Papers by Yu Peng

EvoEdit: Evolving Null-space Alignment for Robust and Efficient Knowledge Editing (2026.findings-acl)

Challenge:	In this paper, we address the problem of data scarcity for the Hong Kong Cantonese language . due to the popularization of deep learning, ASR technology has led to a significant improvement in recognizing many languages.
Approach:	They propose to use a dataset to analyze the data available for the Hong Kong Cantonese language . they use zh-HK as a source and a state-of-the-art ASR model to build a powerful model .
Outcome:	The proposed model improves on the biggest existing dataset, Common Voice zh-HK.

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Sicheng Lyu, Yu Gu, Xinyu Wang, Jerry Huang, Sitao Luan, Yufei Cui, Xiao-Wen Chang, Peng Lu

Cross-Lingual Cross-Modal Consolidation for Effective Multilingual Video Corpus Moment Retrieval (2022.findings-naacl)

Challenge:	Existing approaches to modifying large language models require continual updates to rectify outdated or erroneous knowledge.
Approach:	They propose a model editing strategy that mitigates catastrophic interference through sequential null-space alignment.
Outcome:	EvoEdit achieves better or comparable performance than prior state-of-the-art techniques with up to 3.53 speedup.

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Jiaheng Liu, Tan Yu, Hanyu Peng, Mingming Sun, Ping Li

XLTime: A Cross-Lingual Knowledge Transfer Framework for Temporal Expression Extraction (2022.findings-naacl)

Challenge:	Existing multilingual video corpus moment retrieval methods are based on a two-stream structure.
Approach:	They propose a multilingual video corpus moment retrieval task that uses a two-stream structure to generate a query-visual similarity and a subtitle stream exploits the query-subtitle similarity.
Outcome:	The proposed method improves accuracy on a large-scale video corpus moment retrieval dataset.

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Yuwei Cao, William Groves, Tanay Kumar Saha, Joel Tetreault, Alejandro Jaimes, Hao Peng, Philip Yu

Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representation (2022.naacl-main)

Challenge:	Temporal Expression Extraction (TEE) is essential for understanding time in natural language.
Approach:	They propose a framework for multilingual Temporal Expression Extraction that leverages pre-trained language models to prompt cross-language knowledge transfer from English to non-English languages.
Outcome:	The proposed framework outperforms the existing SOTA methods on French, Spanish, Portuguese, and Basque by large margins.

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Yu Li, Baolin Peng, Yelong Shen, Yi Mao, Lars Liden, Zhou Yu, Jianfeng Gao

EmbodiedBERT: Cognitively Informed Metaphor Detection Incorporating Sensorimotor Information (2024.findings-emnlp)

Challenge:	Existing knowledge-grounded dialogue systems perform poorly on unseen topics due to limited topics covered in training data.
Approach:	They propose a language model that homogenizes different knowledge sources to a unified knowledge representation for knowledge-grounded dialogue generation tasks.
Outcome:	The proposed language model generalizes well across knowledge-grounded dialogue tasks.

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Yu Li, Bo Peng, Yu-Yin Hsu, Chu-Ren Huang

FOLIO: Natural Language Reasoning with First-Order Logic (2024.emnlp-main)

Challenge:	Existing methods for metaphor detection rely on heuristics such as Metaphor Identification Procedure (MIP) and Selection Preference Violation (SPV).
Approach:	They propose a cognitively motivated module that leverages the cognitive information of embodiment that can be derived from word embeddings and explicitly models the process of sensorimotor change that has been demonstrated as essential for metaphor processing.
Outcome:	The proposed module can improve metaphor detection compared with the heuristic MIP that has been applied previously.

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Simeng Han, Hailey Schoelkopf, Yilun Zhao, Zhenting Qi, Martin Riddell, Wenfei Zhou, James Coady, David Peng, Yujie Qiao, Luke Benson, Lucy Sun, Alexander Wardle-Solano, Hannah Szabó, Ekaterina Zubova, Matthew Burtell, Jonathan Fan, Yixin Liu, Brian Wong, Malcolm Sailor, Ansong Ni, Linyong Nan, Jungo Kasai, Tao Yu, Rui Zhang, Alexander Fabbri, Wojciech Kryscinski, Semih Yavuz, Ye Liu, Xi Lin, Shafiq Joty, Yingbo Zhou, Caiming Xiong, Rex Ying, Arman Cohan, Dragomir Radev

HGCLIP: Exploring Vision-Language Models with Graph Representations for Hierarchical Understanding (2025.coling-main)

Challenge:	Existing benchmarks for logical reasoning in large language models lack language naturalness or limited complexity.
Approach:	They propose to use first-order logic annotations to evaluate logical reasoning capabilities of large language models.
Outcome:	The proposed dataset evaluates the FOL reasoning ability of supervised fine-tuning on medium-sized language models.

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Peng Xia, Xingtong Yu, Ming Hu, Lie Ju, Zhiyong Wang, Peibo Duan, Zongyuan Ge

ScalingFilter: Assessing Data Quality through Inverse Utilization of Scaling Laws (2024.emnlp-main)

Challenge:	Object categories are typically organized into a multi-granularity taxonomic hierarchy . traditional uni-modal approaches focus primarily on image features, revealing limitations in complex scenarios.
Approach:	They propose a framework that combines vision-language models with a deeper exploitation of the hierarchy.
Outcome:	The proposed framework shows significant improvements on 11 diverse visual recognition benchmarks.

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Ruihang Li, Yixuan Wei, Miaosen Zhang, Nenghai Yu, Han Hu, Houwen Peng

COM2SENSE: A Commonsense Reasoning Benchmark with Complementary Sentences (2021.findings-acl)

Challenge:	Existing quality filtering methods rely on a high-quality dataset as reference . Existing methods introduce potential biases and compromise diversity .
Approach:	They propose a method that evaluates text quality based on the perplexity difference between two language models trained on the same data.
Outcome:	The proposed approach improves performance of pre-trained models without increasing training costs.

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Shikhar Singh, Nuan Wen, Yu Hou, Pegah Alipoormolabashi, Te-lin Wu, Xuezhe Ma, Nanyun Peng

Self-Consistency Boosts Calibration for Math Reasoning (2024.findings-emnlp)

Challenge:	Recent advances in pretrained language models have shown promising results on commonsense reasoning benchmark datasets.
Approach:	They propose a commonsense reasoning benchmark dataset with 4k sentence pairs . they propose 'gamified' model-in-the-loop setup to incentivize challenging samples .
Outcome:	The proposed benchmarks show that the proposed model achieves 71% standard accuracy and 51% pairwise accuracy, well below human performance.

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Ante Wang, Linfeng Song, Ye Tian, Baolin Peng, Lifeng Jin, Haitao Mi, Jinsong Su, Dong Yu

SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues (2021.acl-long)

Challenge:	Existing solutions for math reasoning tasks use semantic parsing or AST decoding, but performance can degrade dramatically even with slight changes to the questions.
Approach:	They propose three calibration methods based on self-consistency for math reasoning tasks.
Outcome:	The proposed methods bridge model confidence and accuracy better than existing methods based on p(True) or logit.

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Liang Qiu, Yuan Liang, Yizhou Zhao, Pan Lu, Baolin Peng, Zhou Yu, Ying Nian Wu, Song-Chun Zhu

Structured Data in NLP

Challenge:	Existing studies focus on identifying entities' relations from the semantics of dialogues-they utilize either the attention mechanism or a refined token graph to locate informative words.
Approach:	They propose a sequential structure prediction task to incrementally parse SocAoG for dynamic inference upon any incoming utterance.
Outcome:	Empirical results show that the proposed model infers social relations more accurately than the state-of-the-art methods.

LM-Infinite: Zero-Shot Extreme Length Generalization for Large Language Models (2024.naacl-long)

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Chi Han, Qifan Wang, Hao Peng, Wenhan Xiong, Yu Chen, Heng Ji, Sinong Wang

Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarization (2023.acl-long)

Challenge:	Currently, large language models (LLMs) train on short text segments due to the computational overhead quadratic in the input lengths of their Transformer architectures.
Approach:	They propose a method that allows LLMs pre-trained with 2K or 4K-long segments to generalize to up to 200M length inputs while retaining perplexity.
Outcome:	The proposed method achieves 2.7 decoding speed up and 7.5 memory saving over the original model.

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Pengcheng He, Baolin Peng, Song Wang, Yang Liu, Ruochen Xu, Hany Hassan, Yu Shi, Chenguang Zhu, Wayne Xiong, Michael Zeng, Jianfeng Gao, Xuedong Huang

Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic Representations (2024.naacl-long)

Challenge:	Z-Code++ is a pre-trained language model optimized for abstractive text summarization.
Approach:	They propose a pre-trained language model optimized for abstractive text summarization that uses a two-phase pre-training technique to improve model's performance.
Outcome:	The proposed model outperforms the competing models on low-resource summarization tasks in zero-shot and few-shot settings.

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Sihao Chen, Hongming Zhang, Tong Chen, Ben Zhou, Wenhao Yu, Dian Yu, Baolin Peng, Hongwei Wang, Dan Roth, Dong Yu

Keyphrase Generation via Soft and Hard Semantic Corrections (2022.emnlp-main)

Challenge:	Sentence embeddings are typically learned to recognize the semantic relation between two text inputs.
Approach:	They introduce a contrastively-learned contextual embedding model for fine-grained semantic representation of text.
Outcome:	The proposed model is able to produce contextual embeddings corresponding to different atomic propositions, i.e. semantic equivalence between propositions across different text sequences.

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Guangzhen Zhao, Guoshun Yin, Peng Yang, Yu Yao

SPEED++: A Multilingual Event Extraction Framework for Epidemic Prediction and Preparedness (2024.emnlp-main)

Challenge:	Extensive experiments show that CorrKG is capable of generating high-quality keyphrases.
Approach:	They propose a correction model CorrKG on top of the MLE pipeline to correct the biases . the adaptive adaptive mass learning scheme is designed to better fit OT and FreqFS .
Outcome:	The proposed model overcomes the semantic biases in keyphrase generation using OT and FreqFS techniques.

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Tanmay Parekh, Jeffrey Kwan, Jiarui Yu, Sparsh Johri, Hyosang Ahn, Sreya Muppalla, Kai-Wei Chang, Wei Wang, Nanyun Peng

Don’t Stop Early: Scalable Enterprise Deep Research with Controlled Information Flow and Evidence-Aware Termination (2026.acl-industry)

Challenge:	Prior studies focused on English posts to provide early warnings for epidemic prediction, but these work focused on non-English posts.
Approach:	They propose a multilingual event extraction framework for extracting epidemic event information for any disease and language using 5.1K tweets in four languages.
Outcome:	The proposed framework can provide epidemic warnings for COVID-19 in its earliest stages in Dec 2019 (3 weeks before global discussions) and aggregate community epidemic discussions like symptoms and cure measures, aiding misinformation detection and public attention monitoring.

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Prafulla Kumar Choubey, Kung-Hsiang Huang, Pranav Narayanan Venkit, Jiaxin Zhang, Vaibhav Vats, Yu Li, Xiangyu Peng, Chien-Sheng Wu

Challenge:	Enterprise deep research systems fail to produce decision-ready reports due to uneven information coverage, context explosion, and premature stopping.
Approach:	They propose a scalable Enterprise Deep Research (EDR) architecture that decomposes requests into coverage-driven objectives via outline generation with reflection and localizes context with dependency-guided execution and explicit information sharing.
Outcome:	The proposed system achieves the strongest overall performance compared with competitive deep-research baselines on internal sales enablement tasks and the public DeepResearch Bench benchmark.

Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension (2022.acl-long)

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Ying Xu, Dakuo Wang, Mo Yu, Daniel Ritchie, Bingsheng Yao, Tongshuang Wu, Zheng Zhang, Toby Li, Nora Bradford, Branda Sun, Tran Hoang, Yisi Sang, Yufang Hou, Xiaojuan Ma, Diyi Yang, Nanyun Peng, Zhou Yu, Mark Warschauer

Question Answering

Challenge:	Existing QA datasets rarely distinguish fine-grained reading skills, such as the understanding of varying narrative elements.
Approach:	They propose to use FairytaleQA to generate 10,580 questions based on 278 children-friendly stories to assess model's fine-grained learning skills.
Outcome:	The proposed dataset consists of 10,580 questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations.

Emotion Transfer with Enhanced Prototype for Unseen Emotion Recognition in Conversation (2025.emnlp-main)

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Kun Peng, Cong Cao, Hao Peng, Guanlin Wu, Zhifeng Hao, Lei Jiang, Yanbing Liu, Philip S. Yu

Sentiment Analysis

Challenge:	Existing research on emotion recognition in conversation does not reach a consensus on classification theories . despite this, there is no clear consensus on how to recognize previously unseen emotions in real-world applications.
Approach:	They propose a prototype-based emotion transfer framework that can be used in real-world applications.
Outcome:	The proposed framework shows promise but still faces key challenges in the field of emotion recognition in conversation.

Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning (2026.acl-long)

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Zekai Lin, Chao Xue, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Lei Jiang, Yu Lu, Bob Simons, Shuang Liang, Minlong Peng

ℛ3: Advertisement Compliance ℛectification via Group-ℛelative Experience Extractor and Curriculum ℛeinforcement (2026.acl-industry)

Challenge:	Recent approaches to fine-tuning of large language models suffer from task interference and catastrophic forgetting.
Approach:	They propose a fine-tuning framework that adapts isolation decisions based on online estimates of parameter importance.
Outcome:	The proposed framework reduces interference and forgetting while releasing outdated parameters to recover plasticity.

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Yuan Chen, Zhenyu Hu, Mengge Xue, Cao Te, Liqun Liu, Peng Shu, Huan Yu, Jie Jiang

Gradient-guided Attention Map Editing: Towards Efficient Contextual Hallucination Mitigation (2025.findings-naacl)

Challenge:	Existing methods of content moderation are infeasible due to over-editing and compromise the advertiser’s original semantic intent.
Approach:	They propose a framework to harmonize compliance with original intent preservation that integrates a data-driven framework and a curriculum to enforce compliance while maximizing semantic consistency.
Outcome:	The proposed framework outperforms state-of-the-art baselines on industrial datasets and on online A/B testing on industrial video.

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Yu Wang, Jiaxin Zhang, Xiang Gao, Wendi Cui, Peng Li, Kamalika Das

Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models (2026.acl-long)

Challenge:	Large Language Models (LLMs) often experience “contextual hallucination” where they prioritize self-generated content over input context, leading to a disregard for pertinent details.
Approach:	They propose a method that dynamically adjusts attention maps to enhance contextual relevance by using a trained classifier to identify attention maps likely to induce hallucinations.
Outcome:	The proposed approach reduces hallucinations across open-source models on summarization and open-book QA tasks.

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Chao Xue, Yao Wang, Mengqiao Liu, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Chenyao Lu, Lei Jiang, Yu Lu, Haibo Shi, Shuang Liang, Minlong Peng, Flora D. Salim

Cross-lingual Text-to-SQL Semantic Parsing with Representation Mixup (2022.findings-emnlp)

Challenge:	Incomplete learning is widespread and heterogeneous in large language models . authors identify five recurrent sources of incomplete learning: missing prerequisite knowledge, conflicts between SFT supervision and pre-training knowledge, internal inconsistencies within SFT data, left-side forgetting during sequential fine-tuning, and insufficient optimization for rare or complex patterns.
Approach:	They propose a diagnostic-first framework that maps incomplete learning to causes . they identify five recurrent sources of incomplete learning: missing prerequisite knowledge, conflicts between supervision and pre-training knowledge, internal inconsistencies, left-side forgetting during sequential fine-tuning, and insufficient optimization for rare or complex patterns.
Outcome:	The proposed framework maps incomplete learning to causes using observable training and inference signals.

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Peng Shi, Linfeng Song, Lifeng Jin, Haitao Mi, He Bai, Jimmy Lin, Dong Yu

Retaining Key Information under High Compression Ratios: Query-Guided Compressor for LLMs (2024.acl-long)

Challenge:	Experimental results show that Rex can benefit from cross-lingual training and improve the effectiveness of semantic parsers.
Approach:	They propose a Representation Mixup Framework for effectively exploiting translations in the cross-lingual Text-to-SQL task.
Outcome:	The proposed framework can benefit from cross-lingual training and improve the effectiveness of semantic parsers, achieving state-of-the-art performance.

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Zhiwei Cao, Qian Cao, Yu Lu, Ningxin Peng, Luyang Huang, Shanbo Cheng, Jinsong Su

N-GRPO: Embedding-Level Neighbor Mixing for Enhanced Policy Optimization (2026.findings-acl)

Challenge:	Existing methods to compress long contexts have degraded dramatically as compression ratios increase, sometimes even falling to the closed-book level.
Approach:	They propose a query-guided compression method that preserves key information within the compressed context.
Outcome:	The proposed method can consistently perform well even at high compression ratios, and offers significant benefits in terms of inference cost and throughput.

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Xukun Zhu, Hang Yu, Peng Di, Linchao Zhu

FLAG-TRADER: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading (2025.findings-acl)

Challenge:	Recent studies show that Large Language Models can generate diverse solutions during the rollout phase.
Approach:	They propose a new approach that leverages Semantic Neighbor Mixing to generate diverse input representations by mixing anchor tokens and nearest semantic neighbors.
Outcome:	Experimental results show that the proposed approach improves on strong baselines and generalizes on out-of-distribution tasks.

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Guojun Xiong, Zhiyang Deng, Keyi Wang, Yupeng Cao, Haohang Li, Yangyang Yu, Xueqing Peng, Mingquan Lin, Kaleb E Smith, Xiao-Yang Liu, Jimin Huang, Sophia Ananiadou, Qianqian Xie

Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Geospatial Reasoning Questions (2026.findings-acl)

Challenge:	Large language models (LLMs) have impressive reasoning capabilities in financial tasks, but struggle with multi-step, goal-oriented scenarios in interactive financial markets.
Approach:	They propose a framework that integrates large language models with gradient-driven reinforcement learning (RL) policy optimization.
Outcome:	The proposed framework improves performance in trading and other financial domain tasks.

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Dazhou Yu, Riyang Bao, Ruiyu Ning, Jinghong Peng, Gengchen Mai, Liang Zhao

Natural Language Interfaces

Challenge:	Existing large language models lack spatial computing capabilities and access to up-to-date geospatial data.
Approach:	They propose a Retrieval-Augmented Generation framework for geospatial question answering . it integrates structured spatial databases with LLMs via a hybrid spatial retriever .
Outcome:	Experiments show that Spatial-RAG significantly improves over baselines.

ACQUIRED: A Dataset for Answering Counterfactual Questions In Real-Life Videos (2023.emnlp-main)

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Te-Lin Wu, Zi-Yi Dou, Qingyuan Hu, Yu Hou, Nischal Chandra, Marjorie Freedman, Ralph Weischedel, Nanyun Peng

Entropy Guided Extrapolative Decoding to Improve Factuality in Large Language Models (2025.coling-main)

Challenge:	despite its importance, there are few datasets that cover multimodal counterfactual reasoning . a dataset focusing on this area is limited because of its limited coverage over synthetic environments .
Approach:	They develop a video question answering dataset that provides questions on multimodal reasoning . they ask questions about counterfactual hypotheses over visual events .
Outcome:	The proposed dataset shows a significant performance gap between models and humans . it provides questions that span physical, social, and temporal dimensions .

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Souvik Das, Lifeng Jin, Linfeng Song, Haitao Mi, Baolin Peng, Dong Yu

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

Challenge:	Large language models (LLMs) exhibit impressive natural language capabilities but suffer from hallucination – generating content that does not align with realworld facts.
Approach:	They propose to extrapolate critical token probabilities beyond the last layer to improve decoding by manipulating the predicted distributions at inference time.
Outcome:	The proposed methods surpass state-of-the-art on multiple datasets by large margins.

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

INVESTORBENCH: A Benchmark for Financial Decision-Making Tasks with LLM-based Agent (2025.acl-long)

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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Haohang Li, Yupeng Cao, Yangyang Yu, Shashidhar Reddy Javaji, Zhiyang Deng, Yueru He, Yuechen Jiang, Zining Zhu, K.p. Subbalakshmi, Jimin Huang, Lingfei Qian, Xueqing Peng, Jordan W. Suchow, Qianqian Xie

Word Sense Disambiguation with Knowledge-Enhanced and Local Self-Attention-based Extractive Sense Comprehension (2022.coling-1)

Challenge:	Recent advances have underscored the potential of large language model (LLM)-based agents in financial decision-making.
Approach:	They propose to evaluate LLM agents using 13 different LLMs as backbone models across various market environments and tasks.
Outcome:	The proposed framework assesses the reasoning and decision-making capabilities of 13 different LLMs across various market environments and tasks.

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Guobiao Zhang, Wenpeng Lu, Xueping Peng, Shoujin Wang, Baoshuo Kan, Rui Yu

Reason Only When Needed: Efficient Generative Reward Modeling via Model-Internal Uncertainty (2026.findings-acl)

Challenge:	Word sense disambiguation (WSD) is one of the most challenging tasks in natural language processing.
Approach:	They propose a method to extract the right sense from a sentence context . they propose to incorporate additional examples and definitions of related senses in WordNet .
Outcome:	The proposed method achieves better performance than baseline models on public benchmark datasets.

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Chao Xue, Yao Wang, Mengqiao Liu, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Chenyao Lu, Lei Jiang, Yu Lu, Haibo Shi, Shuang Liang, Minlong Peng, Flora D. Salim

SimPBL: A Multi-Agent Framework for Project-Based Learning (2026.acl-long)

Challenge:	Existing approaches to generating reward models rely on voting-based mechanisms to evaluate CoT outputs.
Approach:	They propose an efficient generative reward modeling framework grounded in model-internal uncertainty.
Outcome:	The proposed framework reduces inference cost while improving answer accuracy.

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Daniel Zhang-Li, Joy Jia Yin Lim, Binglin Liu, Shangqing Tu, Zijun Yao, Hao Peng, Jifan Yu, Haoxuan Li, Zhanxin Hao, Ye He, Zekun Li, Jiangyi Wang, Lei Hou, Bin Xu, Xin Cong, Zhiyuan Liu, Huiqin Liu, Yu Zhang, Juanzi Li

Challenge:	Existing LLMs provide partial assistance without modeling these roles, and overly comprehensive help can reduce learner autonomy.
Approach:	They propose a multi-agent framework with an orchestrator agent that provides adaptive scaffolding from interaction logs and collaborator agents that support project work through boundary-aware collaboration.
Outcome:	The proposed framework improves learner examination scores by 14% . it is based on a multi-agent framework with an orchestrator agent .

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

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

Knowledge Representation

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

Investigating BERT’s Knowledge of Language: Five Analysis Methods with NPIs (D19-1)

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Alex Warstadt, Yu Cao, Ioana Grosu, Wei Peng, Hagen Blix, Yining Nie, Anna Alsop, Shikha Bordia, Haokun Liu, Alicia Parrish, Sheng-Fu Wang, Jason Phang, Anhad Mohananey, Phu Mon Htut, Paloma Jeretic, Samuel R. Bowman

Improving LLM Generations via Fine-Grained Self-Endorsement (2024.findings-acl)

Challenge:	Recent work evaluating sentence representation models' knowledge of grammar has been slower to emerge.
Approach:	They propose five experimental methods inspired by prior work evaluating pretrained sentence representation models to examine their grammatical knowledge.
Outcome:	The proposed methods show that the model has significant knowledge of the licensing environment but its success varies widely across different methods.

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Ante Wang, Linfeng Song, Baolin Peng, Lifeng Jin, Ye Tian, Haitao Mi, Jinsong Su, Dong Yu

On Measures of Biases and Harms in NLP (2022.findings-aacl)

Challenge:	Recent large language models (LLMs) have demonstrated remarkable capabilities but can still fail frequently on knowledge-intensive tasks.
Approach:	They propose a self-endorsement framework that leverages fine-grained fact-level comparisons across multiple sampled responses.
Outcome:	The proposed framework can improve factuality of generations with simple prompts across scales of LLMs.

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Sunipa Dev, Emily Sheng, Jieyu Zhao, Aubrie Amstutz, Jiao Sun, Yu Hou, Mattie Sanseverino, Jiin Kim, Akihiro Nishi, Nanyun Peng, Kai-Wei Chang

GBT: Generative Boosting Training Approach for Paraphrase Identification (2023.findings-emnlp)

Challenge:	Recent studies show that natural language processing (NLP) technologies propagate societal biases about demographic groups associated with attributes such as gender, race, and nationality.
Approach:	They propose a framework for harms and questions to help practitioners understand biases . they propose measurable measures to detect and mitigate biased groups .
Outcome:	The proposed framework provides a framework for harms and questions for practitioners to answer to guide the development of bias measures.

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Rui Peng, Zhiling Jin, Yu Hong

Improving Semantic Matching through Dependency-Enhanced Pre-trained Model with Adaptive Fusion (2022.findings-emnlp)

Challenge:	Paraphrase Identification (PI) is a fundamental natural language understanding task with non-trivial challenges.
Approach:	They propose a Generative Boosting Training approach for Paraphrase Identification (PI) they use a seq2seq model to perform DA on misclassified instances periodically .
Outcome:	The proposed method outperforms state-of-the-art PI models on English and Chinese PI tasks with good efficiency and effectiveness.

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Jian Song, Di Liang, Rumei Li, Yuntao Li, Sirui Wang, Minlong Peng, Wei Wu, Yongxin Yu

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

Challenge:	Existing work on dependency prior structure integration into pre-trained models is still unclear.
Approach:	They propose a dependency-based fusion attention paradigm which explicitly introduces dependency prior structure into pre-trained models and adaptively fuses it with semantic information.
Outcome:	The proposed model achieves state-of-the-art or competitive performance on 10 public datasets, demonstrating the benefits of adaptively fusing dependency structure in semantic matching task.

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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

VReST: Enhancing Reasoning in Large Vision-Language Models through Tree Search and Self-Reward Mechanism (2025.acl-long)

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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Congzhi Zhang, Jiawei Peng, Zhenglin Wang, Yilong Lai, Haowen Sun, Heng Chang, Fei Ma, Weijiang Yu

Multi-View Incongruity Learning for Multimodal Sarcasm Detection (2025.coling-main)

Challenge:	Large Vision-Language Models (LVLMs) have shown exceptional performance in multimodal tasks, but their effectiveness in complex visual reasoning is constrained.
Approach:	They propose a training-free approach that enhances Reasoning in Large Vision-Language Models . they propose integrating Monte Carlo Tree Search and Self-Reward mechanisms into the reasoning tree .
Outcome:	The proposed approach surpasses current prompting methods and secures state-of-the-art performance across three multimodal reasoning benchmarks.

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Diandian Guo, Cong Cao, Fangfang Yuan, Yanbing Liu, Guangjie Zeng, Xiaoyan Yu, Hao Peng, Philip S. Yu

Sentiment Analysis

Challenge:	Existing methods for multimodal sarcasm detection rely on spurious correlations, demonstrating poor generalizability beyond training environments.
Approach:	They propose a method that integrates multimodal incongruities via contrastive learning for multimodal sarcasm detection by using three views to drive multi-view learning.
Outcome:	The proposed method outperforms existing methods on benchmark datasets and shows that it is more generalizable than existing methods.

Logic-Consistency Text Generation from Semantic Parses (2021.findings-acl)

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Chang Shu, Yusen Zhang, Xiangyu Dong, Peng Shi, Tao Yu, Rui Zhang

MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)

Challenge:	Text generation from semantic parses is challenging due to the complexity of the inner logic and the lack of automatic evaluation metrics for logic consistency.
Approach:	They propose a framework for logic consistent text generation from semantic parses that employs iterative training procedures and quality control.
Outcome:	The proposed framework enhances logic consistency and human evaluation on two benchmark datasets.

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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie

TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale (2026.acl-industry)

Challenge:	Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models.
Approach:	They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts.
Outcome:	The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings .

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Jun Wang, Ziyin Zhang, Rui Wang, Hang Yu, Peng Di, Rui Wang

Challenge:	a 5minute downtime for an incident could result in a loss of 40 million dollars and erosion of user trust.
Approach:	They propose a multi-stage event unification engine that synergizes efficient indexing techniques with Large Language Models (LLMs) to make informed decisions on event merging.
Outcome:	The proposed system outperforms baseline methods in routing accuracy, clustering quality, and Signal-to-Noise Ratio.

Rethinking Cross-Subject Data Splitting for Brain-to-Text Decoding (2025.emnlp-main)

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Congchi Yin, Qian Yu, Zhiwei Fang, Changping Peng, Piji Li

Transforming Visual Scene Graphs to Image Captions (2023.acl-long)

Challenge:	Recent studies have successfully decoded natural language from non-invasive brain signals . current dataset splitting methods suffer from data leakage problem .
Approach:	They propose a right cross-subject data splitting criterion without data leakage for decoding fMRI and EEG signal to text.
Outcome:	The proposed method overfits and overestimates brain-to-text decoding models.

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Xu Yang, Jiawei Peng, Zihua Wang, Haiyang Xu, Qinghao Ye, Chenliang Li, Songfang Huang, Fei Huang, Zhangzikang Li, Yu Zhang

Self-supervised Cross-modal Pretraining for Speech Emotion Recognition and Sentiment Analysis (2022.findings-emnlp)

Challenge:	Existing approaches to generate captions using image captioning are based on multi-head attention (MHA)
Approach:	They propose to transform scene graphs into more descriptive captions by using multi-head attention to build a Graph Neural Network (GNN) . they construct a Mixture-of-Expert (MOE)-based decoder where each expert is built on MHA for discriminating the graph embeddings to generate different kinds of words.
Outcome:	The proposed framework can generate captions from multiple visual features and objects . it is based on a mixture-of-expert (MOE)-based decoder based upon MHA .

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Iek-Heng Chu, Ziyi Chen, Xinlu Yu, Mei Han, Jing Xiao, Peng Chang

Localizing Active Objects from Egocentric Vision with Symbolic World Knowledge (2023.emnlp-main)

Challenge:	Existing approaches to multimodal speech emotion recognition and sentiment analysis have not improved results due to their relatively simple fusion mechanisms and lack of proper cross-modal pretraining.
Approach:	They propose a deep-fused audio-text bi-modal transformer with carefully designed cross-modal fusion mechanism and stage-wise cross-mod pretraining scheme to facilitate cross-modulation.
Outcome:	The proposed method exceeds benchmarks on public IEMOCAP emotion and CMU-MOSEI sentiment datasets by a large margin.

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Te-Lin Wu, Yu Zhou, Nanyun Peng

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization (2025.findings-emnlp)

Challenge:	Existing work on active object grounding from an egocentric perspective is focusing on localizing and tracking active objects that undergo major state change as a result of human actions/interactions to the environment without being told exactly what/where to ground.
Approach:	They propose to use a narrated egocentric video dataset to localize and track active objects that undergo major state change as a result of human actions/interactions to the environment without being told exactly what/where to ground.
Outcome:	The proposed framework leads to 54% improvement in standard metrics on the TREK-150-OPE-Det localization + tracking task, and >7% improvement in all standard metrics.

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Zhensheng Jin, Xinze Li, Yifan Ji, Chunyi Peng, Zhenghao Liu, Qi Shi, Yukun Yan, Shuo Wang, Furong Peng, Ge Yu

Towards Faithful Industrial RAG: A Reinforced Co-adaptation Framework for Advertising QA (2026.acl-industry)

Challenge:	Existing methods to train LLMs suffer from overthinking, leading to lengthy reasoning traces . Existing approaches to train large language models suffer from this problem .
Approach:	They propose a method to combine multiple reasoning chains for training LLMs . they use stepwise exploration and long-short switched sampling to evaluate reasoning paths .
Outcome:	The proposed method reduces reasoning lengths by approximately 30-50% . it also maintains or improves reasoning accuracy compared to baselines .

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Wenwei Li, Ming Xu, Tianle Xia, Lingxiang Hu, Yiding Sun, Linfang Shang, Liqun Liu, Peng Shu, Huan Yu, Jie Jiang

Structured Data in NLP

Challenge:	Existing methods for QA in industrial environments are inherently relational and often updated.
Approach:	They propose a framework that optimizes retrieval and generation through two components: Graph-aware Retrieval and evidence-constrained reinforcement learning.
Outcome:	Experiments on an internal advertising QA dataset show consistent gains across expert-judged dimensions including accuracy, completeness, safety, and URL validity.

HTCInfoMax: A Global Model for Hierarchical Text Classification via Information Maximization (2021.naacl-main)

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Zhongfen Deng, Hao Peng, Dongxiao He, Jianxin Li, Philip Yu

CI-AVSR: A Cantonese Audio-Visual Speech Datasetfor In-car Command Recognition (2022.lrec-1)

Challenge:	Existing models for hierarchical text classification do not consider statistical constraint on label representations learned by structure encoder.
Approach:	They propose a new hierarchical text classification model called HTCInfoMax which incorporates two modules to improve the model's representations.
Outcome:	The proposed model can model the interaction between each text sample and its ground truth labels explicitly which filters out irrelevant information.

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Wenliang Dai, Samuel Cahyawijaya, Tiezheng Yu, Elham J. Barezi, Peng Xu, Cheuk Tung Yiu, Rita Frieske, Holy Lovenia, Genta Winata, Qifeng Chen, Xiaojuan Ma, Bertram Shi, Pascale Fung

Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study (2025.coling-main)

Challenge:	In-car smart assistants should be able to process general as well as car-related commands and perform corresponding actions, which eases driving and improves safety.
Approach:	They propose a dataset for in-car command recognition in the cantonese language with both video and audio data.
Outcome:	The proposed model can achieve a considerable quality on the clean test set, but the speech recognition quality on noisy data is still inferior.

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Bowen Li, Wenhan Wu, Ziwei Tang, Lin Shi, John Yang, Jinyang Li, Shunyu Yao, Chen Qian, Binyuan Hui, Qicheng Zhang, Zhiyin Yu, He Du, Ping Yang, Dahua Lin, Chao Peng, Kai Chen

ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation (2022.lrec-1)

Challenge:	Existing benchmarks focused on simplified or isolated aspects of coding, ignoring the full spectrum of programming challenges.
Approach:	They propose a case study that examines the performance of large language models across the entire software development lifecycle with four programming languages, multiple domains, and carefully designed and verified metrics for each task.
Outcome:	The proposed model performs across the entire software development lifecycle, including design, environment setup, implementation, acceptance testing, and unit testing.

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Holy Lovenia, Samuel Cahyawijaya, Genta Winata, Peng Xu, Yan Xu, Zihan Liu, Rita Frieske, Tiezheng Yu, Wenliang Dai, Elham J. Barezi, Qifeng Chen, Xiaojuan Ma, Bertram Shi, Pascale Fung

Dialogue Systems & Conversational Agents

Challenge:	Code-switching is a speech phenomenon occurring when a speaker switches language during a conversation.
Approach:	They propose to collect Mandarin Chinese-English code-switching corpus from read speech rather than spontaneous speech to address this phenomenon.
Outcome:	ASCEND consists of 10.62 hours of clean speech, collected from 23 bilingual speakers of Chinese and English.

Predicting Rewards Alongside Tokens: Non-disruptive Parameter Insertion for Efficient Inference Intervention in Large Language Model (2024.emnlp-main)

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Chenhan Yuan, Fei Huang, Ru Peng, Keming Lu, Bowen Yu, Chang Zhou, Jingren Zhou

When Safety Alignment Fails to Generalize: Probing with Language Game Jailbreaks (2026.findings-acl)

Challenge:	Existing approaches to fine tune LLMs produce unsafe responses and unreliable reasoning, but this solution introduces substantial time and space overhead due to the separate models required.
Approach:	They propose to insert extra parameters into transformer architecture to predict calibration signals along with original LLM output.
Outcome:	The proposed model reduces time and space costs while enabling seamless online deployment.

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Zewen Long, Yu Peng, Fangming Dong, Congyi Li, Xingmao Guan, Shu Wu, Kai Chen

On Training Data Influence of GPT Models (2024.emnlp-main)

Challenge:	Existing safety alignment methods rely on fixed or narrow transformation schemes to generalize . existing methods based on fixed and narrow transformations are often inadequate .
Approach:	They propose a framework for discovering and refining language game-based jailbreaks to probe alignment generalization.
Outcome:	The proposed framework allows controlled exploration of alignment behavior across closely related linguistic variants.

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Yekun Chai, Qingyi Liu, Shuohuan Wang, Yu Sun, Qiwei Peng, Hua Wu

DIONYSUS: A Pre-trained Model for Low-Resource Dialogue Summarization (2023.acl-long)

Challenge:	generative language models have redefined performance standards across tasks . current research on the influence of training data on autoregressivity remains underexplored .
Approach:	They propose a parameterized simulation to assess the impact of training examples on the training dynamics of GPT models.
Outcome:	The proposed approach compares existing methods with existing methods across training scenarios in generative language models, spanning tasks across 14 million to 2.8 billion parameters.

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Yu Li, Baolin Peng, Pengcheng He, Michel Galley, Zhou Yu, Jianfeng Gao

Dialogue Systems & Conversational Agents

Challenge:	Existing methods for summarizing dialogues lack in taking into account the structure of dialogues and rely heavily on labeled data.
Approach:	They propose a pre-trained encoder-decoder model for summarizing dialogues in any new domain.
Outcome:	The proposed model outperforms existing methods on six datasets and shows ROUGE scores in zero-shot and few-shot settings.

Question-Interlocutor Scope Realized Graph Modeling over Key Utterances for Dialogue Reading Comprehension (2023.findings-acl)

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Jiangnan Li, Mo Yu, Fandong Meng, Zheng Lin, Peng Fu, Weiping Wang, Jie Zhou

Natural Language Interfaces

Challenge:	Compared to standard RC tasks, dialogue reading comprehension (DRC) has raised challenges because of the complex speaker information and noisy dialogue context.
Approach:	They propose a new method for dialogue reading comprehension that extracts answers from dialogues by using key-utterances-extracting methods and a Question-Interlocutor Scope Realized Graph.
Outcome:	The proposed method achieves state-of-the-art performance against previous works.

Automate Strategy Finding with LLM in Quant Investment (2025.findings-emnlp)

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Zhizhuo Kou, Holam Yu, Junyu Luo, Jingshu Peng, Xujia Li, Chengzhong Liu, Juntao Dai, Lei Chen, Sirui Han, Yike Guo

Teaching Language Models to Self-Improve through Interactive Demonstrations (2024.naacl-long)

Challenge:	Experimental results demonstrate robust performance of the strategy in Chinese & US market regimes compared to established benchmarks.
Approach:	They propose a framework leveraging Large Language Models within a risk-aware multi-agent system for automate strategy finding in quantitative finance.
Outcome:	The proposed framework outperforms all benchmarks in Chinese & US market regimes with 53.17% cumulative return on SSE50.

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Xiao Yu, Baolin Peng, Michel Galley, Jianfeng Gao, Zhou Yu

GALLa: Graph Aligned Large Language Models for Improved Source Code Understanding (2025.acl-long)

Challenge:	Large language models (LLMs) have been shown to improve performance on downstream tasks by prompting them to analyze and revise their outputs.
Approach:	They propose a training algorithm that prompts large language models to analyze and revise their own outputs and uses this feedback to train the small model.
Outcome:	The proposed approach improves LLaMA-7B's performance on math and reasoning tasks by up to 7.13%.

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Ziyin Zhang, Hang Yu, Sage Lee, Peng Di, Jianguo Li, Rui Wang

CityNavAgent: Aerial Vision-and-Language Navigation with Hierarchical Semantic Planning and Global Memory (2025.acl-long)

Challenge:	Programming languages have rich semantics that are represented by graphs and not available from the surface form of source code.
Approach:	They propose to use graph neural networks and cross-modal alignment technologies to inject structural information of code into LLMs as an auxiliary task during finetuning.
Outcome:	The proposed framework improves on five code tasks with six different baseline LLMs, while incurring no cost at inference time.

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Weichen Zhang, Chen Gao, Shiquan Yu, Ruiying Peng, Baining Zhao, Qian Zhang, Jinqiang Cui, Xinlei Chen, Yong Li

Evaluating the Expressive Appropriateness of Speech in Rich Contexts (2026.acl-long)

Challenge:	Existing ground VLN agents struggle in aerial VLLN due to the lack of predefined navigation graphs and the exponentially expanding action space in long-horizon exploration.
Approach:	They propose a large language model-empowered aerial VLN agent that decomposes the long-horizon task into sub-goals with different semantic levels.
Outcome:	The proposed method achieves state-of-the-art performance with significant improvement in continuous city environments.

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Tianrui Wang, Ziyang Ma, Yizhou Peng, Haoyu Wang, Zhikang Niu, Zikang Huang, Yihao Wu, Yi-Wen Chao, Yu Jiang, Yuheng Lu, Guanrou Yang, Xuanchen Li, Hexin Liu, Chunyu Qiang, Cheng Gong, Yifan Yang, Tianchi Liu, Junyu Wang, Nana Hou, Meng Ge, Fuming You, Yang Wei, Zhongqian Sun, Hu Haifeng, Xiaobao Wang, Eng Siong Chng, Xie Chen, Longbiao Wang, Jianwu Dang

Speech & Audio in NLP

Challenge:	Existing methods for evaluating expressive speech focus on word accuracy, naturalness, signal quality, or emotional intensity at the utterance level.
Approach:	They propose a framework for Evaluating Expressive Appropriateness in speech that assesses whether a speech sample aligns with the underlying communicative intent implied by its discourse-level narrative context.
Outcome:	The proposed framework outperforms existing speech evaluation and analysis systems on a human-annotated test set.

Search-P1: Path-Centric Reward Shaping for Stable and Efficient Agentic RAG Training (2026.acl-industry)

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Tianle Xia, Ming Xu, Lingxiang Hu, Yiding Sun, Wenwei Li, Linfang Shang, Liqun Liu, Peng Shu, Huan Yu, Jie Jiang

UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models (2022.emnlp-main)

Challenge:	Retrieval-Augmented Generation (RAG) enhances large language models by incorporating external knowledge, yet traditional singleround retrieval struggles with complex multistep reasoning.
Approach:	They propose a framework that introduces path-centric reward shaping for agentic RAG training.
Outcome:	The proposed framework improves on existing methods with an average accuracy gain of 7.7 points.

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Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zettlemoyer, Tao Yu

Lang2Act: Fine-Grained Visual Reasoning through Self-Emergent Linguistic Toolchains (2026.findings-acl)

Challenge:	Structured knowledge grounding (SKG) uses structured knowledge to complete user requests . since inputs and outputs of SKG tasks are heterogeneous, they have been studied separately .
Approach:	They propose a framework that unifies 21 SKG tasks into a text-to-text format . they use unifiedSKG to benchmark T5 with different sizes .
Outcome:	The proposed framework unifies 21 SKG tasks into a text-to-text format . it achieves state-of-the-art performance on almost all of the 21 tasks, the authors show .

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Yuqi Xiong, Chunyi Peng, Zhipeng Xu, Zhenghao Liu, Zulong Chen, Yukun Yan, Shuo Wang, Yu Gu, Ge Yu

CharTide: Data-Centric Chart-to-Code Generation via Tri-Perspective Tuning and Inquiry-Driven Evolution (2026.acl-long)

Challenge:	Existing frameworks depend on rigid, pre-defined external tools to extend perceptual capabilities of VLMs.
Approach:	They propose a framework that leverages self-emergent linguistic toolchains to enhance visual perception and reasoning.
Outcome:	The proposed framework improves the visual perception capabilities of large language models by incorporating external visual documents to address a given query.

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Xiangxi Zheng, Kuang He, Jiayi Hu, Ping Yu, Rui Yan, Yuan Yao, Peng Hou, Anxiang Zeng, Alex Jinpeng Wang

ManCC: A Task-Anchored Benchmark for Manchu–Classical Chinese Cross-Lingual Modeling (2026.findings-acl)

Challenge:	Existing approaches to chart-to-code generation are constrained by data-centric limitations . authors present a new framework that redesigns both training and alignment data .
Approach:	They propose a data-centric framework that redesigns both training and alignment data for chart-to-code generation.
Outcome:	The proposed framework outperforms open-source baselines and is competitive with GPT-5.

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Meiqi Wang, Xiaoxin Sun, Dongjie Wang, Ruixin Yu, Xiantao Heng, Shuo Wang, Zhen Huang, Peng Zhao, Suhua Wang, Minghao Yin

A Framework of Reflective Agents with Adaptive Collaboration for Attributed Summary Generation (2026.findings-acl)

Challenge:	Mainstream research in natural language processing has focused on high-resource and modern languages.
Approach:	They propose a task-anchored benchmark for Manchu–Classical Chinese translation . they use a parallel corpus of 16,627 sentence pairs to evaluate the model .
Outcome:	The proposed benchmarks show that linguistic differences influence performance and broader language coverage facilitate low-resource transfer.

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Yu Chen, Peng Chen, Ziwei Zheng, Bang Wang

Inference Compute-Optimal Video Vision Language Models (2025.acl-long)

Challenge:	Existing prompt-based summarization approaches face limitations such as positional preference, poor citation quality and sensitivity to uninformative documents.
Approach:	They propose a framework of Reflective Agents with Adaptive Collaboration for attributed summarization that performs iterative summarizing via reflective agents’ collaboration.
Outcome:	The proposed framework outperforms baselines on the ALCE benchmark in factual correctness and citation quality.

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Peiqi Wang, ShengYun Peng, Xuewen Zhang, Hanchao Yu, Yibo Yang, Lifu Huang, Fujun Liu, Qifan Wang

Visual Data in NLP

Challenge:	Using video vision language models, inference costs are often more expensive than finetuning.
Approach:	They investigate the optimal allocation of inference compute across three key scaling factors in video vision language models.
Outcome:	The proposed model configurations are based on three key scaling factors . the results can be applied to real-world tasks and tasks with fixed inference budgets.

T2R-BENCH: A Benchmark for Real World Table-to-Report Task (2025.emnlp-main)

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Jie Zhang, Changzai Pan, Sishi Xiong, Kaiwen Wei, Yu Zhao, Xiangyu Li, Jiaxin Peng, Xiaoyan Gu, Jian Yang, Wenhan Chang, Zhenhe Wu, Jiang Zhong, Shuangyong Song, Xuelong Li

Structured Data in NLP

Challenge:	Existing table benchmarks lack the capacity to adequately assess the practical application of table reasoning in industrial applications.
Approach:	They propose a bilingual table-to-report task and a table-based benchmark to assess the quality of table reasoning.
Outcome:	The proposed task is based on a bilingual benchmark with 457 industrial tables and evaluation criteria to measure the quality of report generation.

Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future (2024.acl-long)

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Zheng Chu, Jingchang Chen, Qianglong Chen, Weijiang Yu, Tao He, Haotian Wang, Weihua Peng, Ming Liu, Bing Qin, Ting Liu

Hierarchical Relation Extraction with Coarse-to-Fine Grained Attention (D18-1)

Challenge:	Recent studies have revealed that chain-of-thought prompting significantly enhances LLM’s reasoning capabilities, which attracts widespread attention from both academics and industry.
Approach:	They propose to summarize advanced methods through a taxonomy that offers novel perspectives.
Outcome:	The proposed method delineates the challenges and future directions, thereby shedding light on future research.

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Xu Han, Pengfei Yu, Zhiyuan Liu, Maosong Sun, Peng Li

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

Challenge:	Existing methods for relation extraction use knowledge graphs to automatically label training data . but, it suffers from the wrong labeling problem because not all sentences containing two entities can express their relations in KGs .
Approach:	They propose a distant supervision approach to automatically label training instances . they integrate hierarchical information of relations into distantly supervised relation extraction .
Outcome:	The proposed model outperforms baseline models on a large-scale dataset.

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

VisKoP: Visual Knowledge oriented Programming for Interactive Knowledge Base Question Answering (2023.acl-demo)

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

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Zijun Yao, Yuanyong Chen, Xin Lv, Shulin Cao, Amy Xin, Jifan Yu, Hailong Jin, Jianjun Xu, Peng Zhang, Lei Hou, Juanzi Li

Bidirectional LMs are Better Knowledge Memorizers? A Benchmark for Real-world Knowledge Injection (2026.acl-long)

Challenge:	Existing knowledge base question answering systems that parse natural language questions into knowledge oriented program language (KoPL) .
Approach:	They propose a knowledge base question answering system that integrates human into the loop to edit and debug queries.
Outcome:	The proposed system can debug and edit knowledge base questions on a million-entity-level . it provides auto-completion for its knowledge base schema and user interaction can fix a large portion of wrong KoPL programs to acquire the correct answer.

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Yuwei Zhang, Wenhao Yu, Shangbin Feng, Yifan Zhu, Letian Peng, Jayanth Srinivasa, Gaowen Liu, Jingbo Shang

CaDRL: Document-level Relation Extraction via Context-aware Differentiable Rule Learning (2025.coling-main)

Challenge:	Existing knowledge injection benchmarks for large language models lack standardized testing grounds.
Approach:	They propose a knowledge injection benchmark that leverages recently-added and expert-curated facts from Wikipedia’s “Did You Know...” entries.
Outcome:	The proposed framework improves reliability accuracy by 29.1%.

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Kunli Zhang, Pengcheng Wu, Bohan Yu, Kejun Wu, Aoze Zheng, Xiyang Huang, Chenkang Zhu, Min Peng, Hongying Zan, Yu Song

ChartAssistant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction Tuning (2024.findings-acl)

Challenge:	Existing methods for document-level relation extraction (DocRE) lack logic and transparency.
Approach:	They propose a Context-aware differentiable rule learning framework that learns the doc-specific logical rule to avoid suboptimal constraints.
Outcome:	The proposed framework outperforms existing rule-based frameworks on three DocRE datasets.

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Fanqing Meng, Wenqi Shao, Quanfeng Lu, Peng Gao, Kaipeng Zhang, Yu Qiao, Ping Luo

Event Detection from Social Media for Epidemic Prediction (2024.naacl-long)

Challenge:	Charts are an effective tool for understanding data patterns, but their combination of graphical elements and textual components poses challenges for general-purpose multimodal models.
Approach:	They propose a chart-based vision-language model for universal chart comprehension and reasoning that leverages a dataset of chart-related tasks.
Outcome:	The proposed model outperforms the state-of-the-art charts with zero-shot setting on various chart tasks.

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Tanmay Parekh, Anh Mac, Jiarui Yu, Yuxuan Dong, Syed Shahriar, Bonnie Liu, Eric Yang, Kuan-Hao Huang, Wei Wang, Nanyun Peng, Kai-Wei Chang

Hierarchical Bi-Directional Self-Attention Networks for Paper Review Rating Recommendation (2020.coling-main)

Challenge:	Social media is an easy-to-access platform providing timely updates about societal trends and events.
Approach:	They propose a framework to extract epidemic-related events from social media posts to provide early warnings.
Outcome:	The proposed framework can detect epidemic events for three unseen epidemics of Monkeypox, Zika, and Dengue while existing models fail miserably.

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Zhongfen Deng, Hao Peng, Congying Xia, Jianxin Li, Lifang He, Philip Yu

BeamLoRA: Beam-Constraint Low-Rank Adaptation (2025.acl-long)

Challenge:	Existing methods for review rating prediction ignore hierarchies among data . paper review rating predictions are important for improving paper review process .
Approach:	They propose a Hierarchical bi-directional self-attention Network framework for paper review rating prediction and recommendation . they leverage hierarchical structure of paper reviews with three levels of encoders .
Outcome:	The proposed approach can be used to make an effective decision-making tool for the academic paper review process.

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Naibin Gu, Zhenyu Zhang, Xiyu Liu, Peng Fu, Zheng Lin, Shuohuan Wang, Yu Sun, Hua Wu, Weiping Wang, Haifeng Wang

Neeko: Leveraging Dynamic LoRA for Efficient Multi-Character Role-Playing Agent (2024.emnlp-main)

Challenge:	Low-Rank Adaptation (LoRA) is one of the most efficient parameter-efficient fine-tuning methods.
Approach:	They propose to conceptualize each LoRA module as a beam where each rank corresponds to a potential sub-solution.
Outcome:	The proposed method improves performance on three base models and 12 datasets.

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Xiaoyan Yu, Tongxu Luo, Yifan Wei, Fangyu Lei, Yiming Huang, Hao Peng, Liehuang Zhu

Green & Sustainable NLP

Challenge:	Large Language Models (LLMs) have revolutionized open-domain dialogue agents but face challenges in multi-character role-playing (MCRP) scenarios.
Approach:	They propose a framework for efficient multi-character role-playing that employs a dynamic low-rank adapter strategy and distinct LoRA blocks for each character.
Outcome:	Neeko employs a dynamic low-rank adapter (LoRA) strategy, enabling it to adapt seamlessly to diverse characters.

SSR-A: Spatial- and Semantic-Aware Instructions and Curriculum Reinforcement for Advertisement Compliant Rectification (2026.acl-industry)

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Cao Te, Mengge Xue, Zhenyu Hu, Yuan Chen, Liqun Liu, Peng Shu, Huan Yu, Jie Jiang

Q2EI: Query-to-Entity Inference for Semantic Condensation in Domain-Specific Retrieval (2026.findings-acl)

Challenge:	Existing methods to fix non-compliant images suffer from over-editing, destroying original intent and perceptual similarity.
Approach:	They propose a framework for the minimalist rectification of non-compliant image ads.
Outcome:	The proposed framework outperforms state-of-the-art baselines in both compliance and preservation of visual and commercial consistency.

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Yixuan Sun, Zhenqin Xu, HanFeng Zhai, Zishu Yu, Xiaohui Peng

Paraphrasing

Challenge:	Existing generative expansions introduce redundancy or hallucinations that cause semantic drift.
Approach:	They propose a query rewriting strategy that reframes rewrite as semantic condensation rather than expansion.
Outcome:	The proposed method outperforms baselines on medical and legal benchmarks while reducing token consumption.

AdvancedIF: Rubric-Based Benchmarking and Reinforcement Learning for Advancing LLM Instruction Following (2026.acl-long)

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Yun He, Wenzhe Li, Hejia Zhang, Songlin Li, Karishma Mandyam, Sopan Khosla, Yuanhao Xiong, Nanshu Wang, Xiaoliang Peng, Beibin Li, Shengjie Bi, Shishir G Patil, Qi Qi, Shengyu Feng, Julian Katz-Samuels, Richard Yuanzhe Pang, Sujan Kumar Gonugondla, Hunter Lang, Yue Yu, Yundi Qian, Maryam Fazel-Zarandi, Licheng Yu, Amine Benhalloum, Hany Hassan Awadalla, Manaal Faruqui

Learning Fine-Grained Grounded Citations for Attributed Large Language Models (2024.findings-acl)

Challenge:	Recent advances in large language models (LLMs) have shown impressive performance on a range of tasks, yet advanced instruction following (IF) remains a significant challenge.
Approach:	They propose a benchmark that features over 1,600 prompts and expert-curated rubrics that assess LLMs’ ability to follow complex, multi-turn, and system-level instructions.
Outcome:	The proposed framework improves instruction-following abilities of large language models, achieving a 6.7% gain on AdvancedIF and strong results on public benchmarks.

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Lei Huang, Xiaocheng Feng, Weitao Ma, Yuxuan Gu, Weihong Zhong, Xiachong Feng, Weijiang Yu, Weihua Peng, Duyu Tang, Dandan Tu, Bing Qin

ARGUS: Policy-Adaptive Ad Governance via Evolving Reinforcement with Adversarial Umpiring (2026.acl-industry)

Challenge:	despite impressive performance, large language models still struggle with hallucinations . current approaches suffer from suboptimal citation quality due to reliance on in-context learning .
Approach:	They propose a framework that teaches large language models to generate fine-grained citations.
Outcome:	The proposed framework outperforms all baselines on the ALCE benchmark and achieves an average improvement of 14.21% in citation quality.

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Deyi Ji, Junyu Lu, Xuanyi Liu, Liqun Liu, Hailong Zhang, Peng Shu, Huan Yu, Jie Jiang, Tianrun Chen, Lanyun Zhu

Separate the Wheat from the Chaff: Winnowing Down Divergent Views in Retrieval Augmented Generation (2025.emnlp-main)

Challenge:	Existing regulatory policies create label inconsistencies and reasoning ambiguities in historical datasets.
Approach:	They propose a policy-adaptive governance system that enables evolving reinforcement through multi-agent adversarial umpiring.
Outcome:	The proposed system outperforms fine-tuning baselines on industrial and public datasets . it enables evolving reinforcement through multi-agent adversarial umpiring .

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Song Wang, Zihan Chen, Peng Wang, Zhepei Wei, Zhen Tan, Yu Meng, Cong Shen, Jundong Li

Text Clustering

Challenge:	Large language models (LLMs) lack robustness in knowledge-intensive tasks due to noisy or irrelevant retrieved data.
Approach:	They propose a multi-agent debate-based RAG framework that integrates external knowledge sources into large language models to improve their accuracy.
Outcome:	The proposed framework is unsupervised and leverages pretrained LLMs without fine-tuning, making it easily adaptable to various tasks.

Activation-Guided Local Editing for Jailbreaking Attacks (2026.acl-long)

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Jiecong Wang, Haoran Li, Hao Peng, Ziqian Zeng, Zihao Wang, Haohua Du, Zhengtao Yu

Robustness in NLP

Challenge:	Existing methods for jailbreaking Large Language Models (LLMs) are limited and produce incoherent or unreadable inputs.
Approach:	They propose a two-stage framework that performs a one-shot, scenario-based generation of context and rephrases the original malicious query to obscure its harmful intent.
Outcome:	The proposed framework achieves state-of-the-art Attack Success Rate, with gains of up to 37.74% over the strongest baseline, and excellent transferability to black-box and large-scale models.

STEM: Structure-Tracing Evidence Mining for Knowledge Graphs-Driven Retrieval-Augmented Generation (2026.acl-long)

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Peng Yu, En Xu, Bin Chen, Haibiao Chen, Yinfei Xu

ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding (2022.findings-emnlp)

Challenge:	Existing reasoning path retrieval methods lack a global structural perspective.
Approach:	They propose a framework that reframes multi-hop reasoning as a schema-guided graph search task.
Outcome:	The proposed framework improves accuracy and evidence completeness of multi-hop reasoning graph retrieval.

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Qiming Peng, Yinxu Pan, Wenjin Wang, Bin Luo, Zhenyu Zhang, Zhengjie Huang, Yuhui Cao, Weichong Yin, Yongfeng Chen, Yin Zhang, Shikun Feng, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang