Papers by Jing Sun

CORD: Bridging the Audio–Text Reasoning Gap via Weighted On-policy Cross-modal Distillation (2026.findings-acl)

Challenge:	a new method for unlearning large language models is proposed to improve the performance of large language model models.
Approach:	They propose a probability perturbation-based unlearning paradigm that allows models to forget implicit knowledge in large language models with a focus on generalisation.
Outcome:	The proposed model improves unlearning vanilla target data while forgetting implicit knowledge.

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Hu Jing, Danxiang Zhu, Xianlong Luo, Dan Zhang, Shuwei He, Yishu Lei, Shikun Feng, Hai-Tao Zheng, Jingzhou HE, Yu Sun, Hua Wu, Haifeng Wang

Sparse-RL: Breaking the Memory Wall in LLM Reinforcement Learning via Stable Sparse Rollouts (2026.acl-long)

Challenge:	Large Audio Language Models (LALMs) exhibit a degradation in knowledge and reasoning capabilities . empirical results show that CORD significantly bridges the audio–text performance gap .
Approach:	They propose a framework that performs online cross-modal self-distillation to bridge the acoustic-semantic gap between LALMs and text-based models.
Outcome:	The proposed framework bridges the acoustic-semantic gap between LALMs and text-based models . it employs on-policy reverse KL divergence with importance-aware weighting .

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Sijia Luo, Xiaokang Zhang, Yuxuan Hu, Bohan Zhang, Ke Wang, Jinbo Su, Mengshu Sun, Lei Liang, Jing Zhang

Challenge:	Existing methods for storing key-value caches during long-horizon rollouts cause performance collapses.
Approach:	They propose a new training paradigm that empowers stable RL training under sparse rollouts.
Outcome:	The proposed model reduces rollout overhead while maintaining the performance.

SHARP: Unlocking Interactive Hallucination via Stance Transfer in Role-Playing LLMs (2025.findings-acl)

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Chuyi Kong, Ziyang Luo, Hongzhan Lin, Zhiyuan Fan, Yaxin Fan, Yuxi Sun, Jing Ma

Enhancing Self-Attention with Knowledge-Assisted Attention Maps (2022.naacl-main)

Challenge:	Existing studies on social interactions neglect hallucination while struggling with poor generalizability and implicit character fidelity judgments.
Approach:	They propose a generalizable and explicit paradigm for uncovering interactive patterns of Large Language Models across diverse worldviews by defining interactive hallucination through stance transfer and SHARP, a benchmark built by extracting relations from commonsense knowledge graphs.
Outcome:	The proposed paradigm is generalizable and explicit and demonstrates its effectiveness and stability.

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Jiangang Bai, Yujing Wang, Hong Sun, Ruonan Wu, Tianmeng Yang, Pengfei Tang, Defu Cao, Mingliang Zhang1, Yunhai Tong, Yaming Yang, Jing Bai, Ruofei Zhang, Hao Sun, Wei Shen

VALU: A Benchmark for Video Anomaly Temporal Localization and Understanding at Multiple Semantic Levels (2026.acl-long)

Challenge:	Existing works of knowledge infusion depend on multi-task learning frameworks, which are inefficient and require large-scale retraining when new knowledge is considered.
Approach:	They propose a method which integrates knowledge-generated attention maps into the self-attention mechanism and integrates it into the model.
Outcome:	The proposed model outperforms existing methods on academic datasets and industry-scale ad relevance applications.

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Yixiao He, Menghao Zhang, Haifeng Sun, Jing Wang, Kangheng Lin, Jinghan Wang, Chenye Xu, Pengfei Ren, Qi Qi, Jingyu Wang

Exploring Mode Connectivity for Pre-trained Language Models (2022.emnlp-main)

Challenge:	Recent advances in Video Large Language Models (Video-LLMs) enhance the ability of VAU models to describe and interpret anomalies.
Approach:	They propose a benchmark that explicitly defines anomalies across five semantic levels and provides detailed temporal boundaries and detailed textual descriptions for each.
Outcome:	The proposed benchmark defines anomalies across five semantic levels and provides detailed descriptions for each.

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Yujia Qin, Cheng Qian, Jing Yi, Weize Chen, Yankai Lin, Xu Han, Zhiyuan Liu, Maosong Sun, Jie Zhou

Knowledge Inheritance for Pre-trained Language Models (2022.naacl-main)

Challenge:	Recent years have witnessed the prevalent application of pre-trained language models (PLMs) in NLP. From the perspective of parameter space, PLMs provide generic initialization, starting from which high-performance minima could be found.
Approach:	They investigate the geometric connections of different minima through the lens of mode connectivity, which measures whether two minima can be connected with a low-loss path.
Outcome:	The proposed model can be used to find low-loss paths between two minima, and to understand how their mode connectivity affects their task knowledge.

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Yujia Qin, Yankai Lin, Jing Yi, Jiajie Zhang, Xu Han, Zhengyan Zhang, Yusheng Su, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou

ExPUNations: Augmenting Puns with Keywords and Explanations (2022.emnlp-main)

Challenge:	Existing large-scale pre-trained language models are mainly trained from scratch individually, ignoring that many well-taught PLMs are available.
Approach:	They propose a pre-training framework called knowledge inheritance and propose auxiliary supervision to efficiently learn larger PLMs.
Outcome:	The proposed framework can be used to train large-scale language models with huge parameters and a large dataset can be adapted to domain adaptation and knowledge transfer.

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Jiao Sun, Anjali Narayan-Chen, Shereen Oraby, Alessandra Cervone, Tagyoung Chung, Jing Huang, Yang Liu, Nanyun Peng

Explainability & Interpretability in NLP

Challenge:	Puns add the challenge of fusing commonsense and world knowledge with the ability to interpret lexical-semantic ambiguity.
Approach:	They propose to augment existing datasets with detailed crowdsourced annotations of puns, keywords and fine-grained funniness ratings to challenge current models' ability to understand and generate humor.
Outcome:	The proposed tasks include explanation generation to aid with pun classification and keyword-conditioned pun generation to challenge state-of-the-art models' ability to understand and generate humor.

Noisy Multi-Label Text Classification via Instance-Label Pair Correction (2024.findings-naacl)

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Pengyu Xu, Mingyang Song, Linkaida Liu, Bing Liu, Hongjian Sun, Liping Jing, Jian Yu

Modeling Aspect Correlation for Aspect-based Sentiment Analysis via Recurrent Inverse Learning Guidance (2022.coling-1)

Challenge:	Noise is a significant challenge for machine learning models, especially deep learning models.
Approach:	They propose a holistic selection metric that identifies noisy pairs while considering global loss information and instance-specific ranking information.
Outcome:	The proposed approach significantly improves performance in noisy multi-label text classification tasks.

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Longfeng Li, Haifeng Sun, Qi Qi, Jingyu Wang, Jing Wang, Jianxin Liao

Sentiment Analysis

Challenge:	Existing methods to learn complex sentence with multiple aspects do not consider correlation between aspects to distinguish overlapped feature.
Approach:	They propose a method that uses aspect correlation to improve aspect correlation modeling . they use Recurrent Mechanism to improve the joint representation of aspects .
Outcome:	The proposed method is state-of-the-art in multiaspect scenarios.

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

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

Span-based Localizing Network for Natural Language Video Localization (2020.acl-main)

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

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Hao Zhang, Aixin Sun, Wei Jing, Joey Tianyi Zhou

Visual Data in NLP

Challenge:	Existing approaches to NLVL are either ranking tasks or regressing the target video span.
Approach:	They propose a video span localizing network to solve a natural language video localization task using a span-based QA approach.
Outcome:	The proposed network outperforms the state-of-the-art methods on three benchmark datasets.

FACT-E: Causality-Inspired Evaluation for Trustworthy Chain-of-Thought Reasoning (2026.findings-acl)

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Yuxi Sun, Aoqi Zuo, Haotian Xie, Wei Gao, Mingming Gong, Jing Ma

Unveiling Internal Reasoning Modes in LLMs: A Deep Dive into Latent Reasoning vs. Factual Shortcuts with Attribute Rate Ratio (2025.emnlp-main)

Challenge:	Existing models generate explanations that appear coherent while containing unfaithful intermediate steps.
Approach:	They propose a causality-inspired framework for evaluating CoT quality using controlled perturbations as an instrumental signal to separate genuine step-to-step dependence from bias-driven artifacts.
Outcome:	Experiments on GSM8K, MATH, and CommonsenseQA show that FACT-E improves reasoning-trajectory selection and yields stronger in-context learning exemplars.

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Yiran Yang, Haifeng Sun, Jingyu Wang, Qi Qi, Zirui Zhuang, Huazheng Wang, Pengfei Ren, Jing Wang, Jianxin Liao

SceneGenAgent: Precise Industrial Scene Generation with Coding Agent (2025.acl-long)

Challenge:	Existing research in multi-hop questions has identified two reasoning modes, but has not investigated how these modes differ during inference.
Approach:	They propose a classification metric that compares latent reasoning and factual shortcuts in multi-hop questions.
Outcome:	The proposed metric achieves 90% accuracy on the proposed datasets and demonstrates effectiveness in RAG conflict scenarios.

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Xiao Xia, Dan Zhang, Zibo Liao, Zhenyu Hou, Tianrui Sun, Jing Li, Ling Fu, Yuxiao Dong

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

Challenge:	Recent work on scene generation focuses on generating 3D scenes from textual descriptions . however, the task of generating industrial scenes with LLMs is complex and requires precise measurements and positioning .
Approach:	They propose an LLM-based agent for generating industrial scenes through C# code.
Outcome:	Experiments show that LLMs powered by SceneGenAgent exceed their original performance . the agent achieves 81.0% success rate in real-world industrial scene generation tasks .

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

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

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

Reasoning-Enhanced Domain-Adaptive Pretraining of Multimodal Large Language Models for Short Video Content Governance (2025.emnlp-industry)

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.

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Zixuan Wang, Yu Sun, Hongwei Wang, Baoyu Jing, Xiang Shen, Xin Dong, Zhuolin Hao, Hongyu Xiong, Yang Song

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

Challenge:	Existing approaches to identifying inappropriate content require extensive human-labeled data and lack cross-issue generalization.
Approach:	They propose a reasoning-enhanced multimodal large language model (MLLM) pretraining paradigm for unified inappropriate content detection.
Outcome:	The proposed model improves the MLLM's performance in both zero-shot and supervised fine-tuning settings and shows strong generalization capabilities to emergent, previously unseen issues.

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

Musical Score Understanding Benchmark: Evaluating Large Language Models’ Comprehension of Complete Musical Scores (2026.acl-long)

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

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Congren Dai, Yue Yang, Krinos Li, Huichi Zhou, Shijie Liang, Zhang Bo, Enyang Liu, Ge Jin, Hongran An, Haosen Zhang, Peiyuan Jing, KinHei Lee, Zhenxuan Zhang, Xiaobing Li, Maosong Sun

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

Challenge:	Existing benchmarks for musical score understanding are narrow in scope, focusing on isolated fragments, short excerpts, or multiple-choice formulations, rather than supporting holistic reasoning over entire scores.
Approach:	They propose a benchmark for score-level musical understanding across textual and visual modalities.
Outcome:	The musical score understanding benchmark contains 1,800 question-answer pairs from works by Bach, Beethoven, Chopin, Debussy, and others.

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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

Bi-directional CognitiveThinking Network for Machine Reading Comprehension (2020.coling-main)

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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Wei Peng, Yue Hu, Luxi Xing, Yuqiang Xie, Jing Yu, Yajing Sun, Xiangpeng Wei

PRDetect: Perturbation-Robust LLM-generated Text Detection Based on Syntax Tree (2025.findings-naacl)

Challenge:	Existing methods for reading comprehension are still in their infancy at the level of cognitive intelligence.
Approach:	They propose a bi-directional cognitive knowledge framework to simulate reverse thinking and inertial thinking in the brain to answer questions.
Outcome:	The proposed framework shows that bi-directional knowledge helps the QA task.

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Xiang Li, Zhiyi Yin, Hexiang Tan, Shaoling Jing, Du Su, Yi Cheng, Huawei Shen, Fei Sun

Syntactic Text Processing

Challenge:	Recent methods for detecting LLM-generated text have shown impressive performance, but in real-world scenarios, users often introduce perturbations to the text.
Approach:	They propose a method that detects syntactic trees that are minimally affected by perturbations and exhibit distinct differences between human-written and LLM-generated text.
Outcome:	The proposed method shows that it is significantly better against perturbations on the HC3 and GPT-3.5-mixed datasets and also has the shortest time expenditure.

MS-DETR: Natural Language Video Localization with Sampling Moment-Moment Interaction (2023.acl-long)

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Wang Jing, Aixin Sun, Hao Zhang, Xiaoli Li

REFLEX: Self-Refining Explainable Fact-Checking via Verdict-Anchored Style Control (2026.acl-long)

Challenge:	Natural language video localization (NLVL) aims to localize a temporal moment from an untrimmed video that semantically corresponds to a given text query.
Approach:	They propose a proposal-based solution that generates proposals and selects the best matching proposal.
Outcome:	The proposed solution is faster than existing approaches on three public datasets.

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Chuyi Kong, Wei Gao, Jing Ma, Hongzhan Lin, Yuxi Sun

CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language Models (2025.findings-naacl)

Challenge:	Existing methods for automated fact-checking often overlook deceptive misinformation styles in generated explanations.
Approach:	They propose a framework that explicitly controls reasoning style by anchoring explanations to the predicted verdict.
Outcome:	The proposed framework achieves state-of-the-art under LLaMA-series models with 465 samples.

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Shangda Wu, Yashan Wang, Ruibin Yuan, Guo Zhancheng, Xu Tan, Ge Zhang, Monan Zhou, Jing Chen, Xuefeng Mu, Yuejie Gao, Yuanliang Dong, Jiafeng Liu, Xiaobing Li, Feng Yu, Maosong Sun

UICOMPASS: UI Map Guided Mobile Task Automation via Adaptive Action Generation (2025.emnlp-main)

Challenge:	Current music information retrieval systems struggle to meet linguistic diversity challenges . current systems struggle with text queries in non-English languages .
Approach:	They propose a music information retrieval system that supports both ABC notation and MIDI . CLaMP 2 includes a multilingual text encoder and a multiple-modal music encoder .
Outcome:	The proposed system achieves state-of-the-art results in multilingual semantic search and music classification across modalities.

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Yuanzhang Lin, Zhe Zhang, He Rui, Qingao Dong, Mingyi Zhou, Jing Zhang, Xiang Gao, Hailong Sun

Challenge:	Mobile task automation is an emerging technology that leverages AI to automatically execute routine tasks by users’ commands on mobile devices like Android.
Approach:	They propose a UI Map-guided LLM-based approach to automate mobile tasks using static analysis and LLMs.
Outcome:	The proposed approach achieves a 15.87% higher task execution success rate than SOTA approaches even when only APK is available.

Value Compass Benchmarks: A Comprehensive, Generative and Self-Evolving Platform for LLMs’ Value Evaluation (2025.acl-demo)

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Jing Yao, Xiaoyuan Yi, Shitong Duan, Jindong Wang, Yuzhuo Bai, Muhua Huang, Yang Ou, Scarlett Li, Peng Zhang, Tun Lu, Zhicheng Dou, Maosong Sun, James Evans, Xing Xie

Challenge:	Current evaluation methods for large language models face two key challenges: 1. evaluation validity and 2. Result interpretation reduce the pluralistic and incommensurable values to one-dimensional scores.
Approach:	They propose a platform for comprehensive value diagnosis of large language models (LLMs) that provides a generative evaluation paradigm that automatically creates real-world test items co-evolving with ever-advancing LLMs.
Outcome:	The proposed platform provides a framework for comprehensive value diagnosis of large language models (LLMs) with fine-grained scores and case studies across 27 value dimensions for 33 leading LLMs, customized comparisons, and visualized analysis of LLM’s alignment with cultural values.

MMEKG: Multi-modal Event Knowledge Graph towards Universal Representation across Modalities (2022.acl-demo)

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Yubo Ma, Zehao Wang, Mukai Li, Yixin Cao, Meiqi Chen, Xinze Li, Wenqi Sun, Kunquan Deng, Kun Wang, Aixin Sun, Jing Shao

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

Challenge:	Recent Knowledge Graphs (KGs) store billions of world facts in a directed graph, but expression ability of such entity-centric KGs is limited.
Approach:	They propose a large-scale multi-modal event knowledge graph named MMEKG that unifies different modalities of knowledge via events.
Outcome:	The proposed system unifies different modalities of knowledge via events, which complement and disambiguate each other.

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

COSY: COunterfactual SYntax for Cross-Lingual Understanding (2021.acl-long)

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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Sicheng Yu, Hao Zhang, Yulei Niu, Qianru Sun, Jing Jiang

Syntactic Text Processing

Challenge:	Pre-trained multilingual language models suffer from a large performance gap between source and target languages . e.g., multilingual-BERT models are widely used in cross-lingual tasks .
Approach:	They propose a language-agnostic approach to integrate universal syntax into language models . they use SYntax-aware networks and a COunterfactual training method .
Outcome:	The proposed model achieves state-of-the-art performance on natural language inference and question answering without auxiliary training data.

Answer-focused and Position-aware Neural Question Generation (D18-1)

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Xingwu Sun, Jing Liu, Yajuan Lyu, Wei He, Yanjun Ma, Shi Wang

Text Generation

Challenge:	Recent neural network-based approaches generate interrogative words that do not match the answer type.
Approach:	They propose an answer-focused and position-aware neural question generation model to address these issues.
Outcome:	The proposed model outperforms the baseline and outperformed the state-of-the-art system.

Translate-Train Embracing Translationese Artifacts (2022.acl-short)

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Sicheng Yu, Qianru Sun, Hao Zhang, Jing Jiang

Multilinguality

Challenge:	Existing approaches to train multilingual tasks are based on translationese and translatetrain.
Approach:	They propose to use translationese to mitigate the gap between the source and target languages to train the translator.
Outcome:	The proposed method outperforms baselines on the multilingual QA dataset TyDiQA.

SHIELD: Evaluation and Defense Strategies for Copyright Compliance in LLM Text Generation (2024.emnlp-main)

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Xiaoze Liu, Ting Sun, Tianyang Xu, Feijie Wu, Cunxiang Wang, Xiaoqian Wang, Jing Gao

Robustness in NLP

Challenge:	Large Language Models (LLMs) have transformed machine learning but have raised significant legal concerns due to their potential to produce text that infringes on copyrights.
Approach:	They propose a lightweight, real-time defense mechanism to prevent the generation of copyrighted text by evaluating methods and testing attack strategies.
Outcome:	The proposed defense significantly reduces the volume of copyrighted text generated by LLMs by effectively refusing malicious requests.

Subtopic-driven Multi-Document Summarization (D19-1)

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Xin Zheng, Aixin Sun, Jing Li, Karthik Muthuswamy

Cross-Thought for Sentence Encoder Pre-training (2020.emnlp-main)

Challenge:	Experimental results show that the proposed model outperforms state-of-the-art methods on benchmark datasets.
Approach:	They propose a multi-document summarization model that assumes a set of documents to be summarized is on the same topic.
Outcome:	The proposed model outperforms state-of-the-art methods on benchmark datasets.

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Shuohang Wang, Yuwei Fang, Siqi Sun, Zhe Gan, Yu Cheng, Jingjing Liu, Jing Jiang

MoVa: Towards Generalizable Classification of Human Morals and Values (2025.emnlp-main)

Challenge:	Existing models to pretrain sentence encoders with large unlabeled corpus are lacking in linguistic information retrieval.
Approach:	They propose a novel approach to pre-training sequence encoder using transformers . they propose to train a Transformer-based sequence encoded over a large set of short sequences based on a set of masked words .
Outcome:	The proposed approach outperforms state-of-the-art encoders on hotpotQA by improving intermediate information retrieval performance.

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Ziyu Chen, Junfei Sun, Chenxi Li, Tuan Dung Nguyen, Jing Yao, Xiaoyuan Yi, Xing Xie, Chenhao Tan, Lexing Xie

Text Classification

Challenge:	Identifying human morals and values embedded in language is essential to empirical studies of communication.
Approach:	They propose a framework for generalizable classification of human morals and values . they recommend a classification strategy that scores all related concepts simultaneously .
Outcome:	The proposed method outperforms fine-tuned models across domains and frameworks.

OPeRA: A Dataset of Observation, Persona, Rationale, and Action for Evaluating LLMs on Human Online Shopping Behavior Simulation (2026.acl-long)

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Ziyi Wang, Yuxuan Lu, Wenbo Li, Amirali Amini, Bo Sun, Yakov Bart, Weimin Lyu, Jiri Gesi, Tian Wang, Jing Huang, Yu Su, Upol Ehsan, Malihe Alikhani, Toby Jia-Jun Li, Lydia Chilton, Dakuo Wang

Challenge:	evaluating LLMs' ability to mimic real user behavior remains an open challenge due to the lack of high-quality, publicly available datasets that capture both the observable actions and the internal reasoning of an actual user.
Approach:	They propose a dataset of Observation, Persona, Rationale, and Action collected from real human participants during online shopping sessions.
Outcome:	The proposed dataset is the first to evaluate how well current LLMs can accurately simulate the next web action of a specific user.

Continual Gradient Low-Rank Projection Fine-Tuning for LLMs (2025.acl-long)

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Chenxu Wang, Yilin Lyu, Zicheng Sun, Liping Jing

Reverse Chain: A Generic-Rule for LLMs to Master Multi-API Planning (2024.findings-naacl)

Challenge:	Low-Rank Adaptation (LoRA) offers efficiency but constrains the model’s ability to learn new tasks and transfer knowledge due to its low-rank nature and reliance on explicit parameter constraints.
Approach:	They propose a training strategy that synergistically combines full and low-rank parameters and jointly updating within a unified low-ranked gradient subspace.
Outcome:	Extensive experiments on continual learning benchmarks show that GORP improves performance compared to state-of-the-art approaches.

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Yinger Zhang, Hui Cai, Xierui Song, Yicheng Chen, Rui Sun, Jing Zheng

QuoteR: A Benchmark of Quote Recommendation for Writing (2022.acl-long)

Challenge:	Existing approaches to enable large language models to implement function calling are limited in their tool-use capabilities.
Approach:	They propose a controllable, target-driven approach to empower LLMs to operate external APIs only via prompts.
Outcome:	The proposed approach limits LLMs to executing simple tasks, e.g., API Selection and Argument Completion.

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Fanchao Qi, Yanhui Yang, Jing Yi, Zhili Cheng, Zhiyuan Liu, Maosong Sun

Challenge:	Existing methods to recommend quotes are evaluated on unpublished datasets .
Approach:	They propose to build a dataset that is open and contains three parts including English, standard Chinese and classical Chinese.
Outcome:	The proposed model outperforms existing methods on all three parts of QuoteR.

MoE Adapter for Large Audio Language Models: Sparsity, Disentanglement, and Gradient-Conflict-Free (2026.findings-acl)

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Yishu Lei, Shuwei He, Hu Jing, Dan Zhang, Xianlong Luo, Danxiang Zhu, Shikun Feng, Rui Liu, Jingzhou HE, Yu Sun, Hua Wu, Haifeng Wang

Parallel Attention Network with Sequence Matching for Video Grounding (2021.findings-acl)

Challenge:	Existing research on Large Language Models (LLMs) limited to textual input modality . acoustic information is intrinsically heterogeneous, entangling attributes such as speech, music, and environmental context.
Approach:	They propose a sparse Mixture-of-Experts architecture to decouple acoustic information by routing audio tokens to specialized experts.
Outcome:	The proposed architecture outperforms existing models on audio semantic and paralinguistic tasks while retaining shared experts for global context.

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Hao Zhang, Aixin Sun, Wei Jing, Liangli Zhen, Joey Tianyi Zhou, Siow Mong Rick Goh

A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss (P18-1)

Challenge:	Existing approaches to video grounding are sensitive to quality of proposals and inefficient because all proposal-query pairs are compared.
Approach:	They propose a Parallel Attention Network with Sequence matching to capture selfmodal contexts and cross-modal attentive information between video and text.
Outcome:	The proposed approach is superior to state-of-the-art methods on three datasets.

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Wan-Ting Hsu, Chieh-Kai Lin, Ming-Ying Lee, Kerui Min, Jing Tang, Min Sun

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

Challenge:	extractive models can obtain sentence-level attention with high ROUGE scores but less readable. abstractive models generate novel words and phrases not copied from the source text.
Approach:	They propose to combine extractive and abstractive models to achieve a unified model that generates readable paragraphs with word-level attention.
Outcome:	The proposed model achieves state-of-the-art ROUGE scores while being the most informative and readable summarization on the CNN/Daily Mail dataset in a solid human evaluation.

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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.

Optimizing Native Sparse Attention with Latent Attention and Local Global Alternating Strategies (2026.findings-acl)

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Yuxuan Hu, Jianchao Tan, Jiaqi Zhang, Wen Zan, Pingwei Sun, Yifan Lu, Xunliang Cai, Jing Zhang

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

Challenge:	Existing research has proposed a variety of training-free and post-training methods for selecting critical key-value pairs at each generation step.
Approach:	They propose to use local (sliding-window) and global (compression/selective) attention across layers to enlarge long-context modeling.
Outcome:	Experiments on models from 340M to 1.3B parameters show that the proposed method matches or exceeds full attention and native sparse attention in both common-sense reasoning and long-context understanding tasks.

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

Beyond Query Bias: Candidate-Aware Iterative Refinement for Zero-Shot Composed Image Retrieval (2026.findings-acl)

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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Nan Sun, Jing Tang, Lei Sun, Rui Chen, Yuxing Lu, Xiangxiang Chu, Hefei Ling, Yujun Cai

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

Challenge:	Existing methods to retrieve target images suffer from inherent cognitive bias due to unknown candidate distribution.
Approach:	They propose a training-free framework that reframes ZS-CIR as a self-correcting process . they propose to use retrieved results as feedback to perceive the candidate distribution .
Outcome:	Experiments on public benchmarks show that CoRR outperforms other SOTA methods.

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

GraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Design (2025.findings-naacl)

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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Yuanfu Sun, Zhengnan Ma, Yi Fang, Jing Ma, Qiaoyu Tan

Global and Local Hierarchical Prompt Tuning Framework for Multi-level Implicit Discourse Relation Recognition (2024.lrec-main)

Challenge:	Text-Attributed Graphs (TAGs) are a powerful tool for understanding complex systems and relationships.
Approach:	They propose a benchmark to evaluate large language models for graph-structured data using prompts.
Outcome:	The proposed benchmark outperforms state-of-the-art graph LLMs and graph neural networks on graph learning tasks without training.

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Lei Zeng, Ruifang He, Haowen Sun, Jing Xu, Chang Liu, Bo Wang

Different Tunes Played with Equal Skill: Exploring a Unified Optimization Subspace for Parameter-Efficient Tuning (2022.findings-emnlp)

Challenge:	Recent methods to recognize hierarchical discourse relations without explicit connectives are inefficient and ignore the utilization of the output probability distribution information of the verbalizer.
Approach:	They propose a global and local hierarchical prompt tuning framework which leverages top-up propagated probability as the global hierarchy to inject it into multi-level verbalizer.
Outcome:	The proposed framework achieves competitive results on two benchmacks.

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Jing Yi, Weize Chen, Yujia Qin, Yankai Lin, Ning Ding, Xu Han, Zhiyuan Liu, Maosong Sun, Jie Zhou

Green & Sustainable NLP

Challenge:	Existing delta tuning algorithms freeze most of the parameters and only optimize minimal adaptive parameters.
Approach:	They propose to decompose DETs into a unified optimization subspace and conduct optimization within the subspace.
Outcome:	The proposed DETs achieve comparable performance to the original DET and can be transferred to another DET with non-trivial performance.

Equal Truth: Rumor Detection with Invariant Group Fairness (2025.findings-emnlp)

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Junyi Chen, Mengjia Wu, Qian Liu, Jing Sun, Ying Ding, Yi Zhang

A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict (2022.findings-naacl)

Challenge:	Existing rumor detection methods rarely consider fairness issues inherent in the model . this can lead to biased predictions across stakeholder groups, undermining their detection effectiveness .
Approach:	They propose a framework to address fairness issues inherent in rumor detection models . they perform unsupervised partitioning to dynamically identify potential unfair data patterns . then, they apply invariant learning to these partitions to extract fair and informative feature representations .
Outcome:	The proposed method outperforms strong baselines regarding detection and fairness performance . it also shows robust performance on out-of-distribution samples .

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Yiyi Liu, Yequan Wang, Aixin Sun, Xuying Meng, Jing Li, Jiafeng Guo

Sentiment Analysis

Challenge:	Sarcasm employs ambivalence, where one says something positive but actually means negative . linguistically, it is difficult to recognize such sentiment conflict because the sentiments are mixed or even implicit .
Approach:	They propose a Dual-Channel Framework to model literal and implied sentiments separately . they propose sarcastic networks that can detect sarcasm sentiments in political debates .
Outcome:	The proposed framework achieves state-of-the-art on political debates and Twitter datasets.

CausalAbstain: Enhancing Multilingual LLMs with Causal Reasoning for Trustworthy Abstention (2025.findings-acl)

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Yuxi Sun, Aoqi Zuo, Wei Gao, Jing Ma

Evaluating and Mitigating Object Hallucination in Large Vision-Language Models: Can They Still See Removed Objects? (2025.naacl-long)

Challenge:	Existing methods to reduce hallucinations in large language models are inaccurate and inaccuracies in the generated feedback.
Approach:	They propose a method that helps LLMs determine whether to utilize multiple generated feedback responses and how to identify the most useful ones.
Outcome:	Extensive experiments show that the proposed method outperforms baselines on encyclopedic and commonsense knowledge QA tasks.

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Yixiao He, Haifeng Sun, Pengfei Ren, Jingyu Wang, Huazheng Wang, Qi Qi, Zirui Zhuang, Jing Wang

Context-Situated Pun Generation (2022.emnlp-main)

Challenge:	LVLMs often mistakenly determine objects as present in images where they do not exist . authors propose a new benchmark to evaluate object hallucinations by removing objects from images and asking the model whether it can still see the removed objects.
Approach:	They propose a benchmark to evaluate object hallucinations by removing objects from images . they propose oDPO, a direct preference optimization objective based on visual objects .
Outcome:	The proposed benchmark reduces the likelihood of object hallucinations by removing objects from images and asking the model whether it can still see the removed objects.

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Jiao Sun, Anjali Narayan-Chen, Shereen Oraby, Shuyang Gao, Tagyoung Chung, Jing Huang, Yang Liu, Nanyun Peng

Information Retrieval

Challenge:	a new task for context-situated pun generation uses a given context to generate puns . human evaluation shows that 69% of top retrieved pun words can be used to generate context-based puns.
Approach:	They propose a task where puns are generated based on contextual keywords and pun words.
Outcome:	The proposed system generates successful puns 31% of the time given a plausible tuple of context words and pun pairs.

DAPE V2: Process Attention Score as Feature Map for Length Extrapolation (2025.acl-long)

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Chuanyang Zheng, Yihang Gao, Han Shi, Jing Xiong, Jiankai Sun, Jingyao Li, Minbin Huang, Xiaozhe Ren, Michael Ng, Xin Jiang, Zhenguo Li, Yu Li

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

Challenge:	Extensive experiments demonstrate that treating attention as a feature map and applying convolution as . a processing method significantly enhances Transformer performance.
Approach:	They propose to use the convolution operator to mimic the processing methods in computer vision to treat attention as a feature map and apply it to neighboring attention scores across different heads.
Outcome:	The proposed model can be adapted to various attention-related models and achieves high performance.

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