Papers by Fei Yu

OVM, Outcome-supervised Value Models for Planning in Mathematical Reasoning (2024.findings-naacl)

Challenge:	Large language models (LLMs) rely on safety alignment to avoid malicious user inputs.
Approach:	They employ weak classifiers to explain LLM safety through the intermediate hidden states.
Outcome:	The proposed model can identify malicious and normal inputs and detect malicious ones without jailbreak.

Copied to clipboard

Fei Yu, Anningzhe Gao, Benyou Wang

STARS: A Unified Framework for Singing Transcription, Alignment, and Refined Style Annotation (2025.findings-acl)

Challenge:	Large language models (LLMs) struggle with maintaining accuracy throughout multiple reasoning steps, especially in mathematical reasoning where an error in earlier steps can propagate to subsequent ones and ultimately leading to an incorrect answer.
Approach:	They propose an Outcome-supervised Value Model (OVM) that employs outcome supervision for training a value model, which prioritizes steps that lead to accurate conclusions.
Outcome:	The proposed model performs better on two multi-step reasoning datasets, GSM8K and Game of 24.

Copied to clipboard

Wenxiang Guo, Yu Zhang, Changhao Pan, Zhiyuan Zhu, Ruiqi Li, ZheTao Chen, Wenhao Xu, Fei Wu, Zhou Zhao

Speech & Audio in NLP

Challenge:	Existing automated singing annotation (ASA) methods tackle isolated aspects of the annotation pipeline.
Approach:	They propose a framework that addresses transcription, alignment, and refined style annotations.
Outcome:	The proposed framework delivers comprehensive multi-level annotations encompassing: (1) precise phoneme-audio alignment, (2) robust note transcription and temporal localization, (3) expressive vocal technique identification, and (4) global stylistic characterization including emotion and pace.

Are Large Language Models Reliable Reviewers? A Benchmark for Error Detection in Financial Documents (2026.findings-acl)

Copied to clipboard

Ying He, Zhouhong Gu, Zhecheng Hu, Yubo Zhou, Hao Shen, Jiaqing Liang, Zhaoqian Dai, Ma Shuguang, Fei Yu, Yanghua Xiao, Zhixu Li

IOPO: Empowering LLMs with Complex Instruction Following via Input-Output Preference Optimization (2025.acl-long)

Challenge:	Existing LLMs struggle to identify errors in financial documents, a study shows . 18% of financial practitioners make errors daily, one-third make errors several times weekly, and 59% make errors multiple times monthly.
Approach:	They introduce FinED-Bench, a publicly available Benchmark for financial error detection . it covers nine real-world financial scenarios and includes over 900 documents in 2025 . supervised fine-tuning can significantly improve the performance of weaker LLMs, they show .
Outcome:	The proposed benchmark covers nine real-world financial scenarios and includes over 900 documents reported in 2025 that are unseen by existing language models.

Copied to clipboard

Xinghua Zhang, Haiyang Yu, Cheng Fu, Fei Huang, Yongbin Li

Challenge:	Existing algorithms to improve the ability of LLMs to follow complex instructions are lacking.
Approach:	They propose a benchmark to improve the ability to follow complex instructions by using a IOPO alignment method to take input and output preference into consideration.
Outcome:	The proposed algorithm shows 8.15%, 2.18% improvements on in-domain data and 5.91%, 2.83% on out-of-domain datasets compared to SFT and DPO respectively.

Order Doesn’t Matter, But Reasoning Does: Training LLMs with Order-Centric Augmentation (2025.emnlp-main)

Copied to clipboard

Qianxi He, Qianyu He, Jiaqing Liang, Weikang Zhou, Zeye Sun, Fei Yu, Yanghua Xiao

Guided Knowledge Generation with Language Models for Commonsense Reasoning (2024.findings-emnlp)

Challenge:	Logical reasoning is essential for large language models (LLMs) to ensure accurate and coherent inferences.
Approach:	They propose an order-centric data augmentation framework based on commutativity in logical reasoning that randomly shuffles independent premises to introduce condition order augmentation.
Outcome:	The proposed framework improves LLMs’ reasoning performance and adaptability to diverse logical structures.

Copied to clipboard

Xiao Wei, Haoran Chen, Hang Yu, Hao Fei, Qian Liu

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have achieved notable success in commonsense reasoning tasks, benefiting from extensive world knowledge acquired through extensive pretraining.
Approach:	They propose a method to generate knowledge explanations and to automatically assign labels based on the probability of correct answers.
Outcome:	The proposed method outperforms baselines on four widely-used commonsense reasoning benchmarks and shows that it can generate high quality knowledge leading to correct answers.

Copied to clipboard

Yixiao Zhou, Ziyu Zhao, Dongzhou Cheng, Zhiliang Wu, Jie Gui, Yi Yang, Fei Wu, Yu Cheng, Hehe Fan

Generating User-Engaging News Headlines (2023.acl-long)

Challenge:	Sparse Mixture-of-Experts (SMoE) architectures require loading all expert parameters . previous work focused on expert pruning and merging but focused on neuron-level structure .
Approach:	They propose a task-agnostic framework for expert pruning and reconstruction . it prunes redundant experts using router statistics, then decomposes them into neuron-level expert segments .
Outcome:	The proposed framework reduces the number of experts and memory usage, making it easier to deploy.

Copied to clipboard

Pengshan Cai, Kaiqiang Song, Sangwoo Cho, Hongwei Wang, Xiaoyang Wang, Hong Yu, Fei Liu, Dong Yu

DeFine: Decision-Making with Analogical Reasoning over Factor Profiles (2025.findings-acl)

Challenge:	Personalized news recommendation systems present the same headline to all users, making it difficult for them to understand the connection between their interests and the recommended article.
Approach:	They propose a framework that incorporates user profiling to generate personalized headlines and a combination of automated and human evaluation methods to determine user preference for personalized headline generation.
Outcome:	The proposed framework can generate personalized headlines that meet the needs of a diverse audience.

Copied to clipboard

Yebowen Hu, Xiaoyang Wang, Wenlin Yao, Yiming Lu, Daoan Zhang, Hassan Foroosh, Dong Yu, Fei Liu

T2I-FactualBench: Benchmarking the Factuality of Text-to-Image Models with Knowledge-Intensive Concepts (2025.acl-long)

Challenge:	Large language models are ideal for decision-making, but they can be difficult to process when they are verbose and include repetition, hedging, and vagueness.
Approach:	They propose a framework that constructs probabilistic factor profiles from complex scenarios and integrates them with analogical reasoning to guide LLMs in making decisions in new situations.
Outcome:	The proposed framework separates the tasks of quantifying uncertainty and incorporating it into LLM decision-making.

Copied to clipboard

Ziwei Huang, Wanggui He, Quanyu Long, Yandi Wang, Haoyuan Li, Zhelun Yu, Fangxun Shu, Weilong Dai, Hao Jiang, Fei Wu, Leilei Gan

NOVA-63: Native Omni-lingual Versatile Assessments of 63 Disciplines (2025.emnlp-main)

Challenge:	Existing studies on text-to-image (T2I) models focus on text alignment, image quality, and object composition capabilities.
Approach:	They propose a T2I-FactualBench benchmark to evaluate the factuality of knowledge-intensive concept generation.
Outcome:	The proposed framework evaluates the factuality of knowledge-intensive concept generation tasks.

Copied to clipboard

Jinyang Zhang, Kexin Yang, Yu Wan, Muyang Ye, Baosong Yang, Fei Huang, Junyang Lin, Dayiheng Liu

CLEAR: A Framework Enabling Large Language Models to Discern Confusing Legal Paragraphs (2025.findings-emnlp)

Challenge:	Existing multilingual benchmarks show severe drawbacks, such as overly translated content, the absence of difficulty control, and disciplinary imbalance, making the benchmarking process unreliable and showing low convincingness.
Approach:	They propose a multilingual benchmark that integrates LLM-assisted formatting, expert quality verification, and multi-level difficulty screening to provide a comprehensive, difficult multilingual assessment.
Outcome:	The proposed benchmark features 93,536 questions sourced from native speakers across 14 languages and 63 academic disciplines.

Copied to clipboard

Qi Xu, Qian Liu, Hao Fei, Hang Yu, Shuhao Guan, Xiao Wei

Better Highlighting: Creating Sub-Sentence Summary Highlights (2020.emnlp-main)

Challenge:	Existing work focuses on enabling LLMs to leverage legal rules to tackle complex legal reasoning tasks, but ignores their ability to understand legal rules.
Approach:	They propose a legal paragraph prediction task that aims to predict the legal paragraph given criminal facts and a framework CLEAR to enhance their legal reasoning ability.
Outcome:	The proposed model improves the ability of LLMs to analyze legal cases with the guidance of legal rule insights.

Copied to clipboard

Sangwoo Cho, Kaiqiang Song, Chen Li, Dong Yu, Hassan Foroosh, Fei Liu

Summarization

Challenge:	Abstractive summarizations are considered to be less reliable because they distort the original meaning and can be confusing for readers.
Approach:	They propose a method to generate summary highlights that are understandable on their own to avoid confusion.
Outcome:	The proposed method allows summaries to be understood in context and avoids misdirecting readers to false conclusions.

Self-Error-Instruct: Generalizing from Errors for LLMs Mathematical Reasoning (2025.acl-long)

Copied to clipboard

Erxin Yu, Jing Li, Ming Liao, Qi Zhu, Boyang Xue, Minghui Xu, Baojun Wang, Lanqing Hong, Fei Mi, Lifeng Shang

Identifying Factual Inconsistencies in Summaries: Grounding LLM Inference via Task Taxonomy (2024.findings-emnlp)

Challenge:	Existing approaches to learning from errors synthesize training data by extrapolating from isolated bad cases, thereby failing to generalize the extensive patterns inherent within these cases.
Approach:	They propose a framework that synthesizes more generalized training data from isolated bad cases by extrapolating from isolated cases.
Outcome:	The proposed framework synthesizes more generalized training data to address these model weaknesses.

Copied to clipboard

Liyan Xu, Zhenlin Su, Mo Yu, Jin Xu, Jinho Choi, Jie Zhou, Fei Liu

Doc2Bot: Accessing Heterogeneous Documents via Conversational Bots (2022.findings-emnlp)

Challenge:	Existing studies have focused on specialized BERT-variants and recent LLMs to reason inconsistencies.
Approach:	They propose to incorporate task-specific taxonomy into inferences to facilitate both zero-shot and supervised paradigms.
Outcome:	The proposed model outperforms specialized non-LLM and recent LLM models in a number of domains.

Copied to clipboard

Haomin Fu, Yeqin Zhang, Haiyang Yu, Jian Sun, Fei Huang, Luo Si, Yongbin Li, Cam Tu Nguyen

GSID: Generative Semantic Indexing for E-Commerce Product Understanding (2025.emnlp-industry)

Challenge:	Documents contain various structures that hinder the ability of machines to comprehend . user information needs are often underspecified, and the nature of heterogeneous documents poses challenges.
Approach:	They propose a dataset for building machines that help users seek information via conversations . their dataset contains over 100,000 turns based on Chinese documents from five domains .
Outcome:	The proposed tasks are challenging and worthy of further research.

Copied to clipboard

Haiyang Yang, Qinye Xie, Qingheng Zhang, Chen Li Yu, Huike Zou, Chengbao Lian, Shuguang Han, Fei Huang, Jufeng Chen, Bo Zheng

Enhancing Character-Level Understanding in LLMs through Token Internal Structure Learning (2025.acl-long)

Challenge:	Structured product information is a major bottleneck for the efficiency of e-commerce platforms.
Approach:	They propose a data-driven approach to generate product structured representations using product metadata.
Outcome:	Extensive experiments show that GSID can generate better product representations on real-world e-commerce platforms.

Copied to clipboard

Zhu Xu, Zhiqiang Zhao, Zihan Zhang, Yuchi Liu, Quanwei Shen, Fei Liu, Yu Kuang, Jian He, Conglin Liu

Challenge:	Large language models (LLMs) use tokenization methods but often obscure internal character structures within tokens.
Approach:	They propose a method that improves models’ ability to capture character positions within tokens by training them on reverse character prediction tasks using the tokenizer’s vocabulary.
Outcome:	Experiments show that the proposed method improves position prediction accuracy in large language models, enabling more precise identification of target characters in original text.

RMTBench: Benchmarking LLMs Through Multi-Turn User-Centric Role-Playing (2025.findings-emnlp)

Copied to clipboard

Hao Xiang, Tianyi Tang, Yang Su, Bowen Yu, An Yang, Fei Huang, Yichang Zhang, Yaojie Lu, Hongyu Lin, Xianpei Han, Jingren Zhou, Junyang Lin, Le Sun

MPTc-Bench: Measuring Cross-market Generative Ability of Vision-Language Models via Movie Poster Transcreation (2026.findings-acl)

Challenge:	Existing benchmarks focus on character-centric approach and fail to reflect real-world applications.
Approach:	RMTBench is a user-centric bilingual role-playing benchmark featuring 80 diverse characters and over 8,000 dialogue rounds.
Outcome:	RMTBench features 80 diverse characters and over 8,000 dialogue rounds.

Copied to clipboard

Youyuan Lin, Yuan Li, Yahan Yu, Fei Cheng, Shin’ya Nishida, Chenhui Chu

LV-BERT: Exploiting Layer Variety for BERT (2021.findings-acl)

Challenge:	Recent work adapts textual transcreation to image editing and formulates image transcreations to better match a target audience while preserving meaning.
Approach:	They propose a two-stage planner-editor pipeline in which an VLM planner specifies executable edits and an image editor renders them.
Outcome:	The proposed model can transcreate a visual asset for a different market while preserving its identity while matching market-specific design preferences and multilingual typography.

Copied to clipboard

Weihao Yu, Zihang Jiang, Fei Chen, Qibin Hou, Jiashi Feng

Improving Question Generation with Multi-level Content Planning (2023.findings-emnlp)

Challenge:	Modern pre-trained language models are mostly built upon stereotyped development sets . LV-BERT model obtained by our method outperforms BERT on various downstream tasks .
Approach:	They propose to exploit layer variety from the layer type set and the layer order to improve pre-trained models.
Outcome:	The proposed model outperforms BERT and its variants on various downstream tasks.

Copied to clipboard

Zehua Xia, Qi Gou, Bowen Yu, Haiyang Yu, Fei Huang, Yongbin Li, Nguyen Cam-Tu

HuatuoGPT, Towards Taming Language Model to Be a Doctor (2023.findings-emnlp)

Challenge:	Existing studies suggest key phrase selection is essential for question generation, yet it is difficult to connect disjointed phrases into meaningful questions, especially for long context.
Approach:	They propose a QG framework that uses multi-level content planning to generate questions from a given context and an answer.
Outcome:	The proposed framework outperforms baselines on two popular QG datasets.

Copied to clipboard

Hongbo Zhang, Junying Chen, Feng Jiang, Fei Yu, Zhihong Chen, Guiming Chen, Jianquan Li, Xiangbo Wu, Zhang Zhiyi, Qingying Xiao, Xiang Wan, Benyou Wang, Haizhou Li

Outcome Accuracy is Not Enough: Aligning the Reasoning Process of Reward Models (2026.acl-long)

Challenge:	Experimental results show that the distilled language model outperforms its teacher model (ChatGPT) in most cases.
Approach:	They propose a Large Language Model (LLM) that leverages both distilled data from ChatGPT and real-world data fromdoctors in the supervised fine-tuning stage.
Outcome:	The proposed model outperforms the teacher model in most cases by using additional real-world data and RLMF to align the language model with the merits of both sources.

Copied to clipboard

Binghai Wang, Yantao Liu, Yuxuan Liu, Tianyi Tang, Shenzhi Wang, Chang Gao, Chujie Zheng, Yichang Zhang, Le Yu, Shixuan Liu, Tao Gui, Qi Zhang, Xuanjing Huang, Bowen Yu, Fei Huang, Junyang Lin

Entity-to-Text based Data Augmentation for various Named Entity Recognition Tasks (2023.findings-acl)

Challenge:	Recent studies observe a phenomenon where reward models achieve high accuracy on static datasets but fail to generalize effectively during RLHF.
Approach:	They propose a method that combines rationale consistency with outcome accuracy to improve performance on RM-Bench and JudgeBench.
Outcome:	The proposed method surpasses baselines on RM-Bench and JudgeBench by an average of 5% and improves creative writing tasks by 7%.

Copied to clipboard

Xuming Hu, Yong Jiang, Aiwei Liu, Zhongqiang Huang, Pengjun Xie, Fei Huang, Lijie Wen, Philip S. Yu

LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages (2024.findings-emnlp)

Challenge:	Existing augmentation techniques manipulate words in the original text that break the semantic coherence of the text, or exploit generative models that ignore preserving entities in the text.
Approach:	They propose a novel Entity-to-Text based data augmentation technique called EnTDA to add, delete, replace or swap entities in the original text.
Outcome:	The proposed technique generates semantically coherent and entity preserving texts on thirteen NER tasks and two settings.

Copied to clipboard

Yinquan Lu, Wenhao Zhu, Lei Li, Yu Qiao, Fei Yuan

Unified Language Representation for Question Answering over Text, Tables, and Images (2023.findings-acl)

Challenge:	Large Language Models (LLMs) exhibit exceptional translation capabilities in high-resource language tasks, yet their effectiveness in low-resourced languages is suboptimal.
Approach:	They conduct extensive multilingual continual pre-training on the LLaMA series models and develop LLiMAX for translation support across more than 100 languages.
Outcome:	The proposed model achieves higher translation performance than existing open-source models and performs on-par with specialized translation model on the Flores-101 benchmark.

Copied to clipboard

Bowen Yu, Cheng Fu, Haiyang Yu, Fei Huang, Yongbin Li

CultureSynth: A Hierarchical Taxonomy-Guided and Retrieval-Augmented Framework for Cultural Question-Answer Synthesis (2025.findings-emnlp)

Challenge:	Existing approaches to answer complex questions are limited to text or structured data.
Approach:	They propose a paradigm that transforms images and tables into unified language representations to simplify QA problems.
Outcome:	The proposed framework outperforms existing methods on two datasets and the WebQA leaderboard.

Copied to clipboard

Xinyu Zhang, Pei Zhang, Shuang Luo, Jialong Tang, Yu Wan, Baosong Yang, Fei Huang

Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion (2025.acl-long)

Challenge:	Cultural competence is defined as the ability to understand and adapt to multicultural contexts.
Approach:	They propose a framework that uses a hierarchical multilingual taxonomy and a Retrieval-Augmented Generation to synthesize culturally relevant question-answer pairs.
Outcome:	The proposed framework contains a hierarchical multilingual taxonomy covering 12 primary and 130 secondary topics and a Retrieval-Augmented Generation (RAG)-based methodology leveraging factual knowledge to synthesize culturally relevant question-answer pairs.

Copied to clipboard

Jianqing Zhu, Huang Huang, Zhihang Lin, Juhao Liang, Zhengyang Tang, Khalid Almubarak, Mosen Alharthi, Bang An, Juncai He, Xiangbo Wu, Fei Yu, Junying Chen, Ma Zhuoheng, Yuhao Du, He Zhang, Saied Alshahrani, Emad A. Alghamdi, Lian Zhang, Ruoyu Sun, Haizhou Li, Benyou Wang, Jinchao Xu

MultiSQL: A Schema-Integrated Context-Dependent Text2SQL Dataset with Diverse SQL Operations (2024.findings-acl)

Challenge:	In the evolving landscape of large language models, the predominant focus has been on English and Chinese.
Approach:	They propose to utilize Arabic-specific vocabulary in the tokenizer to accelerate decoding.
Outcome:	The proposed model achieves decent performance comparable to the best Arabic LLMs across various Arabic benchmarks.

Copied to clipboard

Chunhui Li, Yifan Wang, Zhen Wu, Zhen Yu, Fei Zhao, Shujian Huang, Xinyu Dai

Natural Language Interfaces

Challenge:	Text2SQL is a task that translates natural language into SQL statements.
Approach:	They propose a task that translates natural language into SQL statements.
Outcome:	The proposed task enables users to convert natural language into SQL statements.

Nudging: Inference-time Alignment of LLMs via Guided Decoding (2025.acl-long)

Copied to clipboard

Yu Fei, Yasaman Razeghi, Sameer Singh

Challenge:	Large language models (LLMs) require alignment to effectively and safely follow user instructions.
Approach:	They propose a simple, training-free algorithm that aligns any base model at inference time using a small aligned model.
Outcome:	The proposed algorithm outperforms large aligned models on open-instruction tasks without training.

Don’t Tell the Answer, Truly Guide the Reasoning During RL Rollouts (2026.findings-acl)

Copied to clipboard

Xinyi Wang, Jinyi Han, Zishang Jiang, Tingyun li, Jiaqing Liang, Sihang Jiang, Zhaoqian Dai, Ma Shuguang, Fei Yu, Yanghua Xiao

DeepSolution: Boosting Complex Engineering Solution Design via Tree-based Exploration and Bi-point Thinking (2025.acl-long)

Challenge:	Existing methods such as GRPO often break down when task difficulty exceeds the model’s capacity, resulting in sparse rewards and inefficient training.
Approach:	They propose to measure the compatibility between external guidance and a model's intrinsic policy by introducing an adaptive framework to enhance reasoning performance while explicitly preserving high Affinity.
Outcome:	The proposed framework outperforms baseline models while maintaining high Affinity.

Copied to clipboard

Zhuoqun Li, Haiyang Yu, Xuanang Chen, Hongyu Lin, Yaojie Lu, Fei Huang, Xianpei Han, Yongbin Li, Le Sun

Challenge:	Existing studies in retrieval-augmented generation (RAG) do not sufficiently address the design of complex engineering solutions.
Approach:	They propose a retrieval-augmented generation system that leverages tree-based exploration and bi-point thinking mechanism to generate reliable solutions.
Outcome:	Experiments show that the proposed system achieves state-of-the-art (SOTA) performance on the SolutionBench, highlighting its potential to enhance the automation and reliability of complex engineering solution design in real-world applications.

Speech-Text Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment (2023.acl-long)

Copied to clipboard

Tianshu Yu, Haoyu Gao, Ting-En Lin, Min Yang, Yuchuan Wu, Wentao Ma, Chao Wang, Fei Huang, Yongbin Li

Teaching Small Language Models Reasoning through Counterfactual Distillation (2024.emnlp-main)

Challenge:	Existing speech-text pre-training methods are limited to one or two specific tasks, despite their success in speech-language processing tasks.
Approach:	They propose a temporal position prediction task to capture the speech-text alignment . they use a textual dialog pre-training task to generalize a response selection task .
Outcome:	The proposed model is superior in learning speech-text alignment and multi-turn dialog context.

Copied to clipboard

Tao Feng, Yicheng Li, Li Chenglin, Hao Chen, Fei Yu, Yin Zhang

Lego-MT: Learning Detachable Models for Massively Multilingual Machine Translation (2023.findings-acl)

Challenge:	Large language models (LLMs) have demonstrated remarkable performance in a wide range of downstream tasks.
Approach:	They propose a counterfactual distillation framework that leverages LLMs to generate high-quality counterfacts and utilizes multi-view CoT to enhance the diversity of reasoning samples.
Outcome:	The proposed framework enhances reasoning capabilities of large language models and is more robust to OOD data.

Copied to clipboard

Fei Yuan, Yinquan Lu, Wenhao Zhu, Lingpeng Kong, Lei Li, Yu Qiao, Jingjing Xu

SpeechIQ: Speech-Agentic Intelligence Quotient Across Cognitive Levels in Voice Understanding by Large Language Models (2025.acl-long)

Challenge:	Existing monolithic models for multilingual neural machine translation encounter parameter interference and inefficient inference for large models.
Approach:	They propose a detachable multi-way model that assigns each language to an individual branch . they use data from OPUS to build a translation benchmark covering 433 languages .
Outcome:	The proposed model outperforms existing models in OPUS and is faster than existing models.

Copied to clipboard

Zhen Wan, Chao-Han Huck Yang, Yahan Yu, Jinchuan Tian, Sheng Li, Ke Hu, Zhehuai Chen, Shinji Watanabe, Fei Cheng, Chenhui Chu, Sadao Kurohashi

ADaPT: Token-Level Decoupling for Efficient Large Reasoning Models (2026.findings-acl)

Challenge:	SIQ quantifies voice understanding abilities and provides unified comparisons between cascaded methods and end-to-end models.
Approach:	They propose a human cognition-inspired evaluation pipeline for voice understanding large language models (LLM_Voice) that quantifies voice understanding abilities and provides unified comparisons between cascaded methods and end-to-end models.
Outcome:	The proposed framework quantifies voice understanding abilities and provides unified comparisons between cascaded methods and end-to-end models, identifies annotation errors in existing benchmarks, and detects hallucinations in LLM_Voice.

Copied to clipboard

Tingyun li, Zishang Jiang, Jinyi Han, Xinyi Wang, Sihang Jiang, Han Xia, Zhaoqian Dai, Ma Shuguang, Fei Yu, Jiaqing Liang, Yanghua Xiao

Green & Sustainable NLP

Challenge:	Existing efficiency-oriented methods attempt to shorten or mix reasoning strategies, yet often degrade reasoning capability.
Approach:	They propose a token-level dual-process framework that explicitly decouples efficiency and correctness signals during training.
Outcome:	The proposed framework reduces inference cost while maintaining strong reasoning ability across multiple benchmarks.

DEMO: Reframing Dialogue Interaction with Fine-grained Element Modeling (2025.findings-acl)

Copied to clipboard

Minzheng Wang, Xinghua Zhang, Kun Chen, Nan Xu, Haiyang Yu, Fei Huang, Wenji Mao, Yongbin Li

Toward Automated Robustness Evaluation of Mathematical Reasoning (2026.findings-acl)

Challenge:	Large language models (LLMs) enabled dialogue systems are one of the central modes in human-machine interaction.
Approach:	They propose a benchmark task for dialogue element MOdeling and Element Awareness and a new benchmark for dialogue agent interaction that allows the agent to model dialogue elements via imitation learning.
Outcome:	The proposed agent performs well in both dialogue element modeling and out-of-domain tasks.

Copied to clipboard

Yutao Hou, Zeguan Xiao, Fei Yu, Yihan Jiang, Ma Shuguang, Zhaoqian Dai, Hailiang Huang, Yun Chen, Guanhua Chen

SportsMetrics: Blending Text and Numerical Data to Understand Information Fusion in LLMs (2024.acl-long)

Challenge:	Existing robustness evaluations rely on hand-crafted templates or a limited set of perturbation rules, resulting in model failure.
Approach:	They propose a framework inspired by software stress testing that generates adversarial variants via a multi-round rewrite-verify loop, ensuring semantic consistency while successfully inducing model failure.
Outcome:	The proposed framework generates adversarial variants dynamically for each LLM, minimizing the risk of data contamination.

Copied to clipboard

Yebowen Hu, Kaiqiang Song, Sangwoo Cho, Xiaoyang Wang, Hassan Foroosh, Dong Yu, Fei Liu

Numerical Reasoning

Challenge:	Large language models can handle text and data, but blending text and numerical data presents significant challenges.
Approach:	They propose four tasks to evaluate the numerical reasoning and information fusion capabilities of large language models in sports data analytics.
Outcome:	The proposed tasks evaluate the numerical reasoning and information fusion capabilities of large language models in sports data analytics.

P-MMEval: A Parallel Multilingual Multitask Benchmark for Consistent Evaluation of LLMs (2025.emnlp-main)

Copied to clipboard

Yidan Zhang, Yu Wan, Boyi Deng, Baosong Yang, Hao-Ran Wei, Fei Huang, Bowen Yu, Dayiheng Liu, Junyang Lin, Fei Huang, Jingren Zhou

Multi-Document Summarization with Determinantal Point Processes and Contextualized Representations (D19-54)

Challenge:	Recent advances in large language models showcase varied multilingual capabilities across tasks . previous assessments focused on fundamental natural language processing (NLP) or isolated capability-specific tasks.
Approach:	They propose a multilingual multitask benchmark to assess multilingual capabilities . they use a large-scale benchmark covering fundamental and capability-specialized datasets .
Outcome:	The proposed benchmark compares models and tasks across languages and tasks and examines knowledge transfer from English to other languages.

Copied to clipboard

Sangwoo Cho, Chen Li, Dong Yu, Hassan Foroosh, Fei Liu

Summarization

Challenge:	Determinantal point processes (DPP) is one of the best performing techniques for extractive summarization.
Approach:	They propose to combine determinantal point processes with surface indicators for effective identification of summary-worthy sentences.
Outcome:	The determinantal point processes (DPP) framework is one of the best performing in summarization competitions.

Domain Incremental Lifelong Learning in an Open World (2023.findings-acl)

Copied to clipboard

Yi Dai, Hao Lang, Yinhe Zheng, Bowen Yu, Fei Huang, Yongbin Li

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

Challenge:	Existing approaches to lifelong learning (LL) models require access to task identities in the testing phase or cannot handle samples from unseen tasks.
Approach:	They propose a dynamic architecture-based lifelong learning model that tries to learn a sequence of tasks with a prompt-enhanced language model.
Outcome:	The proposed model outperforms state-of-the-art models in handling unseen tasks and focuses on task-level prompts to capture knowledge from different granularities.

Copied to clipboard

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

CoSafe: Evaluating Large Language Model Safety in Multi-Turn Dialogue Coreference (2024.emnlp-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.

Copied to clipboard

Erxin Yu, Jing Li, Ming Liao, Siqi Wang, Gao Zuchen, Fei Mi, Lanqing Hong

Robustness in NLP

Challenge:	Existing studies have not noticed the safety risks of large language models . authors evaluated 1,400 questions in multi-turn dialogue coreference .
Approach:	They are the first to evaluate LLM safety in multi-turn dialogue coreference . they created a dataset of 1,400 questions and tested five open-source models .
Outcome:	The study shows that model safety decreases in multi-turn dialogue coreference scenarios . the highest success rate was with the LLaMA2-Chat-7b model, while the lowest was with mistral-7B-Instruct model .

LongWeave: A Long-Form Generation Benchmark Bridging Real-World Relevance and Verifiability (2025.findings-emnlp)

Copied to clipboard

Zikai Xiao, Fei Huang, Jianhong Tu, Jianhui Wei, Wen Ma, Yuxuan Zhou, Jian Wu, Bowen Yu, Zuozhu Liu, Junyang Lin

Challenge:	Existing benchmarks for long-form generation assess real-world queries with hard-to-verify metrics or use synthetic setups that overlook real-life intricacies.
Approach:	They propose a new approach that balances verifiable and real-world assessment with Target-Anchored Evaluation.
Outcome:	The proposed model balances real-world and verifiable assessment with Target-Anchored Evaluation (TAE) it generates queries, textual materials, and anchors based on verifier targets within real-life scenarios .

FastSeq: Make Sequence Generation Faster (2021.acl-demo)

Copied to clipboard

Yu Yan, Fei Hu, Jiusheng Chen, Nikhil Bhendawade, Ting Ye, Yeyun Gong, Nan Duan, Desheng Cui, Bingyu Chi, Ruofei Zhang

Step-by-Step Mastery: Enhancing Soft Constraint Following Ability of Large Language Models (2025.findings-acl)

Challenge:	Transformer-based models have made tremendous impact in natural language generation, but inference speed is still a bottleneck due to large model size and intensive computing involved in auto-regressive decoding process.
Approach:	They propose an attention cache optimization, an efficient algorithm for detecting repeated n-grams, and an asynchronous generation pipeline with parallel I/O to accelerate sequence generation without loss of accuracy.
Outcome:	The proposed framework can accelerate the sequence generation by 4x to 9x with a simple one-line code change for a set of widely used and diverse models.

Copied to clipboard

Qingyu Ren, Jie Zeng, Qianyu He, Jiaqing Liang, Yanghua Xiao, Weikang Zhou, Zeye Sun, Fei Yu

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

Challenge:	In real-world scenarios, user instructions often contain soft constraints, which are semantically related and cannot be rule-based verified, posing challenges for large language models.
Approach:	They propose a pipeline to construct datasets with high-quality outputs for instructions containing soft constraints automatically and use Direct Preference Optimization (DPO) as the training method.
Outcome:	The proposed model improves the LLMs' soft constraint following ability by using direct preference optimization (DPO) and constraint quantity.

Copied to clipboard

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

Generating Classical Chinese Poems from Vernacular Chinese (D19-1)

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 .

Copied to clipboard

Zhichao Yang, Pengshan Cai, Yansong Feng, Fei Li, Weijiang Feng, Elena Suet-Ying Chiu, Hong Yu

InFoBench: Evaluating Instruction Following Ability in Large Language Models (2024.findings-acl)

Challenge:	Existing models for classical Chinese poetry generation only allow users to use keywords to interfere with the meaning of generated poems.
Approach:	They propose a model to generate classical Chinese poems from vernacular . their model uses unsupervised machine translation to generate Chinese poems . human evaluation shows it can generate high-quality poems comparable to amateur poems - authors .
Outcome:	The proposed model improves the perplexity and BLEU of the proposed model compared with typical models and human evaluation shows it generates high-quality poems comparable to amateur poems.

Copied to clipboard

Yiwei Qin, Kaiqiang Song, Yebowen Hu, Wenlin Yao, Sangwoo Cho, Xiaoyang Wang, Xuansheng Wu, Fei Liu, Pengfei Liu, Dong Yu

Causal Document-Grounded Dialogue Pre-training (2023.emnlp-main)

Challenge:	Existing methods for evaluating Large Language Models (LLMs) ability to follow instructions have not been able to provide a detailed analysis of their compliance with instructions.
Approach:	They propose a new metric for evaluating Large Language Models' ability to follow instructions and a benchmark for DRFR.
Outcome:	The proposed metric and benchmark compared with traditional scoring methods and explores annotation sources including human experts, crowd-sourced workers, and GPT-4.

Copied to clipboard

Yingxiu Zhao, Bowen Yu, Bowen Li, Haiyang Yu, Jinyang Li, Chao Wang, Fei Huang, Yongbin Li, Nevin Zhang

Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models (2024.acl-long)

Challenge:	Existing methods for document-grounded dialogue (DocGD) rely on general pre-trained language models without a tailored pre-training approach that explicitly captures causal relationships.
Approach:	They propose a causally-complete dataset construction strategy for developing million-scale DocGD pre-training corpora and a perturbation-based strategy to capture causality.
Outcome:	The proposed strategy yields significant and consistent improvements in fully-supervised, low-resource, few-shot, and zero-shot settings.

Copied to clipboard

Fangzhi Xu, Zhiyong Wu, Qiushi Sun, Siyu Ren, Fei Yuan, Shuai Yuan, Qika Lin, Yu Qiao, Jun Liu

Coarse-to-Fine: Hierarchical Multi-task Learning for Natural Language Understanding (2022.coling-1)

Challenge:	Large Language Models (LLMs) have limitations when it comes to comprehending and expressing world knowledge that extends beyond the boundaries of natural language.
Approach:	They propose a model that integrates symbolic data into LLM training without loss of generality ability.
Outcome:	The proposed model performs better on symbol- and NL-centric tasks.

Copied to clipboard

Zhaoye Fei, Yu Tian, Yongkang Wu, Xinyu Zhang, Yutao Zhu, Zheng Liu, Jiawen Wu, Dejiang Kong, Ruofei Lai, Zhao Cao, Zhicheng Dou, Xipeng Qiu

Optimizing Instruction Synthesis: Effective Exploration of Evolutionary Space with Tree Search (2024.findings-emnlp)

Challenge:	Existing methods to learn downstream tasks by stitches skill block lack rationality and interpretation.
Approach:	They propose a hierarchical framework with a coarse-to-fine paradigm for generalized text representations from the large-scale corpus.
Outcome:	The proposed model learns basic language properties from all tasks and boosts performance on relevant tasks.

Copied to clipboard

Li Chenglin, Qianglong Chen, Zhi Li, FengTao FengTao, Yicheng Li, Hao Chen, Fei Yu, Yin Zhang

Multi-Value-Product Retrieval-Augmented Generation for Industrial Product Attribute Value Identification (2025.emnlp-industry)

Challenge:	Extensive research has highlighted the quality of instruction data is essential for the success of this alignment.
Approach:	They propose a framework for iteratively improving existing instruction data by using Monte Carlo tree search to find suitable prompts that align the language model to effectively learn multiple skills.
Outcome:	The proposed framework improves the evaluation scores of seed instruction data, raising the average evaluation scores from 2.19 to 3.81.

Copied to clipboard

Huike Zou, Haiyang Yang, Yindu Su, Chen Li Yu, Qinye Xie, Chengbao Lian, Qingheng Zhang, Shuguang Han, Fei Huang, Jufeng Chen

Divide and Conquer: Legal Concept-guided Criminal Court View Generation (2024.findings-emnlp)

Challenge:	Existing methods for product attribute value identification suffer from cascading errors and lack of generalization capability.
Approach:	They propose a multi-level retrieval scheme that uses products and attribute values as distinct hierarchical levels in PAVI domain.
Outcome:	The proposed method performs better than the state-of-the-art methods on a real-world industrial dataset.

Copied to clipboard

Qi Xu, Xiao Wei, Hang Yu, Qian Liu, Hao Fei

Scaling Data Diversity for Fine-Tuning Language Models in Human Alignment (2024.lrec-main)

Challenge:	Existing methods for creating rationales for criminal cases do not pay enough attention to the important legal concepts.
Approach:	They propose a legal concept-guided court view generation framework that generates rationales based on predicted legal concepts . they first divide the court view into sub-views, then employ a solver and verifier to generate and select rationale.
Outcome:	The proposed model generates coherent and coherent court views on a real-world criminal case dataset.

Copied to clipboard

Feifan Song, Bowen Yu, Hao Lang, Haiyang Yu, Fei Huang, Houfeng Wang, Yongbin Li

EIFBENCH: Extremely Complex Instruction Following Benchmark for Large Language Models (2025.emnlp-main)

Challenge:	Large language models (LLMs) can reveal toxic or offensive content inadvertently or intentionally.
Approach:	They propose to control the diversity of both sides according to the number of samples for fine-tuning, which can directly reflect their impact.
Outcome:	The proposed approach improves the performance of large language models after fine-tuning.

Copied to clipboard

Tao Zou, Xinghua Zhang, Haiyang Yu, Minzheng Wang, Fei Huang, Yongbin Li

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

Challenge:	Existing benchmarks focusing on single-task environments with limited constraints lack the complexity required to fully reflect the evolution of large language models (LLMs).
Approach:	They propose to use a Segment Policy Optimization algorithm to enhance the LLM's ability to accurately fulfill multi-task workflows.
Outcome:	The proposed benchmarks show that existing benchmarks lack the complexity required to fully reflect the evolution of large language models.

Copied to clipboard

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

Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA (2024.emnlp-main)

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.

Copied to clipboard

Minzheng Wang, Longze Chen, Fu Cheng, Shengyi Liao, Xinghua Zhang, Bingli Wu, Haiyang Yu, Nan Xu, Lei Zhang, Run Luo, Yunshui Li, Min Yang, Fei Huang, Yongbin Li

Universal Information Extraction with Meta-Pretrained Self-Retrieval (2023.findings-acl)

Challenge:	Existing benchmarks for evaluating long-context language models employ irrelevant noise texts to artificially extend the length of test cases, diverging from the real-world scenarios of long-constituency applications.
Approach:	They propose a long-context benchmark, Loong, aligning with realistic scenarios through extended multi-document question answering (QA) .
Outcome:	The proposed model can scale up the context window of large language models to perform in-depth analysis of multiple long documents.

Copied to clipboard

Xin Cong, Bowen Yu, Mengcheng Fang, Tingwen Liu, Haiyang Yu, Zhongkai Hu, Fei Huang, Yongbin Li, Bin Wang

Diversify Question Generation with Retrieval-Augmented Style Transfer (2023.emnlp-main)

Challenge:	Existing methods for IE are task-specific, resulting in specialized and isolated approaches for different tasks.
Approach:	They propose a method to retrieve task-specific knowledge from pretrained language models to enhance universal IE by using a Meta-Pretraining Algorithm.
Outcome:	The proposed method achieves the new state-of-the-art on 4 IE tasks, 12 datasets under fully-supervised, low-resource and few-shot scenarios.

Copied to clipboard

Qi Gou, Zehua Xia, Bowen Yu, Haiyang Yu, Fei Huang, Yongbin Li, Nguyen Cam-Tu

Towards Abstractive Grounded Summarization of Podcast Transcripts (2022.acl-long)

Challenge:	Existing question generation systems focus on the internal knowledge within the textual passage or the semantic word space for diverse content planning. Existing solutions focus on relying on the knowledge of the text and the semantic words, but have not considered the potential of external knowledge for expression diversity.
Approach:	They propose a framework for Retrieval-Augmented Style Transfer that utilizes the style of diverse templates for question generation.
Outcome:	The proposed framework outperforms baselines on diversity while being comparable in terms of consistency scores.

Copied to clipboard

Kaiqiang Song, Chen Li, Xiaoyang Wang, Dong Yu, Fei Liu

BENTO: A Visual Platform for Building Clinical NLP Pipelines Based on CodaLab (2020.acl-demos)

Challenge:	Podcast summarization is of practical benefit to content providers and consumers . however, podcast summarizing faces significant challenges including factual inconsistencies . speech recognizers induce transcription errors and abstractive summarisation models may hallucinate .
Approach:	They propose a method to generate podcast summaries while grounding segments in specific regions of the transcript to allow full inspection of summary details.
Outcome:	The proposed method can produce an abstractive summary while grounding segments in specific regions of the transcript to allow full inspection of summary details.

Copied to clipboard

Yonghao Jin, Fei Li, Hong Yu

Visual Data in NLP

Challenge:	CodaLab has limited support for creating reusable tools that can be easily applied to different datasets and composed into pipelines.
Approach:	They propose a workflow management platform with a graphic user interface built on top of CodaLab to facilitate the process of building clinical NLP pipelines.
Outcome:	The proposed workflow management platform, BENTO, is designed for clinical NLP tasks and can be easily used by researchers and developers.

Instructions are all you need: Self-supervised Reinforcement Learning for Instruction Following (2026.acl-long)

Copied to clipboard

Qingyu Ren, Qianyu He, Powei Chang, Jie Zeng, Zeye Sun, Fei Yu, Jiaqing Liang, Yanghua Xiao

Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation (2025.findings-naacl)

Challenge:	Existing reinforcement learning approaches suffer from dependency on external supervision and sparse reward signals from multi-constraint tasks.
Approach:	They propose a self-supervised reinforcement learning framework that eliminates dependency on external supervision by deriving reward signals directly from instructions and generating pseudo-labels for reward model training.
Outcome:	The proposed framework achieves strong improvements across 3 in-domain and 5 out-of-domain datasets while maintaining computational efficiency.

Copied to clipboard

Sirui Xia, Xintao Wang, Jiaqing Liang, Yifei Zhang, Weikang Zhou, Jiaji Deng, Fei Yu, Yanghua Xiao

Cross-lingual Cross-modal Pretraining for Multimodal Retrieval (2021.naacl-main)

Challenge:	Existing methods to enhance credibility and verifiability of large language models (LLMs) mainly focus on passage-level or paragraph-level references or citations, which fall short in verifikatability.
Approach:	They propose a method that provides sentence-level citations in LLM-generated responses.
Outcome:	The proposed method achieves 90% accuracy in long-form question-answering tasks.

Copied to clipboard

Hongliang Fei, Tan Yu, Ping Li

Bacteria Biotope Relation Extraction via Lexical Chains and Dependency Graphs (D19-57)

Challenge:	Recent pretrained vision-language models have achieved impressive performance on cross-modal retrieval tasks in English.
Approach:	They propose a new approach to learn cross-lingual cross-modal representations for matching images and captions in multiple languages using an annotated corpus.
Outcome:	The proposed model achieves impressive performance on two multimodal multilingual image caption benchmarks: Multi30k with German captions and MSCOCO with Japanese captions.

Copied to clipboard

Wuti Xiong, Fei Li, Ming Cheng, Hong Yu, Donghong Ji

Generation of Patient After-Visit Summaries to Support Physicians (2022.coling-1)

Challenge:	In this paper, we describe our approach for the Bacteria Biotopes relation extraction subtask in the BioNLP Shared Task 2019 .
Approach:	They propose a novel approach for dependency graph construction based on lexical chains . they then propose 'neuro network' model which uses short-term memories and syntax information .
Outcome:	The proposed approach achieves the best F1 (66.3%) in the official evaluation participated by 7 teams.

Copied to clipboard

Pengshan Cai, Fei Liu, Adarsha Bajracharya, Joe Sills, Alok Kapoor, Weisong Liu, Dan Berlowitz, David Levy, Richeek Pradhan, Hong Yu

Challenge:	After-visit summary is a summary note given to patients after their clinical visit.
Approach:	They propose to automate the generation of after-visit summaries and introduce a feedback mechanism that alerts physicians when an automatic summary fails to capture important details of the clinical notes.
Outcome:	The proposed system improves on a large clinical dataset that contains electronic health record (EHR) notes and their associated summaries.

PAFT: Prompt-Agnostic Fine-Tuning (2025.emnlp-main)

Copied to clipboard

Chenxing Wei, Yao Shu, Mingwen Ou, Ying He, Fei Yu

Salience Allocation as Guidance for Abstractive Summarization (2022.emnlp-main)

Challenge:	Prompt-agnostic fine-tuning (PAFT) improves performance by reducing overfitting to specific prompts.
Approach:	They propose a method that enhances robustness through dynamic prompt variation during training.
Outcome:	The proposed method achieves higher generalization accuracy on unseen prompts than standard methods with similar training efficiency.

Copied to clipboard

Fei Wang, Kaiqiang Song, Hongming Zhang, Lifeng Jin, Sangwoo Cho, Wenlin Yao, Xiaoyang Wang, Muhao Chen, Dong Yu

MPBoCo: Multimodal Prompt-based Boundary-enhanced Continual Framework for Joint Entity and Relation Extraction (2026.acl-long)

Challenge:	Abstractive summarization models implicitly learn to capture the salient information from scratch.
Approach:	They propose a method that uses salience expectation to guide abstractive summarization by averaging salient content to a fixed threshold.
Outcome:	The proposed method can be easily adapted to documents with various abstractiveness and achieves high performance.

Copied to clipboard

Guanglu Sun, Xinyu Liu, Lili Liang, Yang Yu, Fei Lang, Suxia Zhu, Ming Liu

Low-Resource NLP

Challenge:	Existing methods struggle to balance real-time adaptability and computational efficiency in continual learning scenarios.
Approach:	They propose a Continual Multimodal Entity and Relation Joint Extraction task and a Multimodal Prompt-based Boundary-enhanced Continuum framework that stores task-specific knowledge via learnable multimodal prompts.
Outcome:	The proposed framework outperforms baseline methods in real-world scenarios by 5.5% and 7.2%.

AceGPT, Localizing Large Language Models in Arabic (2024.naacl-long)

Copied to clipboard

Huang Huang, Fei Yu, Jianqing Zhu, Xuening Sun, Hao Cheng, Song Dingjie, Zhihong Chen, Mosen Alharthi, Bang An, Juncai He, Ziche Liu, Junying Chen, Jianquan Li, Benyou Wang, Lian Zhang, Ruoyu Sun, Xiang Wan, Haizhou Li, Jinchao Xu

EvoRoute: Experience-Driven Self-Routing LLM Agent Systems (2026.acl-long)

Challenge:	Significant concerns emerge when addressing cultural sensitivity and local values.
Approach:	They propose a localized Large Language Model (LLM) specifically for Arabic, a language imbued with unique cultural characteristics inadequately addressed by current mainstream models.
Outcome:	The proposed model sets the state-of-the-art standard for open Arabic LLMs across various benchmarks.

Copied to clipboard

Guibin Zhang, Haiyang Yu, Kaiming Yang, Bingli Wu, Fei Huang, Yongbin Li, Shuicheng Yan

Evaluating Test-Time Scaling LLMs for Legal Reasoning: OpenAI o1, DeepSeek-R1, and Beyond (2025.findings-emnlp)

Challenge:	EvoRoute is a self-evolving model routing paradigm that transcends static, pre-defined model assignments.
Approach:	They propose a model routing paradigm that transcends static, pre-defined model assignments.
Outcome:	Experiments on GAIA and BrowseComp+ show that EvoRoute reduces execution cost and latency by over 70%.

Copied to clipboard

Yinghao Hu, Yaoyao Yu, Leilei Gan, Bin Wei, Kun Kuang, Fei Wu

Pruning Foundation Models for High Accuracy without Retraining (2024.findings-emnlp)

Challenge:	Experimental results show that Legal-R1 delivers competitive performance across diverse tasks.
Approach:	They propose to evaluate 12 large language models across 17 legal tasks across statutory and case-law traditions to determine their general reasoning performance.
Outcome:	The proposed model performs well across 17 legal tasks across statutory and case-law traditions.

Copied to clipboard

Pu Zhao, Fei Sun, Xuan Shen, Pinrui Yu, Zhenglun Kong, Yanzhi Wang, Xue Lin

Toward Unifying Text Segmentation and Long Document Summarization (2022.emnlp-main)

Challenge:	Despite the superior performance of foundation models, it is challenging to deploy large language models in practical applications due to their massive parameters and computations.
Approach:	They propose a pruning algorithm to prune LLMs in one-shot without retraining . they propose retrainable pruning algorithms to prune multiple weights in LLM .
Outcome:	The proposed pruning methods perform better than baseline pruning methods on sparse and unstructured sparsity models.

Copied to clipboard

Sangwoo Cho, Kaiqiang Song, Xiaoyang Wang, Fei Liu, Dong Yu

UAlign: Leveraging Uncertainty Estimations for Factuality Alignment on Large Language Models (2025.acl-long)

Challenge:	Abstractive strategies produce more condensed summaries, but they suffer from hallucinations and factual errors, which pose a more difficult generation challenge.
Approach:	They propose a method that learns robust sentence representations by performing summarization and segmentation simultaneously, which is further enhanced by an optimization-based regularizer to promote selection of diverse summary sentences.
Outcome:	The proposed model achieves state-of-the-art performance on publicly available benchmarks and better cross-genre transferability when equipped with text segmentation.

Copied to clipboard

Boyang Xue, Fei Mi, Qi Zhu, Hongru Wang, Rui Wang, Sheng Wang, Erxin Yu, Xuming Hu, Kam-Fai Wong

OpenUE: An Open Toolkit of Universal Extraction from Text (2020.emnlp-demos)

Challenge:	Large Language Models (LLMs) often struggle to accurately express factual knowledge, especially in cases where the knowledge boundaries are ambiguous.
Approach:	They propose a framework that leverages Uncertainty estimations to represent knowledge boundaries and incorporates these representations into prompts for LLMs to Align with factual knowledge.
Outcome:	The proposed framework significantly improves the LLMs’ capacities to confidently answer known questions and refuse unknown questions on both in-domain and out-of-domain tasks.

Copied to clipboard

Ningyu Zhang, Shumin Deng, Zhen Bi, Haiyang Yu, Jiacheng Yang, Mosha Chen, Fei Huang, Wei Zhang, Huajun Chen

API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs (2023.emnlp-main)

Challenge:	a large number of natural language processing tasks focus on token-level or sentence-level understandings.
Approach:	They propose an open-source and extensible toolkit for various extraction tasks . they deploy an online demo with restful APIs to support real-time extraction .
Outcome:	The proposed model can be used to extract information from text without training and deployment.

Copied to clipboard

Minghao Li, Yingxiu Zhao, Bowen Yu, Feifan Song, Hangyu Li, Haiyang Yu, Zhoujun Li, Fei Huang, Yongbin Li

Natural Language Interfaces

Challenge:	Recent research has demonstrated that Large Language Models (LLMs) can enhance their capabilities by utilizing external tools.
Approach:	They propose a runnable evaluation system consisting of 73 API tools and an annotation system for 314 tool-use dialogues with 753 API calls.
Outcome:	The proposed benchmark assesses the effectiveness of existing LLMs by analyzing 314 tool-use dialogues with 753 API calls.

Order Matters: Investigate the Position Bias in Multi-constraint Instruction Following (2025.findings-acl)

Copied to clipboard

Jie Zeng, Qianyu He, Qingyu Ren, Jiaqing Liang, Weikang Zhou, Zeye Sun, Fei Yu, Yanghua Xiao

Challenge:	Existing large language models struggle to follow multi-constraint instructions in real-world applications.
Approach:	They propose to quantify the difficulty distribution of constraints by a novel Difficulty Distribution Index (CDDI) they find that LLMs are more performant when presented with constraints in a “hard-to-easy” order.
Outcome:	The proposed model is more performant when presented with constraints in a “hard-to-easy” order, compared with existing models with different architectures and sizes of parameters.

InImageTrans: Multimodal LLM-based Text Image Machine Translation (2025.findings-acl)

Copied to clipboard

Fei Zuo, Kehai Chen, Yu Zhang, Zhengshan Xue, Min Zhang

Mixture of insighTful Experts (MoTE): The Synergy of Reasoning Chains and Expert Mixtures in Self-Alignment (2025.acl-long)

Challenge:	Existing multimodal large language models suffer from repetition and omission hallucinations when transferred to text image machine translation task.
Approach:	They propose an efficient MLLM named InImageTrans for TiMT and a method for advancing it.
Outcome:	The proposed method outperforms existing open-source MLLMs on the MCiT benchmark.

Copied to clipboard

Zhili Liu, Yunhao Gou, Kai Chen, Lanqing Hong, Jiahui Gao, Fei Mi, Yu Zhang, Zhenguo Li, Xin Jiang, Qun Liu, James Kwok

Mitigating Label Biases for In-context Learning (2023.acl-long)

Challenge:	Recent studies show that reasoning abilities contribute significantly to model safety, while integrating Mixture-of-Experts (MoE) architectures can further enhance alignment.
Approach:	They propose a framework that synergistically combines reasoning chains and expert mixtures to improve self-alignment.
Outcome:	The proposed framework improves model safety, jailbreak resistance, and over-refusal capabilities, achieving performance comparable to OpenAI’s state-of-the-art o1 model.

Copied to clipboard

Yu Fei, Yifan Hou, Zeming Chen, Antoine Bosselut

Flexora: Flexible Low-Rank Adaptation for Large Language Models (2025.acl-long)

Challenge:	Existing methods to categorize label biases in in-context learning (ICL) have not addressed all three types of label bias.
Approach:	They propose a method that estimates a language model’s label bias using random in-domain words from the task corpus to categorize and detect label biases in ICL.
Outcome:	The proposed method significantly improves the performance of GPT-J and GPT-3 on a wide range of tasks.

Copied to clipboard

Chenxing Wei, Yao Shu, Ying Tiffany He, Fei Yu

Harnessing the Power of Large Language Model for Uncertainty Aware Graph Processing (2024.lrec-main)

Challenge:	Large language models (LLMs) have revolutionized artificial intelligence, but performance on specific tasks is limited by knowledge boundaries.
Approach:	They propose a method that automatically selects the most critical layers for fine-tuning to optimize performance across diverse downstream tasks.
Outcome:	The proposed method outperforms baseline models and natural language tasks.

Copied to clipboard

Zhenyu Qian, Yiming Qian, Yuting Song, Fei Gao, Hai Jin, Chen Yu, Xia Xie

Generating Visual Spatial Description via Holistic 3D Scene Understanding (2023.acl-long)

Challenge:	Existing methods for graph processing rely on assumptions about data relations that are inadequate when handling large and complex graph data.
Approach:	They propose a large language model enhanced by an uncertainty-aware module to provide a confidence score on the generated graph data.
Outcome:	The proposed approach surpasses state-of-the-art algorithms by a substantial margin on ten datasets.

Copied to clipboard

Yu Zhao, Hao Fei, Wei Ji, Jianguo Wei, Meishan Zhang, Min Zhang, Tat-Seng Chua

Learning as Conversation: Dialogue Systems Reinforced for Information Acquisition (2022.naacl-main)

Challenge:	Existing VSD work focuses on skewed spatial understanding of target objects . Existing work merely models the 2D geometrical vision features .
Approach:	They propose to incorporate 3D scene features into visual spatial description tasks by sampling topologically-diverse subgraphs from Go3D-S2G.
Outcome:	The proposed framework outperforms baselines on two VSD datasets and produces more spatially-diversified generation.

Copied to clipboard

Pengshan Cai, Hui Wan, Fei Liu, Mo Yu, Hong Yu, Sachindra Joshi

Natural Language Interfaces

Challenge:	a novel AI-empowered chat bot for learning as conversation can be applied to various domains without in-domain dialogue data.
Approach:	They propose a novel task where a user does not read a passage but gains information and knowledge through conversation with a teacher bot.
Outcome:	The proposed system can be transferred to various domains without in-domain dialogue data and can carry out conversations both informative and attentive to users.

Tree-Instruct: A Preliminary Study of the Intrinsic Relationship between Complexity and Alignment (2024.lrec-main)

Copied to clipboard

Yingxiu Zhao, Bowen Yu, Binyuan Hui, Haiyang Yu, Minghao Li, Fei Huang, Nevin L. Zhang, Yongbin Li

Challenge:	Extensive research has highlighted the importance of data complexity as a crucial metric, but the impact of complexity remains relatively unexplored.
Approach:	They propose to add a specified number of nodes to instructions’ semantic trees to enhance the instruction complexity in a controllable manner.
Outcome:	The proposed approach outperforms diverse yet complex instructions under the same token budget and can control the difficulty level of modified instructions.

VisualRWKV: Exploring Recurrent Neural Networks for Visual Language Models (2025.coling-main)

Copied to clipboard

Haowen Hou, Peigen Zeng, Fei Ma, Fei Richard Yu

Towards a Mechanistic Interpretation of Multi-Step Reasoning Capabilities of Language Models (2023.emnlp-main)

Challenge:	Visual Language Models (VLMs) have been gaining popularity with large language models, but few attempts have been made to incorporate efficient linear Recurrent Neural Networks (RNNs) into VLMs.
Approach:	They propose a linear RNN model with a data-dependent recurrence and sandwich prompts to enhance modeling capabilities and a 2D image scanning mechanism to enrich the processing of visual sequences.
Outcome:	The proposed model achieves competitive performance compared to Transformer-based models on various benchmarks.

Copied to clipboard

Yifan Hou, Jiaoda Li, Yu Fei, Alessandro Stolfo, Wangchunshu Zhou, Guangtao Zeng, Antoine Bosselut, Mrinmaya Sachan

Beyond prompting: Making Pre-trained Language Models Better Zero-shot Learners by Clustering Representations (2022.emnlp-main)

Challenge:	Recent work has shown that language models (LMs) have strong multi-step (i.e., procedural) reasoning capabilities.
Approach:	They propose a mechanistic interpretation of language models for multi-step reasoning tasks by introducing a new probing approach that recovers the reasoning tree from the model’s attention patterns.
Outcome:	The proposed model implicitly embeds a reasoning tree resembling the correct reasoning process within it, and detects the information from the model’s attention patterns for most examples.

Copied to clipboard

Yu Fei, Zhao Meng, Ping Nie, Roger Wattenhofer, Mrinmaya Sachan