, Yichen Yan, Zijia Zhao, Xinlong Wang, Jing Liu

Visual Data in NLP

Challenge:	Existing methods for visual grounding rely on the assumption that the given expression must be literal . this impedes the practical deployment of agents in real-world scenarios.
Approach:	They propose a visual grounding task that uses intention expressions to locate foreground entities . they build a large-scale IVG dataset with free-form intention expression to promote VG .
Outcome:	The proposed method is based on a large-scale intention-driven visual-language (V-L) dataset with free-form intention expressions.

TOME: A Two-stage Approach for Model-based Retrieval (2023.acl-long)

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Ruiyang Ren, Wayne Xin Zhao, Jing Liu, Hua Wu, Ji-Rong Wen, Haifeng Wang

SGG: Learning to Select, Guide, and Generate for Keyphrase Generation (2021.naacl-main)

Challenge:	Recent research has focused on model-based retrieval, which discards the index in the traditional retrieval model and memorizes the candidate corpora using model parameters.
Approach:	They propose a model-based retrieval approach that discards the index in the traditional retrieval model and memorizes the candidate corpora using model parameters.
Outcome:	The proposed approach eliminates the index in the traditional retrieval model and memorizes the candidate corpora using model parameters.

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Jing Zhao, Junwei Bao, Yifan Wang, Youzheng Wu, Xiaodong He, Bowen Zhou

Text Generation

Challenge:	Existing keyphrase generation approaches synchronously generate present and absent keyphrases without explicitly distinguishing these two categories.
Approach:	They propose to deal with present and absent keyphrases separately with different mechanisms by using a hierarchical neural network with a pointing-based selector and a selection-guided generator.
Outcome:	The proposed model outperforms baselines on four keyphrase generation tasks and shows extensibility in natural language generation tasks.

Conditioned Masked Language and Image Modeling for Image-Text Dense Retrieval (2022.findings-emnlp)

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Ziyang Luo, Yadong Xi, Rongsheng Zhang, GongZheng Li, Zeng Zhao, Jing Ma

BlendFilter: Advancing Retrieval-Augmented Large Language Models via Query Generation Blending and Knowledge Filtering (2024.emnlp-main)

Challenge:	Large-scale two-stream pre-trained models like CLIP have achieved tremendous success in image-text retrieval.
Approach:	They propose a cross-modal framework for image-text retrieval using two-stream pre-trained models . they embed images and texts into instance representations with two separate encoders . experimental results on MSCOCO and Flickr30k reveal the effectiveness of their framework .
Outcome:	The proposed framework improves image-text retrieval performance on two popular cross-modal retrieval benchmarks.

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Haoyu Wang, Ruirui Li, Haoming Jiang, Jinjin Tian, Zhengyang Wang, Chen Luo, Xianfeng Tang, Monica Cheng, Tuo Zhao, Jing Gao

CodeJudge-Eval: Can Large Language Models be Good Judges in Code Understanding? (2025.coling-main)

Challenge:	Retrieval-augmented Large Language Models struggle with complex inputs and noisy knowledge retrieval hindering model effectiveness.
Approach:	They propose a query generation method that integrates query generation blending with knowledge filtering to enhance retrieval-augmented LLMs.
Outcome:	The proposed approach surpasses state-of-the-art benchmarks on open-domain question answering benchmarks.

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Yuwei Zhao, Ziyang Luo, Yuchen Tian, Hongzhan Lin, Weixiang Yan, Annan Li, Jing Ma

MAGNET: Augmenting Generative Decoders with Representation Learning and Infilling Capabilities (2025.acl-long)

Challenge:	Recent advances in large language models (LLMs) have showcased impressive code generation capabilities, primarily evaluated through language-to-code benchmarks.
Approach:	They propose a benchmark to assess LLMs’ code understanding abilities from the perspective of code judging rather than code generation.
Outcome:	The proposed benchmark evaluates 12 well-known large language models to determine the correctness of provided code solutions.

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Savya Khosla, Aditi Tiwari, Kushal Kafle, Simon Jenni, Handong Zhao, John Collomosse, Jing Shi

Enhancing Chain-of-Thought Reasoning via Neuron Activation Differential Analysis (2025.emnlp-main)

Challenge:	Decoder-only large language models are increasingly being adapted for bidirectional modeling . however, their reliance on causal attention restricts their effectiveness in tasks that require understanding of bidirectional context.
Approach:	They propose a method to adapt decoder-only large language models to generate robust representations and infill missing text spans.
Outcome:	The proposed method surpasses strong decoders on token-level and sentence-level representation learning tasks and generates contextually appropriate text infills without excessive repetition of words or phrases.

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Yiru Tang, Kun Zhou, Yingqian Min, Xin Zhao, Jing Sha, Zhichao Sheng, Shijin Wang

Reasoning

Challenge:	Existing studies focus on optimizing external components of CoT, but lack internal explanations for the quality of the model's outputs.
Approach:	They propose an efficient method to identify reasoning-critical neurons by analyzing their activation patterns under reasoning chains of varying quality.
Outcome:	The proposed method shows that neurons in the feed-forward layers are critical in the generation of high-quality reasoning chains.

An Empirical Study of LLM-as-a-Judge for LLM Evaluation: Fine-tuned Judge Model is not a General Substitute for GPT-4 (2025.findings-acl)

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Hui Huang, Xingyuan Bu, Hongli Zhou, Yingqi Qu, Jing Liu, Muyun Yang, Bing Xu, Tiejun Zhao

Muffin or Chihuahua? Challenging Multimodal Large Language Models with Multipanel VQA (2024.acl-long)

Challenge:	Recent studies have fine-tuned judge models based on open-source LLMs to evaluate the quality of other LLM.
Approach:	They propose to use open-source LLMs to evaluate Large Language Models (LLMs) their empirical results show that the models underperform GPT-4 in several dimensions .
Outcome:	The proposed models outperform GPT-4 on several dimensions including generalizability, fairness and adaptability.

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Yue Fan, Jing Gu, Kaiwen Zhou, Qianqi Yan, Shan Jiang, Ching-Chen Kuo, Yang Zhao, Xinze Guan, Xin Wang

PsFuture: A Pseudo-Future-based Zero-Shot Adaptive Policy for Simultaneous Machine Translation (2024.emnlp-main)

Challenge:	Multipanel images are a common form of visual representations, and humans can achieve approximately 99% accuracy on these questions.
Approach:	They propose a benchmark that tests multipanel visual reasoning models with 6,600 triplets of questions, answers, and multipanel images.
Outcome:	The proposed benchmark features 6,600 triplets of questions, answers, and multipanel images that challenge state-of-the-art Multimodal Large Language Models (MLLMs) human users can attain approximately 99% accuracy on these questions, compared with previous benchmarks.

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Libo Zhao, Jing Li, Ziqian Zeng

Text Generation

Challenge:	Simultaneous machine translation (SiMT) requires target tokens to be generated in real-time as streaming source tokens are consumed.
Approach:	They propose a zero-shot adaptive read/write policy for siMT that generates target tokens concurrently as streaming source tokens are consumed.
Outcome:	The proposed policy achieves performance on par with strong baselines and the P2F method can further enhance performance.

VQA-Augmented Machine Translation with Cross-Modal Contrastive Learning (2025.findings-emnlp)

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Zhihui Zhang, Shiliang Sun, Jing Zhao, Tengfei Song, Hao Yang

M3-VQA: A Benchmark for Multimodal, Multi-Entity, Multi-Hop Visual Question Answering (2026.acl-long)

Challenge:	Existing multimodal machine translation methods often extract visual features using pre-trained models while learning text features from scratch, leading to representation imbalance.
Approach:	They propose a cross-modal VQA-augmented multimodal machine translation method . it aligns image-source text pairs and image-question text pairs through dual-text contrastive learning .
Outcome:	The proposed method outperforms state-of-the-art methods on multiple evaluation metrics.

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Jiatong Ma, Longteng Guo, Yuchen Liu, Zijia Zhao, Dongze Hao, Xuanxu Lin, Jing Liu

Towards Understanding Gender Bias in Relation Extraction (2020.acl-main)

Challenge:	Existing knowledge-based VQA benchmarks focus on coarse-grained categories and simple reasoning over single entities.
Approach:	They propose a knowledge-based Visual Question Answering benchmark to enhance multimodality evaluation.
Outcome:	The proposed benchmark improves evaluation of multimodal large language models in fine-grained multimodal entity understanding and complex multihop reasoning.

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Andrew Gaut, Tony Sun, Shirlyn Tang, Yuxin Huang, Jing Qian, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, William Yang Wang

Information Extraction & Text Mining

Challenge:	Existing bias mitigation techniques have a negative effect on NRE, a study finds .
Approach:	They create a dataset to analyze gender bias in relation extraction systems . they find that existing bias mitigation techniques have a negative effect on NRE .
Outcome:	The proposed dataset analyzes gender bias in relation extraction systems using a 10% human annotated test set.

IndiVec: An Exploration of Leveraging Large Language Models for Media Bias Detection with Fine-Grained Bias Indicators (2024.findings-eacl)

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Luyang Lin, Lingzhi Wang, Xiaoyan Zhao, Jing Li, Kam-Fai Wong

TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities (2023.acl-demo)

Challenge:	Existing studies on social media bias detection focus on fine-tuning models specific to particular datasets and testing them on corresponding test sets.
Approach:	They propose a general bias detection framework, IndiVec, built upon large language models and vector databases.
Outcome:	The proposed framework outperforms baseline methods on four political bias datasets and provides explicit top-k indicators to interpret bias predictions.

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Zhe Zhao, Yudong Li, Cheng Hou, Jing Zhao, Rong Tian, Weijie Liu, Yiren Chen, Ningyuan Sun, Haoyan Liu, Weiquan Mao, Han Guo, Weigang Gou, Taiqiang Wu, Tao Zhu, Wenhang Shi, Chen Chen, Shan Huang, Sihong Chen, Liqun Liu, Feifei Li, Xiaoshuai Chen, Xingwu Sun, Zhanhui Kang, Xiaoyong Du, Linlin Shen, Kimmo Yan

Fine- and Coarse-Granularity Hybrid Self-Attention for Efficient BERT (2022.acl-long)

Challenge:	Several pre-training models of different modalities are showing a rising trend of homogeneity in their model structures.
Approach:	They propose a toolkit that supports pre-training models of different modalities.
Outcome:	The proposed toolkit can match the performance of the original implementations on text, vision, and audio benchmarks.

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Jing Zhao, Yifan Wang, Junwei Bao, Youzheng Wu, Xiaodong He

FEED PETs: Further Experimentation and Expansion on the Disambiguation of Potentially Euphemistic Terms (2023.starsem-1)

Challenge:	Transformer-based pre-trained models achieve state-of-the-art results, but they can be prohibitively costly.
Approach:	They propose a fine- and coarse-granularity hybrid self-attention that shortens the computational sequence length in self- attention by progressively shortening the computational time.
Outcome:	The proposed model reduces computation cost by shortening the computational sequence length in self-attention.

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Patrick Lee, Iyanuoluwa Shode, Alain Trujillo, Yuan Zhao, Olumide Ojo, Diana Plancarte, Anna Feldman, Jing Peng

CodeHacker: Automated Test Case Generation for Detecting Vulnerabilities in Competitive Programming Solutions (2026.acl-long)

Challenge:	Existing work on euphemism disambiguation tasks has focused on transformers . euphorias are expressions that soften the message they convey, therefore dictionary-based approaches are ineffective .
Approach:	They propose to annotate PETs for vagueness and use transformers to classify PETs . they perform euphemism disambiguation experiments in three different languages .
Outcome:	The proposed models perform well in English euphemism disambiguation task . preliminary results will be used to launch future work .

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Jingwei Shi, Xinxiang Yin, Jing Huang, Shengyu Tao, Jinman Zhao

Robustness in NLP

Challenge:	Existing benchmarks for Large Language Models often lack coverage for subtle corner cases . a substantial amount of effort has been applied to address this challenge .
Approach:	They propose a framework that generates adversarial test cases that expose latent vulnerabilities in code submissions.
Outcome:	The proposed framework improves the True Negative Rate (TNR) of existing datasets and generates superior adversarial cases on liveCodeBench.

PAIR: Leveraging Passage-Centric Similarity Relation for Improving Dense Passage Retrieval (2021.findings-acl)

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Ruiyang Ren, Shangwen Lv, Yingqi Qu, Jing Liu, Wayne Xin Zhao, QiaoQiao She, Hua Wu, Haifeng Wang, Ji-Rong Wen

DISCO Balances the Scales: Adaptive Domain- and Difficulty-Aware Reinforcement Learning on Imbalanced Data (2025.findings-emnlp)

Challenge:	Recent studies only consider query-centric similarity relation when learning the dual-encoder retriever.
Approach:	They propose a query-centric and PAssage-centric approach to capture more comprehensive similarity relations for dense passage retrieval.
Outcome:	The proposed approach significantly outperforms existing models on both MSMARCO and Natural Questions datasets.

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Yuhang Zhou, Jing Zhu, Shengyi Qian, Zhuokai Zhao, Xiyao Wang, Xiaoyu Liu, Ming Li, Paiheng Xu, Wei Ai, Furong Huang

HadSkip: Homotopic and Adaptive Layer Skipping of Pre-trained Language Models for Efficient Inference (2023.findings-emnlp)

Challenge:	Large Language Models (LLMs) are increasingly aligned with human preferences through Reinforcement Learning from Human Feedback (RLHF).
Approach:	a new study proposes a domain-informed self-consistency policy optimization extension to GRPO that addresses inter-group imbalance.
Outcome:	a new extension of GRPO addresses inter-group imbalance with two key innovations . the proposed method outperforms existing GR PO variants by 5% on Qwen3 models .

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Haoyu Wang, Yaqing Wang, Tianci Liu, Tuo Zhao, Jing Gao

MMATH: A Multilingual Benchmark for Mathematical Reasoning (2025.findings-emnlp)

Challenge:	Existing methods to exit pre-trained language models suffer from the limitation that they have to sequentially traverse through all layers prior to the selected exit layer, which degrades their performance.
Approach:	They propose a homotopic and adaptive layer skipping fine-tuning method that adaptively selects the layers to skip based on a predefined budget.
Outcome:	The proposed method outperforms all state-of-the-art baselines on the GLUE benchmark and shows that it is highly efficient.

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Wenyang Luo, Xin Zhao, Jing Sha, Shijin Wang, Ji-Rong Wen

Reasoning

Challenge:	a benchmark for multilingual complex reasoning spans 374 high-quality math problems across 10 typologically diverse languages.
Approach:	They propose a benchmark for multilingual complex reasoning across 10 languages . they show reasoning in English and answering in target languages can enhance performance .
Outcome:	The proposed benchmark demonstrates that models with high-quality reasoning can perform in multiple languages.

ClinicalTrialsHub: Bridging Registries and Literature for Comprehensive Clinical Trial Access (2026.eacl-demo)

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Jiwoo Park, Ruoqi Liu, Avani Jagdale, Andrew Srisuwananukorn, Jing Zhao, Lang Li, Ping Zhang, Sachin Kumar

Question Answering

Challenge:	ClinicalTrialsHub consolidates clinical trial data from ClinicalTrial.gov and augments it by extracting and structuring trial-relevant information from PubMed.
Approach:	They propose a search-focused platform that consolidates PubMed data and extracts structured trial information.
Outcome:	ClinicalTrialsHub increases access to structured clinical trial data by 83.8% compared to ClinicalTrial.gov alone.

Imagination and Contemplation: A Balanced Framework for Semantic-Augmented Multimodal Machine Translation (2025.findings-emnlp)

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Zhuang Yu, Shiliang Sun, Jing Zhao, Tengfei Song, Hao Yang

Evaluating Cognitive-Behavioral Fixation via Multimodal User Viewing Patterns on Social Media (2025.emnlp-main)

Challenge:	Multimodal Machine Translation (MMT) is effective in resolving linguistic ambiguities, but visual information often introduces redundancy or noise, potentially impairing translation quality.
Approach:	They propose a semantic-augmented framework that integrates "Imagination" and "Contemplation" they first generate synthetic images from source text and align them with authentic images via an optimal transport loss .
Outcome:	The proposed framework outperforms baselines on translation datasets with visually ambiguous or weakly correlated content.

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Yujie Wang, Yunwei Zhao, Jing Yang, Han Han, Shiguang Shan, Jie Zhang

Text Detoxification: Data Efficiency, Semantic Preservation and Model Generalization (2025.emnlp-main)

Challenge:	Digital media platforms often contribute to cognitive-behavioral fixation, a phenomenon in which users exhibit sustained and repetitive engagement with narrow content domains.
Approach:	They propose a multimodal topic extraction module and a cognitive-behavioral fixation quantification module that collaboratively enable adaptive, hierarchical, and interpretable assessment of user behavior.
Outcome:	The proposed framework lays the groundwork for scalable computational analysis of cognitive fixation.

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Jing Yu, Yibo Zhao, Jiapeng Zhu, Wenming Shao, Bo Pang, Zhao Zhang, Xiang Li

MEDs for PETs: Multilingual Euphemism Disambiguation for Potentially Euphemistic Terms (2024.findings-eacl)

Challenge:	Existing methods for detoxification of text often rely on manually annotated data . xiangli: "detoxification of texts is a powerful way to remove toxic content"
Approach:	They propose a reinforcement learning framework that optimizes detoxification and semantic preservation without annotating large amounts of data.
Outcome:	The proposed method overcomes major limitations and surpasses humanannotated references across multiple benchmarks.

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Patrick Lee, Alain Chirino Trujillo, Diana Cuevas Plancarte, Olumide Ojo, Xinyi Liu, Iyanuoluwa Shode, Yuan Zhao, Anna Feldman, Jing Peng

Multilinguality

Challenge:	Euphemisms are a linguistic device used to soften or neutralize language that may otherwise be harsh or awkward to state directly.
Approach:	They train a multilingual transformer model to disambiguate potentially euphemistic terms in multilingual and cross-lingual settings.
Outcome:	The proposed model performs better than monolingual models on the disambiguation task compared to monolingual ones in multilingual and cross-lingual settings.

Personalizing LLMs with Binary Feedback: A Preference-Calibrated Optimization Framework (2026.acl-long)

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Xilai Ma, Liye Zhao, Weijun Yao, Haibing Di, Wenya Wang, Jing Li

Challenge:	Existing methods focus on isolated user histories, neglecting the essential role of inter-user differences.
Approach:	They propose a framework that personalizes Large Language Models via preference-calibrated binary signals.
Outcome:	The proposed framework outperforms baselines in a variety of personalization tasks and backbone LLMs.

RoseLoRA: Row and Column-wise Sparse Low-rank Adaptation of Pre-trained Language Model for Knowledge Editing and Fine-tuning (2024.emnlp-main)

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Haoyu Wang, Tianci Liu, Ruirui Li, Monica Cheng, Tuo Zhao, Jing Gao

Reinforced Informativeness Optimization for Long-Form Retrieval-Augmented Generation (2026.acl-long)

Challenge:	Pre-trained language models have strong generalizability, but fine-tuning involves updating all parameters, rendering full fine-uning resource-intensive.
Approach:	They propose a parameter-efficient fine-tuning method that updates all pre-trained parameters during inference.
Outcome:	The proposed method outperforms baseline methods on five benchmarks across 20 datasets.

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Yuhao Wang, Ruiyang Ren, Yucheng Wang, Xin Zhao, Jing Liu, Hua Wu, Haifeng Wang

ECC: Synergizing Emotion, Cause and Commonsense for Empathetic Dialogue Generation (2025.coling-main)

Challenge:	Existing reinforcement learning systems lack verifiable reward mechanisms for long-form question answering . current systems lack reliable long-term answers due to lack of factual content .
Approach:	They propose a framework for reinforced verifiable informativeness optimization . it defines informativeness as measurable and externally verifier objective for RL .
Outcome:	Experiments show that RioRAG achieves higher factual recall and faithfulness . the proposed framework is based on a framework that uses nugget-centric verification with cross-source checks .

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Xu Wang, Bo Wang, Yihong Tang, Dongming Zhao, Jing Liu, Ruifang He, Yuexian Hou

Dialogue Systems & Conversational Agents

Challenge:	Empathy improves human-machine dialogue systems by enhancing the user's experience.
Approach:	They propose a framework that leverages specialized encoders to capture the key features of emotion, cause, and commonsense and collaboratively models these through a Conditional Variational Auto-Encoder.
Outcome:	Empirical results show that the framework outperforms baseline models and offers a robust solution for empathetic dialogue generation.

RoleBreak: Character Hallucination as a Jailbreak Attack in Role-Playing Systems (2025.coling-main)

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Yihong Tang, Bo Wang, Xu Wang, Dongming Zhao, Jing Liu, Ruifang He, Yuexian Hou

A Fine-grained Chinese Software Privacy Policy Dataset for Sequence Labeling and Regulation Compliant Identification (2022.emnlp-main)

Challenge:	Existing approaches to combat character hallucination are vulnerable to attack . large language models (LLMs) are capable of generating responses inconsistent with intended personas .
Approach:	They propose a novel defence strategy that generates supplemental context through narration to mitigate role-query conflicts and improve query generalization.
Outcome:	The proposed defence strategy outperforms refusal-based strategies in character hallucinations and query generalization.

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Kaifa Zhao, Le Yu, Shiyao Zhou, Jing Li, Xiapu Luo, Yat Fei Aemon Chiu, Yutong Liu

Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation (2025.coling-main)

Challenge:	Existing datasets that ignore law requirements are limited to English.
Approach:	They construct a Chinese privacy policy dataset that can be used to analyze software privacy policies.
Outcome:	The proposed dataset includes 483 Chinese Android privacy policies, over 11K sentences, and 52K fine-grained annotations.

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Ruiyang Ren, Yuhao Wang, Yingqi Qu, Wayne Xin Zhao, Jing Liu, Hua Wu, Ji-Rong Wen, Haifeng Wang

Large Dual Encoders Are Generalizable Retrievers (2022.emnlp-main)

Challenge:	Large language models (LLMs) have shown impressive prowess in solving a wide range of tasks with world knowledge, but it remains unclear how well they perceive their factual knowledge boundaries.
Approach:	They propose to use a retrieval augmentation approach to enhance LLMs' awareness of factual knowledge boundaries to analyze factual and factual information in open-domain question answering (QA)
Outcome:	The proposed method improves LLMs’ QA and judgemental capabilities by integrating supporting documents with the questions.

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Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernandez Abrego, Ji Ma, Vincent Zhao, Yi Luan, Keith Hall, Ming-Wei Chang, Yinfei Yang

RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking (2021.emnlp-main)

Challenge:	Experimental results show that dual encoders outperform sparse and dense retrievers on the BEIR dataset significantly.
Approach:	They challenge belief that bottleneck layer is too limited for out-of-domain generalization . they scale up the model while keeping bottleneck as a single dot-product with a fixed size .
Outcome:	The proposed model outperforms sparse and dense retrievers on the BEIR dataset significantly.

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Ruiyang Ren, Yingqi Qu, Jing Liu, Wayne Xin Zhao, QiaoQiao She, Hua Wu, Haifeng Wang, Ji-Rong Wen

LegalDrill: Diagnosis-Driven Synthesis for Legal Reasoning in Small Language Models (2026.acl-industry)

Challenge:	Recent studies show that passage retrieval and passage reranking are important for achieving mutual improvement.
Approach:	They propose a unified listwise training approach for passage retrieval and passage reranking that incorporates a retrieval procedure and a hybrid data augmentation strategy.
Outcome:	The proposed approach improves on both MSMARCO and Natural Questions datasets.

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Tianchun Li, Haochen Liu, Vishwa Pardeshi, Xingchen Wang, Tianci Liu, Huijun Zhao, Wei Fan, Jing Gao

Continual Pre-training of Language Models for Math Problem Understanding with Syntax-Aware Memory Network (2022.acl-long)

Challenge:	Small language models (SLMs) are promising for real-world deployment but struggle with high-stakes legal reasoning tasks.
Approach:	They propose a diagnostic-driven synthesis framework that extracts and refines reasoning trajectories from a capable teacher via fine-grained prompting and a self-reflective verification is employed to adaptively select the most effective data for the SLM student.
Outcome:	The proposed framework extracts and refines reasoning trajectories from a capable teacher via fine-grained prompting, then a self-reflective verification is employed to adaptively select the most effective data for the student.

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Zheng Gong, Kun Zhou, Xin Zhao, Jing Sha, Shijin Wang, Ji-Rong Wen

Seeking Common but Distinguishing Difference, A Joint Aspect-based Sentiment Analysis Model (2021.emnlp-main)

Challenge:	a fundamental challenge in modeling math problems is how to fuse semantics of textual description and formulas.
Approach:	They propose a method to continually pre-train language models for improving understanding of math problems with syntax-aware memory networks.
Outcome:	The proposed approach outperforms competitive baselines on four math tasks.

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Hongjiang Jing, Zuchao Li, Hai Zhao, Shu Jiang

Information Extraction & Text Mining

Challenge:	Existing models focus on aspect term extraction, opinion term extraction and sentiment polarity classification but ignore the difference.
Approach:	They propose a joint aspect-based sentiment analysis task that focuses on the difference between the two tasks to improve the model's robustness.
Outcome:	Empirical results show that the proposed model outperforms the previous state-of-the-art on four benchmark datasets.

Impromptu Cybercrime Euphemism Detection (2025.coling-main)

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Xiang Li, Yucheng Zhou, Laiping Zhao, Jing Li, Fangming Liu

Text Classification

Challenge:	Existing methods for detecting euphemisms are ineffective in impromptu euphorism detection . Existing approaches for e-mail detection are limited to word-level ephemismals .
Approach:	They propose a framework for impromptu euphemism detection that integrates context augmentation and multi-round iterative training to better predict the actual meaning of a masked token.
Outcome:	The proposed framework improves 76-fold over the previous state-of-the-art euphemism detector.

REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering (2024.emnlp-main)

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Yuhao Wang, Ruiyang Ren, Junyi Li, Xin Zhao, Jing Liu, Ji-Rong Wen

MapGuide: A Simple yet Effective Method to Reconstruct Continuous Language from Brain Activities (2024.naacl-long)

Challenge:	Existing methods to extend knowledge scope of large language models (LLMs) lack internal parametric knowledge, resulting in misusing external knowledge.
Approach:	They propose a retrieval-augmented approach that provides LLMs with potentially relevant documents through a module.
Outcome:	The proposed approach outperforms existing methods on four open-domain QA tasks.

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Xinpei Zhao, Jingyuan Sun, Shaonan Wang, Jing Ye, Xhz Xhz, Chengqing Zong

Representation Learning

Challenge:	Decoding continuous language from brain activity is a formidable but promising field of research . previous attempts to map brain activity to text relied on learning to encode brain activity .
Approach:	They propose a method that maps brain activity to text embeddings by directly comparing them with predicted brain responses.
Outcome:	The proposed method outperforms the current state-of-the-art model showing improvements on BLEU and METEOR scores.

DrFrattn: Directly Learn Adaptive Policy from Attention for Simultaneous Machine Translation (2025.emnlp-main)

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Libo Zhao, Jing Li, Ziqian Zeng

Machine Translation

Challenge:	Existing approaches to learn read/write policies from attention mechanism may compromise effectiveness of attention mechanism .
Approach:	They propose a method that directly learns adaptive policies from the attention mechanism . experimental results demonstrate that the method achieves an improved balance between translation accuracy and latency.
Outcome:	The proposed method achieves improved balance between translation accuracy and latency.

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

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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

RoR: Read-over-Read for Long Document Machine Reading Comprehension (2021.findings-emnlp)

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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Jing Zhao, Junwei Bao, Yifan Wang, Yongwei Zhou, Youzheng Wu, Xiaodong He, Bowen Zhou

Machine Reading Comprehension

Challenge:	Existing models for machine reading comprehension are limited to individual chunks due to encoding length constraint.
Approach:	They propose a read-over-read method that expands the reading field from chunk to document by predicting regional answers for each chunk.
Outcome:	Extensive experiments on QuAC and TriviaQA show that the proposed model performs well for long document reading.

OPERA: Operation-Pivoted Discrete Reasoning over Text (2022.naacl-main)

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Yongwei Zhou, Junwei Bao, Chaoqun Duan, Haipeng Sun, Jiahui Liang, Yifan Wang, Jing Zhao, Youzheng Wu, Xiaodong He, Tiejun Zhao

A Thorough Examination on Zero-shot Dense Retrieval (2023.findings-emnlp)

Challenge:	Existing methods to predict logical forms ignore the utilization of symbolic operations and lack reasoning ability and interpretability.
Approach:	They propose an operation-pivoted discrete reasoning framework that uses symbolic operations as neural modules to facilitate reasoning ability and interpretability.
Outcome:	Extensive experiments on DROP and RACENum datasets show the reasoning ability of OPERA.

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Ruiyang Ren, Yingqi Qu, Jing Liu, Xin Zhao, Qifei Wu, Yuchen Ding, Hua Wu, Haifeng Wang, Ji-Rong Wen

CoreEval: Automatically Building Contamination-Resilient Datasets with Real-World Knowledge toward Reliable LLM Evaluation (2025.acl-long)

Challenge:	Recent advances in dense retrieval (DR) models have been shown to be not as competitive as traditional sparse retrieval models in a zero-shot retrieval setting.
Approach:	They propose to examine the zero-shot capability of DR models by analyzing key factors related to source training set and potential bias from target dataset.
Outcome:	The proposed model is not as competitive as sparse retrieval models in a zero-shot retrieval setting.

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Jingqian Zhao, Bingbing Wang, Geng Tu, Yice Zhang, Qianlong Wang, Bin Liang, Jing Li, Ruifeng Xu

Self-Evaluation of Large Language Model based on Glass-box Features (2024.findings-emnlp)

Challenge:	Publicly available datasets can be used to evaluate performance of large language models . however, contamination of test data can artificially inflate model performance .
Approach:	They propose a Contamination-resilient Evaluation strategy that updates data with real-world knowledge.
Outcome:	The proposed evaluation strategy can be used to update datasets with real-world knowledge.

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Hui Huang, Yingqi Qu, Jing Liu, Muyun Yang, Bing Xu, Tiejun Zhao, Wenpeng Lu

BASES: Large-scale Web Search User Simulation with Large Language Model based Agents (2024.findings-emnlp)

Challenge:	Existing evaluation methods rely on external evaluators, focusing on training and prompting strategies, but model-aware glass-box features are overlooked.
Approach:	They propose to use model-aware glass-box features to evaluate an LLM's output.
Outcome:	The proposed model-aware features are reliable quality indicators for self-evaluation on public benchmarks.

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Ruiyang Ren, Peng Qiu, Yingqi Qu, Jing Liu, Xin Zhao, Hua Wu, Ji-Rong Wen, Haifeng Wang

KnowLA: Enhancing Parameter-efficient Finetuning with Knowledgeable Adaptation (2024.naacl-long)

Challenge:	Existing research on web search rely on real-user experiments, which can be costly to scale up.
Approach:	They propose a user simulation framework with LLM-based agents that can generate unique user profiles at scale.
Outcome:	The proposed framework can generate unique user profiles at scale, leading to diverse search behaviors.

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Xindi Luo, Zequn Sun, Jing Zhao, Zhe Zhao, Wei Hu

P3LM: Probabilistically Permuted Prophet Language Modeling for Generative Pre-Training (2022.findings-emnlp)

Challenge:	Existing methods for parameter-efficient finetuning (PEFT) are limited and only finetune a small number of parameters using limited instruction data.
Approach:	They propose a method that inserts an adaptation layer into an LLM to integrate embeddings of entities appearing in the input text.
Outcome:	The proposed method can activate parameterized knowledge in an LLM without changing its parameters or input prompts.

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Junwei Bao, Yifan Wang, Ying Jiangyong, Yeyun Gong, Jing Zhao, Youzheng Wu, Xiaodong He

Incorporating Linguistic Constraints into Keyphrase Generation (P19-1)

Challenge:	Existing autoregressive left-to-right (L2R) models are limited to unidirectional information and constrained on strong local dependencies.
Approach:	They propose a probabilistically permuted prophet language model which strengthens the modeling of bidirectional information and long token dependencies for sequence generation.
Outcome:	Experiments on GLGE dataset show that P3LM improves on natural language generation tasks.

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Jing Zhao, Yuxiang Zhang

LUNA: Learning Slot-Turn Alignment for Dialogue State Tracking (2022.naacl-main)

Challenge:	Existing keyphrase generation methods generate overlapping phrases (including sub-phrases or super-phrase) Existing methods are far from satisfactory for a wide range of natural language processing tasks.
Approach:	They propose a parallel Seq2Seq network with coverage attention to alleviate the overlapping phrase problem by integrating linguistic constraints of keyphrase into the basic Seq2-Sequeq network on the source side and employ the multi-task learning framework on the target side.
Outcome:	The proposed method outperforms the state-of-the-art CopyRNN on scientific datasets and is also more effective in news domain.

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Yifan Wang, Jing Zhao, Junwei Bao, Chaoqun Duan, Youzheng Wu, Xiaodong He

Dialogue Systems & Conversational Agents

Challenge:	Existing methods exploit the utterances of all dialogue turns to assign value to slots . this can lead to suboptimal results due to information introduced from irrelevant utterrances .
Approach:	They propose a SLot-TUrN Alignment enhanced approach to assign slot value . they explicitly align each slot with its most relevant utterance and then predict the corresponding value based on this aligned utteration.
Outcome:	The proposed approach achieves state-of-the-art on three multi-domain task-oriented dialogue datasets.

Unsupervised Melody-to-Lyrics Generation (2023.acl-long)

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Yufei Tian, Anjali Narayan-Chen, Shereen Oraby, Alessandra Cervone, Gunnar Sigurdsson, Chenyang Tao, Wenbo Zhao, Tagyoung Chung, Jing Huang, Nanyun Peng

Low-Resource NLP

Challenge:	Existing methods for automatic melody-to-lyric generation are limited due to the limited amount of melody-lyrical aligned data.
Approach:	They propose a method for automatic melody-to-lyric generation without training on any aligned melody-lyr data.
Outcome:	The proposed model generates high-quality lyrics that are singable, intelligible, and coherent than baseline models.

A Pretraining Numerical Reasoning Model for Ordinal Constrained Question Answering on Knowledge Base (2021.findings-emnlp)

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Yu Feng, Jing Zhang, Gaole He, Wayne Xin Zhao, Lemao Liu, Quan Liu, Cuiping Li, Hong Chen

RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering (2021.naacl-main)

Challenge:	Existing knowledge bases (KBs) can explicitly facilitate the QA process.
Approach:	They propose a numerical reasoning model pretraining NumGNN and NumTransformer, guided by explicit self-supervision signals, to enhance numerical reasoning ability for IR-based KBQA models.
Outcome:	Extensive experiments on two KBQA benchmarks confirm the effectiveness of the proposed model.

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Yingqi Qu, Yuchen Ding, Jing Liu, Kai Liu, Ruiyang Ren, Wayne Xin Zhao, Daxiang Dong, Hua Wu, Haifeng Wang

E3-TIR: Enhanced Experience Exploitation for Tool-Integrated Reasoning (2026.findings-acl)

Challenge:	Open-domain question answering uses dense passage retrieval to find answers . however, it is difficult to effectively train a dual-encoder due to discrepancy between training and inference .
Approach:	They propose an optimized training approach to improve dense passage retrieval using RocketQA . they propose cross-batch negatives, denoised hard negatives and data augmentation .
Outcome:	The proposed approach outperforms state-of-the-art models on both MSMARCO and Natural Questions.

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Weiyang Guo, Zesheng Shi, Liye Zhao, Jiayuan Ma, Zeen Zhu, Junxian He, Min Zhang, Jing Li

MASTER: A Multi-Agent System with LLM Specialized MCTS (2025.naacl-long)

Challenge:	Existing training paradigms for Large Language Models (LLMs) suffer from inefficient exploration and mode degradation due to a lack of prior guidance, while SFT-then-RL is limited by high data costs and capability plateaus caused by low-entropy collapse.
Approach:	They propose an Enhanced Experience Exploitation paradigm that integrates expert prefixes, expert guided, and self-exploration to improve agent training.
Outcome:	The proposed model achieves a 6% performance improvement over traditional paradigms on tool-use tasks while requiring less than 10% of the synthetic data.

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Bingzheng Gan, Yufan Zhao, Tianyi Zhang, Jing Huang, Li Yusu, Shu Xian Teo, Changwang Zhang, Wei Shi

Challenge:	Large Language Models (LLMs) are increasingly being explored for problem-solving tasks . their strategic planning capability is often viewed with skepticism due to their limited planning capabilities.
Approach:	They propose a framework that coordinates agent recruitment and communication through LLM specialized MCTS.
Outcome:	The proposed framework achieves 76% accuracy on HotpotQA and 80% on WebShop . it relies on extensive sampling simulations to approximate the true reward distribution .

Multimodal Machine Translation with Text-Image In-depth Questioning (2025.findings-acl)

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Yue Gao, Jing Zhao, Shiliang Sun, Xiaosong Qiao, Tengfei Song, Hao Yang