Papers by Chao Yu

Make-A-Voice: Revisiting Voice Large Language Models as Scalable Multilingual and Multitask Learners (2024.acl-long)

Challenge:	Recent proposed methods fail to consider the linguistic structure of texts and lack the ability to handle the low-resource problem.
Approach:	They propose a coherence-based contrastive learning model named CoCo to detect MGTs under low-resource scenario.
Outcome:	The proposed model outperforms state-of-the-art methods on two datasets and two self-constructed datasets.

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Rongjie Huang, Chunlei Zhang, Yongqi Wang, Dongchao Yang, Jinchuan Tian, Zhenhui Ye, Luping Liu, Zehan Wang, Ziyue Jiang, Xuankai Chang, Jiatong Shi, Chao Weng, Zhou Zhao, Dong Yu

BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers (2024.emnlp-main)

Challenge:	Large language models (LLMs) have been used for general-purpose interfaces across multiple tasks and languages.
Approach:	They propose to use large language models as a general-purpose interface across multiple tasks and languages.
Outcome:	The proposed model performs better on 200K hours of 6-language data for voice generation applications.

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Ran Xu, Wenqi Shi, Yue Yu, Yuchen Zhuang, Yanqiao Zhu, May Dongmei Wang, Joyce Ho, Chao Zhang, Carl Yang

AcTune: Uncertainty-Based Active Self-Training for Active Fine-Tuning of Pretrained Language Models (2022.naacl-main)

Challenge:	Developing effective biomedical retrieval models is important for excelling at knowledge-intensive biomedically tasks but still challenging due to the lack of sufficient publicly annotated biomedic data and computational resources.
Approach:	They propose a series of dense retrievers for enhancing biomedical retrieval via unsupervised pre-training on large biomedically corpora, followed by instruction fine-tuning on a combination of labeled datasets and synthetic pairs.
Outcome:	Experiments on 5 biomedical tasks across 11 datasets confirm the performance of the retrieval model on various biomedically demanding tasks.

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Yue Yu, Lingkai Kong, Jieyu Zhang, Rongzhi Zhang, Chao Zhang

COCO-DR: Combating Distribution Shift in Zero-Shot Dense Retrieval with Contrastive and Distributionally Robust Learning (2022.emnlp-main)

Challenge:	Existing methods for fine-tuning pre-trained language models ignore the potential of unlabeled data.
Approach:	They propose a framework that allows users to unleash the power of unlabeled data via self-training.
Outcome:	The proposed framework outperforms active learning and self-training baselines and improves the label efficiency of PLM fine-tuning by 56.2% on average.

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Yue Yu, Chenyan Xiong, Si Sun, Chao Zhang, Arnold Overwijk

LLM×MapReduce-V3: Enabling Interactive In-Depth Survey Generation through a MCP-Driven Hierarchically Modular Agent System (2025.emnlp-demos)

Challenge:	Using COCO-DR, we combat distribution shifts between source training tasks and target scenarios.
Approach:	They propose a method to combat distribution shifts between source training tasks and target scenarios by COtinuous COtrastive learning.
Outcome:	The proposed method outperforms existing models on BEIR and the giant GPT-3 embedding model with 500x more parameters.

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Yu Chao, Siyu Lin, Xiaorong Wang, Zhu Zhang, Zihan Zhou, Haoyu Wang, Shuo Wang, Jie Zhou, Zhiyuan Liu, Maosong Sun

Challenge:	Generating high-quality long-form survey articles poses significant challenges to AI Agent systems.
Approach:	They propose a hierarchically modular agent system for long-form survey generation . they use atomic models to implement skeleton initialization, digest construction, and skelet refinement . human evaluations demonstrate system surpasses representative baselines .
Outcome:	The proposed system surpasses representative baselines in both content depth and length, highlighting the strength of MCP-based modular planning.

Attention Mechanism with Energy-Friendly Operations (2022.findings-acl)

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Yu Wan, Baosong Yang, Dayiheng Liu, Rong Xiao, Derek Wong, Haibo Zhang, Boxing Chen, Lidia Chao

Challenge:	Empirical results show that attention mechanism can be improved from the energy consumption aspects.
Approach:	They propose to replace multiplications with either selective operations or additions to reduce energy consumption.
Outcome:	The proposed model achieves competitable accuracy while saving 99% and 66% energy during alignment calculation and the whole attention procedure.

ProgGen: Generating Named Entity Recognition Datasets Step-by-step with Self-Reflexive Large Language Models (2024.findings-acl)

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Yuzhao Heng, Chunyuan Deng, Yitong Li, Yue Yu, Yinghao Li, Rongzhi Zhang, Chao Zhang

Text Classification Using Label Names Only: A Language Model Self-Training Approach (2020.emnlp-main)

Challenge:	Large Language Models (LLMs) exhibit remarkable adaptability across domains, but they are often not suitable for structured knowledge extraction tasks such as named entity recognition (NER).
Approach:	They propose a method that instructs LLMs to self-reflect on the specific domain and generates domain-relevant attributes for creating attribute-rich training data.
Outcome:	The proposed method produces NER datasets in domains with domain-relevant attributes and generates entity terms and NER context data around these entities.

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Yu Meng, Yunyi Zhang, Jiaxin Huang, Chenyan Xiong, Heng Ji, Chao Zhang, Jiawei Han

Information Retrieval

Challenge:	Current text classification methods require a large number of labeled documents as training data.
Approach:	They propose a model that uses only the label name of each class to train classification models on unlabeled data without using any labeled examples.
Outcome:	The proposed model achieves 90% accuracy on four benchmark datasets using label names as the only supervision .

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

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

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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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

HiGen: Hierarchy-Aware Sequence Generation for Hierarchical Text Classification (2024.eacl-long)

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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Vidit Jain, Mukund Rungta, Yuchen Zhuang, Yue Yu, Zeyu Wang, Mu Gao, Jeffrey Skolnick, Chao Zhang

NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization (2022.findings-emnlp)

Challenge:	Hierarchical text classification is a complex subtask under multi-label text classification . the relevance of document sections can vary based on the hierarchy level, necessitating a dynamic document representation.
Approach:	They propose a text-generation-based framework that uses language models to encode dynamic text representations.
Outcome:	The proposed framework surpasses existing methods while handling data and mitigating class imbalance.

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Chao Zhao, Faeze Brahman, Kaiqiang Song, Wenlin Yao, Dian Yu, Snigdha Chaturvedi

Text Generation

Challenge:	Existing studies focus on summarizing news documents or structured documents.
Approach:	They propose to use a large-scale narrative summarization dataset to encourage research . they find there is a performance gap between humans and the models on NarraSum .
Outcome:	The proposed dataset shows that humans and state-of-the-art models perform poorly when summarizing a narrative . it contains 122K narratives collected from synopses of movies and TV episodes with diverse genres .

Cold-Start Data Selection for Better Few-shot Language Model Fine-tuning: A Prompt-based Uncertainty Propagation Approach (2023.acl-long)

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Yue Yu, Rongzhi Zhang, Ran Xu, Jieyu Zhang, Jiaming Shen, Chao Zhang

RRAtention: Dynamic Block Sparse Attention via Per-Head Round-Robin Shifts for Long-Context Inference (2026.acl-long)

Challenge:	Pre-trained language models (PLMs) have achieved competitive performance with limited labeled data for many NLP tasks.
Approach:	They propose a prompt-based data selection method for pre-trained language models fine-tuning under cold-start scenarios.
Outcome:	The proposed method outperforms the strongest cold-start data selection baselines on six text classification datasets with 128 labels.

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Siran Liu, Guoxia Wang, Sa Wang, Jinle Zeng, Haoyang Xie, Siyu Lou, Jiabin Yang, Dianhai Yu, Haifeng Wang, Chao Yang

Visual Data in NLP

Challenge:	Existing approaches to dynamic sparse attention require preprocessing, lack global evaluation, violate query independence, or incur high computational overhead.
Approach:	They propose a dynamic sparse attention method that achieves all desirable properties through a head round-robin (RR) sampling strategy.
Outcome:	Experiments on natural language understanding and multimodal video comprehension show that the proposed method achieves 2.4 speedup at 128K context length outperforming existing methods.

Inference-Time Language Model Alignment via Integrated Value Guidance (2024.findings-emnlp)

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Zhixuan Liu, Zhanhui Zhou, Yuanfu Wang, Chao Yang, Yu Qiao

MADS: Multi-Agent Dialogue Simulation for Diverse Persuasion Data Generation (2025.emnlp-industry)

Challenge:	Large language models are fine-tuned to align with human preferences, but tuning large models is computationally intensive and complex.
Approach:	They propose a method that uses implicit and explicit value functions to guide language model decoding at token and chunk-level respectively.
Outcome:	The proposed method outperforms traditional methods and circumvents the complexities of fine-tuning.

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Mingjin Li, Yu Liu, Huayi Liu, Xiang Ye, Chao Jiang, Hongguang Zhang, Yu Ruan

Natural Language Interfaces

Challenge:	Recent studies show that LLM-based agents exhibit superior moral and emotional language performance compared to humans, raising expectations for their deployment in persuasive tasks.
Approach:	They propose a framework for generating persuasive multi-turn dialogues via agent self-play using user agents designed to simulate diverse persona-driven behaviors, a Dialog Agent executing task-oriented persuasion strategies and an Optimization Agent evaluating and refining dialogue outcomes.
Outcome:	The proposed framework significantly improved the persuasion capacity of small LLMs, increasing the organic traffic conversion rate by 22.4% (from 1.83% to 2.24%) .

Attribution-Based Analysis and Optimization of Modular Agentic Workflows (2026.findings-acl)

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Yingxuan Yang, Bo Huang, Siyuan Qi, Chao Feng, Haoyi Hu, Yuxuan Zhu, Jinbo Hu, Haoran Zhao, Ziyi He, Xiao Liu, ZongYu Wang, Muning Wen, Lin Qiu, Xuezhi Cao, Xunliang Cai, Yong Yu, Weinan Zhang

Challenge:	Large Language Models (LLMs) have driven the rise of agentic workflows . yet, how can we attribute performance gains to individual upgrades and their interactions?
Approach:	They propose a game-theoretic framework that models component upgrades as players and evaluates component coalitions to compute Shapley values.
Outcome:	The proposed framework provides interaction-aware attribution and recommendation for model allocation under a fixed workflow structure.

Training Language Model to Critique for Better Refinement (2025.findings-acl)

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Tianshu Yu, Chao Xiang, Mingchuan Yang, Pei Ke, Bosi Wen, Cunxiang Wang, Jiale Cheng, Li Zhang, Xinyu Mu, Chuxiong Sun, Minlie Huang

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

Challenge:	Large language models (LLMs) have remarkable evaluation and critique capabilities, providing insightful feedback and identifying flaws in various tasks.
Approach:	They propose a framework to train critic models using refinement signals to generate feedback loops where critiques guide the model in refining its responses.
Outcome:	The proposed framework outperforms traditional methods and open-source models in terms of critique quality and refinement outcomes.

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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

Speech-Text Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment (2023.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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Tianshu Yu, Haoyu Gao, Ting-En Lin, Min Yang, Yuchuan Wu, Wentao Ma, Chao Wang, Fei Huang, Yongbin Li

ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select (2022.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.

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Yuchen Zhuang, Yinghao Li, Junyang Zhang, Yue Yu, Yingjun Mou, Xiang Chen, Le Song, Chao Zhang

Information Extraction & Text Mining

Challenge:	Our proposed method extracts N-ary relation tuples from scientific articles.
Approach:	They propose a method that decomposes the task into two stages . they propose modal query and modal entity selection . their results show that ReSel outperforms state-of-the-art baselines significantly .
Outcome:	The proposed method outperforms state-of-the-art baselines on three scientific information extraction datasets.

ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora (2021.emnlp-main)

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Xuan Ouyang, Shuohuan Wang, Chao Pang, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang

Attacks, Defenses and Evaluations for LLM Conversation Safety: A Survey (2024.naacl-long)

Challenge:	Existing methods for pretraining cross-lingual models are limited in their size due to the limited amount of parallel corpora.
Approach:	They propose a method that encourages the model to align multiple languages with monolingual corpora to overcome the constraint of the parallel corpus size.
Outcome:	The proposed method outperforms existing cross-lingual models and delivers new state-of-the-art results in various cross-linguistic downstream tasks.

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Zhichen Dong, Zhanhui Zhou, Chao Yang, Jing Shao, Yu Qiao

Beyond One-Preference-Fits-All Alignment: Multi-Objective Direct Preference Optimization (2024.findings-acl)

Challenge:	Large Language Models (LLMs) are now commonplace in conversation applications, but their misuse for generating harmful responses has raised serious societal concerns.
Approach:	They provide a comprehensive overview of recent studies covering attacks, defenses, and evaluations of Large Language Models (LLMs) .
Outcome:	The proposed review summarizes three aspects of LLM conversation safety: attacks, defenses, and evaluations.

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Zhanhui Zhou, Jie Liu, Jing Shao, Xiangyu Yue, Chao Yang, Wanli Ouyang, Yu Qiao

ARL2: Aligning Retrievers with Black-box Large Language Models via Self-guided Adaptive Relevance Labeling (2024.acl-long)

Challenge:	Recent approaches to language model alignment assume homogeneous human preferences, but actual human preferences vary widely and are hard to satisfy with a single language model.
Approach:	They propose an RL-free extension of Direct Preference Optimization (DPO) that folds language modeling directly into reward modeling and trains language models as collective reward models that combine all objectives with specific weights.
Outcome:	The proposed method matches or outperforms existing methods in safety alignment and long-form question answering.

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LingXi Zhang, Yue Yu, Kuan Wang, Chao Zhang

WebAgent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement Learning (2025.emnlp-main)

Challenge:	Existing retrievers are misaligned with large language models due to separate training processes and inherent black-box nature of LLMs.
Approach:	They propose a retriever learning technique that harnesses LLMs as labelers to annotate and score adaptive relevance evidence.
Outcome:	Extensive experiments show that ARL2 improves accuracy and reduces the cost of API calls.

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Zhepei Wei, Wenlin Yao, Yao Liu, Weizhi Zhang, Qin Lu, Liang Qiu, Changlong Yu, Puyang Xu, Chao Zhang, Bing Yin, Hyokun Yun, Lihong Li

Challenge:	Existing work on reinforcement learning has focused on single-turn tasks such as solving math problems.
Approach:	They propose a framework that learns directly from online interactions by asynchronously generating diverse trajectories, guided by binary rewards depending on task success.
Outcome:	Experiments on the WebArena-Lite benchmark show that the framework outperforms state-of-the-art methods and strong proprietary models.

M3AV: A Multimodal, Multigenre, and Multipurpose Audio-Visual Academic Lecture Dataset (2024.acl-long)

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Zhe Chen, Heyang Liu, Wenyi Yu, Guangzhi Sun, Hongcheng Liu, Ji Wu, Chao Zhang, Yu Wang, Yanfeng Wang

Self-Paced Learning for Neural Machine Translation (2020.emnlp-main)

Challenge:	Publishing open-source academic video recordings is an emerging approach to sharing knowledge online.
Approach:	They propose a multimodal, multigenre, and multipurpose audio-visual academic lecture dataset with human annotations for multimodal content recognition and understanding tasks.
Outcome:	The proposed dataset can be used for multiple audio-visual recognition and understanding tasks.

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Yu Wan, Baosong Yang, Derek F. Wong, Yikai Zhou, Lidia S. Chao, Haibo Zhang, Boxing Chen

Machine Translation

Challenge:	Existing studies have shown that the training of neural machine translation (NMT) rely on the quality of artificial schedule drawn up with the handcrafted features, e.g. sentence length or word rarity.
Approach:	They propose to train NMT model using a self-paced learning approach that allows it to quantify the learning confidence over training examples and flexibly govern its learning via regulating the loss in each iteration step.
Outcome:	The proposed model outperforms baseline models and those trained with human-designed curricula on translation quality and convergence speed.

Learning Word Embeddings for Low-Resource Languages by PU Learning (N18-1)

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Chao Jiang, Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang

Self-Generated Critiques Boost Reward Modeling for Language Models (2025.naacl-long)

Challenge:	Existing approaches to learn word embedding on a corpus with only a few million tokens are limited to low-resource languages.
Approach:	They propose to use a sparse co-occurrence matrix to factorize the co-existence matrix and validate the proposed approaches in four different languages.
Outcome:	The proposed model is validated in four different languages.

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Yue Yu, Zhengxing Chen, Aston Zhang, Liang Tan, Chenguang Zhu, Richard Yuanzhe Pang, Yundi Qian, Xuewei Wang, Suchin Gururangan, Chao Zhang, Melanie Kambadur, Dhruv Mahajan, Rui Hou

Learning-by-Narrating: Narrative Pre-Training for Zero-Shot Dialogue Comprehension (2022.acl-short)

Challenge:	Existing reward models produce scalar scores and struggle to incorporate critiques in a natural language format.
Approach:	They propose a framework that predicts critiques and rewards using self-generated critiques without extra supervision.
Outcome:	The proposed framework improves reward modeling accuracy by 3.7%-7.3% compared to standard reward models and LLM judges.

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Chao Zhao, Wenlin Yao, Dian Yu, Kaiqiang Song, Dong Yu, Jianshu Chen

Dialogue Systems & Conversational Agents

Challenge:	Existing models for dialogue comprehension are not available for the pre-training of such a model.
Approach:	They propose a narrative-guided pre-training strategy that learns by narrating key information from a dialogue input.
Outcome:	The proposed model performs better on four dialogue-based tasks and is comparable to existing models.

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

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

Dialogue Systems & Conversational Agents

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.

Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach (2021.naacl-main)

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Yue Yu, Simiao Zuo, Haoming Jiang, Wendi Ren, Tuo Zhao, Chao Zhang

UniTE: Unified Translation Evaluation (2022.acl-long)

Challenge:	Fine-tuned pre-trained language models (LMs) have enormous success in many natural language processing tasks, but they still require excessive labeled data in the fine-tuning stage.
Approach:	They propose a framework to enable fine-tuning pre-trained language models with weak supervision without any labeled data.
Outcome:	The proposed framework outperforms the strongest baseline and achieves competitive performance with fully-supervised fine-tuning methods.

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Yu Wan, Dayiheng Liu, Baosong Yang, Haibo Zhang, Boxing Chen, Derek Wong, Lidia Chao

Multilinguality

Challenge:	Recent methods for evaluation of translation quality are focused on one task, ignoring commonalities .
Approach:	They propose a unified framework engaged with abilities to handle all three evaluation tasks.
Outcome:	The proposed framework can universally surpass state-of-the-art or winner methods across tasks.

Language Model Based Text-to-Audio Generation: Anti-Causally Aligned Collaborative Residual Transformers (2025.emnlp-main)

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Juncheng Wang, Chao Xu, Cheng Yu, Zhe Hu, Haoyu Xie, Guoqi Yu, Lei Shang, Shujun Wang

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

Challenge:	Autoregressive language models excel in text-to-audio generation, but lag behind diffusion models by a non-trivial margin.
Approach:	They propose a framework that integrates multiple isolated transformers with causal conditioning and anti-causal alignment via reinforcement learning.
Outcome:	The proposed framework outperforms existing LM-based and diffusion-based systems in audio synthesis.

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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

Red Teaming Large Reasoning Models (2026.acl-long)

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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Jiawei Chen, Yang Yang, Chao Yu, Yu Tian, Zhi Cao, Xue Yang, Linghao Li, Hang Su, Zhaoxia Yin

ReGen: Zero-Shot Text Classification via Training Data Generation with Progressive Dense Retrieval (2023.findings-acl)

Challenge:	Large Reasoning Models (LRMs) have emerged as a powerful advancement in multi-step reasoning tasks, but they introduce safety and reliability risks, such as CoT-hijacking and prompt-induced inefficiencies.
Approach:	They propose a unified benchmark to assess the trustworthiness of Large Reasoning Models.
Outcome:	The proposed benchmark evaluates truthfulness, safety and efficiency on 26 models.

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Yue Yu, Yuchen Zhuang, Rongzhi Zhang, Yu Meng, Jiaming Shen, Chao Zhang

Correcting Chinese Spelling Errors with Phonetic Pre-training (2021.findings-acl)

Challenge:	Recent studies show that large pretrained language models can generate training data with no task-specific or cross-task data.
Approach:	They propose a retrieval-enhanced framework to create training data from a general-domain unlabeled corpus.
Outcome:	The proposed framework achieves 4.3% gain over baselines and saves 70% of time compared with baselines using large language models.

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Ruiqing Zhang, Chao Pang, Chuanqiang Zhang, Shuohuan Wang, Zhongjun He, Yu Sun, Hua Wu, Haifeng Wang

Syntactic Text Processing

Challenge:	Existing methods for Chinese spelling correction only use pre-trained language model or incorporate phonological information as external knowledge.
Approach:	They propose a phonetic Chinese spelling correction model that integrates phonetic features into language model by leveraging pre-training and fine-tuning methods.
Outcome:	The proposed model outperforms existing methods on SIGHAN datasets and improves on other datasets.

Can Reasoning Path still be Effective as Input? Bridging Post-Reasoning to Chain-of-Thought Compression (2026.acl-long)

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Chengzhengxu Li, Xiaoming Liu, Zhaohan Zhang, Shengchao Liu, Guoxin Ma, Yu Lan, Cong Wang, Chao Shen

SEER: Facilitating Structured Reasoning and Explanation via Reinforcement Learning (2024.acl-long)

Challenge:	Existing work on reducing CoT generation in reasoning impairs the necessary information for deriving the correct answer.
Approach:	They propose a reasoning paradigm that takes CoT as a part of context to simplify the reasoning task for Large Language Models (LLMs).
Outcome:	The proposed framework reduces the generation length of LLMs, but its effectiveness hinges on the efficiency and reliability of the contextual CoT generation.

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Guoxin Chen, Kexin Tang, Chao Yang, Fuying Ye, Yu Qiao, Yiming Qian

Harnessing Pre-Trained Neural Networks with Rules for Formality Style Transfer (D19-1)

Challenge:	Existing methods focus on single-step reasoning, ignoring logical dependencies between steps.
Approach:	They propose a method that maximizes a structure-based return to facilitate structured reasoning and explanation.
Outcome:	The proposed method outperforms state-of-the-art methods on EntailmentBank and STREET benchmarks.

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Yunli Wang, Yu Wu, Lili Mou, Zhoujun Li, Wenhan Chao

DRIFT: Transferring Reasoning Priors for Efficient MLLM Fine-Tuning (2026.findings-acl)

Challenge:	Existing studies normalize informal sentences with rules, but they introduce noise if we use them in a naive way.
Approach:	They propose to harness rules into a state-of-the-art neural network that is typically pretrained on massive corpora.
Outcome:	The proposed method can be used to generate a state-of-the-art on a small dataset.

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Chao Huang, Zeliang Zhang, Jiang Liu, Ximeng Sun, Jialian Wu, Xiaodong Yu, Ze Wang, Chenliang Xu, Emad Barsoum, Zicheng Liu

Reasoning

Challenge:	Multimodal large language models (MLLMs) have made rapid progress in perception and alignment, but their reasoning ability often lags behind strong text-only LLMs.
Approach:	They propose a method that transfers reasoning knowledge in the gradient space while preserving multimodal alignment.
Outcome:	Experiments on multimodal reasoning benchmarks show that DRIFT outperforms naive merging and standard SFT.

Formality Style Transfer with Shared Latent Space (2020.coling-main)

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Yunli Wang, Yu Wu, Lili Mou, Zhoujun Li, WenHan Chao

Multilinguality

Challenge:	Existing approaches for formality style transfer use neural networks for sentence generation, but the dataset for formal style transfer is considerably smaller than translation corpora.
Approach:	They propose a new approach for formality style transfer using shared latent space and two auxiliary losses.
Outcome:	The proposed approach outperforms baselines in various settings, especially when limited data is available.

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

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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.

Vision-Flan: Scaling Human-Labeled Tasks in Visual Instruction Tuning (2024.findings-acl)

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Zhiyang Xu, Chao Feng, Rulin Shao, Trevor Ashby, Ying Shen, Di Jin, Yu Cheng, Qifan Wang, Lifu Huang

Does DetectGPT Fully Utilize Perturbation? Bridging Selective Perturbation to Fine-tuned Contrastive Learning Detector would be Better (2024.acl-long)

Challenge:	Recent vision-language models (VLMs) have shown impressive capabilities as general visual assistants, but there are two challenges to their performance: (1) lacking task diversity in pretraining and visual instruction tuning; (2) annotation error and bias in GPT-4 synthesized instruction tuning data.
Approach:	They propose a two-stage instruction tuning framework that fine tunes VLMs firstly and further tuned on GPT-4 synthesized data.
Outcome:	The proposed framework outperforms the traditional single-stage visual instruction tuning framework and achieves state-of-the-art performance across a wide range of multi-modal evaluation benchmarks.

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Shengchao Liu, Xiaoming Liu, Yichen Wang, Zehua Cheng, Chengzhengxu Li, Zhaohan Zhang, Yu Lan, Chao Shen

Self-Training with Differentiable Teacher (2022.findings-naacl)

Challenge:	Existing methods to detect MGT from human-written texts are inadequate . existing methods are fine-tuned and zero-shot metric-based, but they can be more accurate.
Approach:	They propose a novel fine-tuned detector that can detect MGT from human-written texts by contrastive learning on selective perturbation.
Outcome:	The proposed method outperforms the state-of-the-art by 1.20% on four public datasets.

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Simiao Zuo, Yue Yu, Chen Liang, Haoming Jiang, Siawpeng Er, Chao Zhang, Tuo Zhao, Hongyuan Zha

Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning (2022.acl-long)

Challenge:	Existing methods for self-training are interpreted as teacher-student frameworks, where the teacher generates pseudo-labels and the student makes predictions.
Approach:	They propose a differentiable self-training method that treats teacher-student as a Stackelberg game where a leader is always in a more advantageous position than a follower.
Outcome:	The proposed model outperforms existing methods on semi- and weakly-supervised learning tasks on semi and weak supervised tasks.

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Rongzhi Zhang, Yue Yu, Pranav Shetty, Le Song, Chao Zhang

Low-Resource NLP

Challenge:	Weakly-supervised learning (WSL) has shown promising results in addressing label scarcity on many NLP tasks, but manual designing a comprehensive, high-quality set of labeling rules is tedious and difficult.
Approach:	They propose a weakly-supervised learning model that iterates and discovers new labeling rules from data to improve the WSL model.
Outcome:	The proposed model outperforms state-of-the-art models on four tasks and bridges the gaps with fully supervised models.

Dynamic Collaboration of Multi-Language Models based on Minimal Complete Semantic Units (2025.emnlp-main)

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Chao Hao, Zezheng Wang, Yanhua Huang, Ruiwen Xu, Wenzhe Niu, Xin Liu, Zitong Yu

LightRAG: Simple and Fast Retrieval-Augmented Generation (2025.findings-emnlp)

Challenge:	Existing methods to enhance reasoning capabilities of language models are expensive and often lack the ability to perform complex reasoning tasks.
Approach:	They propose a token-level multi-model collaboration strategy to enhance reasoning capabilities in language models by selecting the optimal tokens from the next token distributions.
Outcome:	The proposed method is superior to existing methods and will be released soon.

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Zirui Guo, Lianghao Xia, Yanhua Yu, Tu Ao, Chao Huang

Green & Sustainable NLP

Challenge:	Existing RAG systems rely on flat data representations and inadequate contextual awareness . lightRAG framework incorporates graph structures into text indexing and retrieval processes .
Approach:	LightRAG is a framework that integrates graph structures into text indexing and retrieval processes.
Outcome:	The proposed framework incorporates graph structures into text indexing and retrieval processes.

D4: a Chinese Dialogue Dataset for Depression-Diagnosis-Oriented Chat (2022.emnlp-main)

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Binwei Yao, Chao Shi, Likai Zou, Lingfeng Dai, Mengyue Wu, Lu Chen, Zhen Wang, Kai Yu

Dialogue Systems & Conversational Agents

Challenge:	Existing human-machine dialogue systems are not able to provide diagnostic information for depression diagnosis due to stigma associated with mental illness.
Approach:	They propose to construct a Chinese Dialogue Dataset for depression-diagnosis-oriented chat based on clinical depression diagnostic criteria.
Outcome:	The proposed system can be used to diagnose depression using a Chinese Dialogue Dataset.

Uncertainty-Aware Curriculum Learning for Neural Machine Translation (2020.acl-main)

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Yikai Zhou, Baosong Yang, Derek F. Wong, Yu Wan, Lidia S. Chao

ERNIE-Code: Beyond English-Centric Cross-lingual Pretraining for Programming Languages (2023.findings-acl)

Challenge:	Neural machine translation (NMT) has proven to be facilitated by curriculum learning which presents examples in an easy-to-hard order at different training stages.
Approach:	They propose to use an uncertainty-aware curriculum learning approach to assess data difficulty and model competence to provide examples in an easy-to-hard order at different training stages.
Outcome:	The proposed approach outperforms baseline and related methods on translation quality and convergence speed.

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Yekun Chai, Shuohuan Wang, Chao Pang, Yu Sun, Hao Tian, Hua Wu

Multilinguality

Challenge:	ERNIE-Code is a unified pre-trained language model for 116 NLs and 6 PLs.
Approach:	They propose a unified pre-trained language model for 116 NLs and 6 PLs . they employ span-corruption language modeling that learns patterns from monolingual NL or PL .
Outcome:	The proposed model outperforms previous multilingual models for NL or NL across end tasks.

Self-Adjust Softmax (2025.emnlp-main)

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Chuanyang Zheng, Yihang Gao, Guoxuan Chen, Han Shi, Jing Xiong, Xiaozhe Ren, Chao Huang, Zhenguo Li, Yu Li

SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup (2020.emnlp-main)

Challenge:	Usually, tokens with larger attention scores are important for the final prediction.
Approach:	They propose to modify softmax(z) to z softmax and its normalized variant to improve the Transformer attention mechanism by making minor adjustments to the softmax function.
Outcome:	The proposed model provides enhanced gradient properties compared to the vanilla softmax function.

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Rongzhi Zhang, Yue Yu, Chao Zhang

QualiSpeech: A Speech Quality Assessment Dataset with Natural Language Reasoning and Descriptions (2025.acl-long)

Challenge:	Existing active sequence labeling methods use the queried samples alone in each iteration, which is inefficient for leveraging human annotations.
Approach:	They propose a data augmentation method to augment queried samples by generating extra labeled sequences in each iteration.
Outcome:	The proposed method improves the standard active sequence labeling method by 2.27%–3.75% in terms of F1 scores.

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Siyin Wang, Wenyi Yu, Xianzhao Chen, Xiaohai Tian, Jun Zhang, Lu Lu, Yu Tsao, Junichi Yamagishi, Yuxuan Wang, Chao Zhang

Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning (2024.acl-long)

Challenge:	Existing datasets lack comprehensive annotations for speech quality assessment . existing methods lack detailed annotations, resulting in inaccurate evaluations.
Approach:	They propose a low-level speech quality assessment dataset incorporating natural language descriptions and a Benchmark to evaluate low- level speech understanding capabilities of auditory large language models.
Outcome:	The proposed model can be used to evaluate the low-level speech understanding capabilities of auditory large language models.

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Yue Yu, Jiaming Shen, Tianqi Liu, Zhen Qin, Jing Nathan Yan, Jialu Liu, Chao Zhang, Michael Bendersky

LLM×MapReduce: Simplified Long-Sequence Processing using Large Language Models (2025.acl-long)

Challenge:	Recent advances in natural language processing (NLP) have witnessed the remarkable capabilities of Large Language Models (LLMs).
Approach:	They propose an Explanation-Aware Soft Ensemble framework to empower in-context learning with Large language models.
Outcome:	The proposed framework can be used to enhance in-context learning on seven natural language understanding tasks and four varying-size LLMs.

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Zihan Zhou, Chong Li, Xinyi Chen, Shuo Wang, Yu Chao, Zhili Li, Haoyu Wang, Qi Shi, Zhixing Tan, Xu Han, Xiaodong Shi, Zhiyuan Liu, Maosong Sun

Joint Learning Event-Specific Probe and Argument Library with Differential Optimization for Document-Level Multi-Event Extraction (2025.findings-naacl)

Challenge:	Existing studies have focused on extending the context length of large language models (LLMs) due to their quadratic computational complexity and a lack of high-quality long training examples, most LLMs are trained with a limited window size.
Approach:	They propose a training-free framework that enables large language models to effectively process long texts using a divide-and-conquer strategy for comprehensive document understanding.
Outcome:	The proposed framework outperforms open-source and commercial long-context LLMs and is compatible with several models.

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Jianpeng Hu, Chao Xue, Chunqing Yu, JiaCheng Xu, Chengxiang Tan

Information Extraction & Text Mining

Challenge:	Existing methods for document-level multi-event extraction neglect the fine-grained difference between events in multi-documents, which leads to event confusion and missing.
Approach:	They propose an event-specific probe-based method to sniff multiple events by querying each corresponding argument library.
Outcome:	The proposed method outperforms the state-of-the-art method in the recall of multi-events.

StablePT : Towards Stable Prompting for Few-shot Learning via Input Separation (2024.findings-emnlp)

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Xiaoming Liu, Chen Liu, Zhaohan Zhang, Chengzhengxu Li, Longtian Wang, Yu Lan, Chao Shen

scRAG: Hybrid Retrieval-Augmented Generation for LLM-based Cross-Tissue Single-Cell Annotation (2025.findings-acl)

Challenge:	Existing studies on prompt tuning have shown that language models can be effective few-shot learners with prompting.
Approach:	They propose to treat the hard prompt and soft prompt as separate inputs to mitigate noise brought by prompt initialization.
Outcome:	Experimental results show that the proposed method outperforms state-of-the-art methods by 6.97% in accuracy and reduces the standard deviation by 1.92 on average.

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Zhiyin Yu, Chao Zheng, Chong Chen, Xian-Sheng Hua, Xiao Luo

Information Retrieval

Challenge:	Large language models (LLMs) have demonstrated impressive potential in a wide range of fields, including biology, genomics and healthcare.
Approach:	They propose a framework that integrates advanced LLM-based RAG techniques into cross-tissue single-cell annotation.
Outcome:	The proposed framework outperforms baseline models, generalist models, domain-specific methods, and trained classifiers on a cross-tissue dataset.

NSP-BERT: A Prompt-based Few-Shot Learner through an Original Pre-training Task —— Next Sentence Prediction (2022.coling-1)

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Yi Sun, Yu Zheng, Chao Hao, Hangping Qiu