Papers by Yiming Wang

Gradient-based Intra-attention Pruning on Pre-trained Language Models (2023.acl-long)

Challenge:	Existing text-based recommendation frameworks that use pretrained language models (PLMs) can improve performance on text-related tasks.
Approach:	They propose a unified local- and global-attention Transformer encoder to better model two-level contexts of user history.
Outcome:	The proposed framework improves on three text-based recommendation tasks.

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Ziqing Yang, Yiming Cui, Xin Yao, Shijin Wang

On the Sentence Embeddings from Pre-trained Language Models (2020.emnlp-main)

Challenge:	Pre-trained language models are computationally expensive and slow in inference due to their large sizes.
Approach:	They propose a structured pruning method which combines pruning with knowledge distillation to yield highly effective models.
Outcome:	The proposed method outperforms other pruning methods in sparsity regimes while maintaining 93% 99% performance.

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Bohan Li, Hao Zhou, Junxian He, Mingxuan Wang, Yiming Yang, Lei Li

JiraiBench: A Bilingual Benchmark for Evaluating Large Language Models’ Detection of Human risky health behavior Content in Jirai Community (2026.eacl-long)

Challenge:	Pre-trained contextual representations like BERT have been widely used for NLP tasks.
Approach:	They propose to transform anisotropic sentence embedding distribution to smooth and isotropic Gaussian distribution by normalizing flows that are learned with an unsupervised objective.
Outcome:	The proposed method achieves significant performance gains over state-of-the-art embeddings on a variety of semantic textual similarity tasks.

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Yunze Xiao, Tingyu He, Lionel Z. Wang, Yiming Ma, Xingyu Song, Xiaohang Xu, Mona T. Diab, Irene Li, Ka Chung Ng

GL-GAN: Perceiving and Integrating Global and Local Styles for Handwritten Text Generation with Mamba (2025.coling-main)

Challenge:	a cross-lingual dataset captures a transnational cultural phenomenon . risky health behaviors (RHB) are often linked to complex mental health conditions .
Approach:	They present the first cross-lingual dataset that captures a transnational cultural phenomenon . their dataset of more than 15,000 annotated social media posts forms the core of JiraiBench .
Outcome:	The study shows that cultural context can be more influential than linguistic similarity . the study also shows that the Japanese prompts better handle Chinese content .

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Yiming Wang, Hongxi Wei, Heng Wang, Shiwen Sun, Chao He

Text Generation

Challenge:	Existing models lack the ability to perceive and integrate handwriting styles, which affects the realism of the synthesized samples.
Approach:	They propose a Hybrid Style Encoder that captures global and local styles and integrates them into a Dynamic Feature Enhancement Module (DFEM).
Outcome:	The proposed model outperforms state-of-the-art models on two widely used handwriting datasets and can provide training data for handwritten text recognition and signature verification.

Generate, Discriminate and Contrast: A Semi-Supervised Sentence Representation Learning Framework (2022.emnlp-main)

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Yiming Chen, Yan Zhang, Bin Wang, Zuozhu Liu, Haizhou Li

GMSA: Enhancing Context Compression via Group Merging and Layer Semantic Alignment (2026.acl-long)

Challenge:	Existing supervised sentence embedding techniques rely on expensive human-annotated sentence pairs as the supervised signals.
Approach:	They propose a semi-supervised sentence embedding framework that leverages large-scale unlabeled data.
Outcome:	The proposed framework surpasses state-of-the-art methods on four domain adaptation tasks.

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Jiwei Tang, Zhicheng Zhang, Shunlong Wu, Jingheng Ye, Lichen Bai, Zitai Wang, Tingwei Lu, Lin Hai, Yiming Zhao, Hai-Tao Zheng, Hong-Gee Kim

Know-Adapter: Towards Knowledge-Aware Parameter-Efficient Transfer Learning for Few-shot Named Entity Recognition (2024.lrec-main)

Challenge:	Large Language Models (LLMs) have achieved remarkable performance across NLP tasks . however, in long-context scenarios, they face high computational cost and information redundancy.
Approach:	They propose an encoder-decoder context compression framework that generates a compact sequence of soft tokens for downstream tasks.
Outcome:	Experiments show that GMSA outperforms baselines on multiple long-context question answering and summarization benchmarks while maintaining low end-to-end latency.

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Binling Nie, Yiming Shao, Yigang Wang

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

Challenge:	Named entity recognition (NER) is a fundamental task in natural language processing.
Approach:	They propose a knowledgeable adapter to incorporate structure and semantic knowledge of knowledge graphs into PLMs for few-shot NER.
Outcome:	The proposed adapter improves the quality of retrieved information by adding explicit knowledge from external sources to PEFTs.

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

Extracting Topics with Simultaneous Word Co-occurrence and Semantic Correlation Graphs: Neural Topic Modeling for Short Texts (2021.findings-emnlp)

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.

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Yiming Wang, Ximing Li, Xiaotang Zhou, Jihong Ouyang

CMQA: A Dataset of Conditional Question Answering with Multiple-Span Answers (2022.coling-1)

Challenge:	Empirical results validate that DWGTM can generate more semantically coherent topics than baseline topic models.
Approach:	They develop a neural topic model which extracts topics from word co-occurrence graphs . Empirical results validate that DWGTM can generate more semantically coherent topics than baseline topic models.
Outcome:	Empirical results show that the proposed model can generate more coherent topics than baseline topic models.

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Yiming Ju, Weikang Wang, Yuanzhe Zhang, Suncong Zheng, Kang Liu, Jun Zhao

Cross-Lingual Machine Reading Comprehension (D19-1)

Challenge:	Existing QA datasets only contain unconditional and parallel answers . conditional question answering with hierarchical multi-span answers is challenging for the community to solve .
Approach:	They propose a conditional question answering task with hierarchical multi-span answers . they propose CMQA, which contains conditional and hierarchic samples .
Outcome:	The proposed task can be used to build more reliable and sophisticated QA systems.

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Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Shijin Wang, Guoping Hu

Machine Reading Comprehension

Challenge:	Existing work on machine reading comprehension task is focused on English, but there are few efforts on other languages due to the lack of large-scale training data.
Approach:	They propose a cross-lingual machine reading comprehension task for other languages . they propose cloze-style reading comprehension and various neural network approaches .
Outcome:	The proposed model improves reading comprehension performance of Chinese datasets over state-of-the-art systems by a large margin over existing systems.

DARM: Distribution-Aware Reward Modeling by Alleviating Biases from Low Preference-Context Dependency Data (2026.acl-long)

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Shaofan Liu, Guoqiang Zhang, Shihan Dou, Huiyuan Zheng, Yiming Zhou, Junjie Ye, Shaowen Wang, Shichun Liu, Jiazheng Zhang, Tao Gui, Qi Zhang, Xuanjing Huang

Challenge:	Existing methods for training reward models are vulnerable to context neglect and degraded accuracy.
Approach:	They propose distribution-aware reward modeling that augments the RM objective with a conditional mutual information regularizer that maximizes context and the predicted reward conditioned on the response.
Outcome:	The proposed model improves performance in RLHF and improves accuracy in other settings.

MlingConf: A Comprehensive Study of Multilingual Confidence Estimation on Large Language Models (2025.findings-acl)

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Boyang Xue, Hongru Wang, Rui Wang, Sheng Wang, Zezhong Wang, Yiming Du, Bin Liang, Wenxuan Zhang, Kam-Fai Wong

FinDVer: Explainable Claim Verification over Long and Hybrid-content Financial Documents (2024.emnlp-main)

Challenge:	Existing studies on LLM confidence estimations in languages other than English have been limited to English.
Approach:	They propose to use question-related language to prompt LLMs to assess their confidence in large language models.
Outcome:	The proposed model improves on question-related language prompts for LS tasks, while English exhibits notable linguistic dominance in confidence estimations.

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Yilun Zhao, Yitao Long, Tintin Jiang, Chengye Wang, Weiyuan Chen, Hongjun Liu, Xiangru Tang, Yiming Zhang, Chen Zhao, Arman Cohan

Meta-Reasoning: Semantics-Symbol Deconstruction for Large Language Models (2024.findings-acl)

Challenge:	FinDVer is a benchmark to evaluate the explainable claim verification capabilities of LLMs . financial documents are typically long, intricate and dense, and they include both quantita and numerical reasoning.
Approach:	They propose a benchmark to evaluate the explainable claim verification capabilities of LLMs . they assess 25 LLM systems under long-context and RAG settings .
Outcome:	The proposed benchmark can be used to evaluate the explainable claim verification capabilities of LLMs in financial documents.

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Yiming Wang, Zhuosheng Zhang, Pei Zhang, Baosong Yang, Rui Wang

GODBench: A Benchmark for Multimodal Large Language Models in Video Comment Art (2025.acl-long)

Challenge:	Existing methods rely on syntactically mapping natural languages to complete formal languages like Python and SQL.
Approach:	They propose to deconstruct reasoning-independent semantic information into generic symbolic representations, thereby efficiently capturing more generalized reasoning knowledge.
Outcome:	The proposed method improves in-context reasoning accuracy, learning efficiency, out-of-domain generalization, and output stability compared to the Chain-of thought technique.

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Yiming Lei, Chenkai Zhang, Zeming Liu, Haitao Leng, ShaoGuo Liu, Tingting Gao, Qingjie Liu, Yunhong Wang

IDOL: Indicator-oriented Logic Pre-training for Logical Reasoning (2023.findings-acl)

Challenge:	Existing benchmarks for video comment art are constrained by their limited modalities and insufficient categories, hindering creativity in video-based comment art creation.
Approach:	They propose a benchmark that integrates video and text modalities to evaluate MLLMs’ abilities to compose video Comment art.
Outcome:	The proposed framework integrates video and text modalities to evaluate MLLMs’ abilities to compose video comment art.

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Zihang Xu, Ziqing Yang, Yiming Cui, Shijin Wang

TeCS: A Dataset and Benchmark for Tense Consistency of Machine Translation (2023.acl-short)

Challenge:	Existing systems for logical reasoning have surpassed the average performance of humans in many tasks like SQuAD but there is still a long way to go when it comes to logical reasoning.
Approach:	They propose an InDicator-Oriented Logic Pre-training task which logically strengthens pre-trained models with the help of 6 types of logical indicators and a logicalally rich dataset.
Outcome:	The proposed task achieves state-of-the-art on ReClor and LogiQA, the two most representative benchmarks in logical reasoning MRC.

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Yiming Ai, Zhiwei He, Kai Yu, Rui Wang

Machine Translation

Challenge:	tense inconsistency is a common problem in machine translation systems.
Approach:	They propose a parallel tense test set, containing French-English 552 utterances, and introduce a benchmark, tence prediction accuracy.
Outcome:	The proposed model can measure the tense consistency performance of machine translation systems for the first time.

Adversarial Training for Machine Reading Comprehension with Virtual Embeddings (2021.starsem-1)

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Ziqing Yang, Yiming Cui, Chenglei Si, Wanxiang Che, Ting Liu, Shijin Wang, Guoping Hu

When Backdoors Speak: Understanding LLM Backdoor Attacks Through Model-Generated Explanations (2025.acl-long)

Challenge:	Neural networks are vulnerable to adversarial examples that have been mixed with certain perturbations.
Approach:	They propose a novel adversarial training method that perturbs the embedding matrix instead of word vectors to differentiate the roles of passages and questions.
Outcome:	The proposed method is effective universally and further improves the performance of MRC tasks.

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Huaizhi Ge, Yiming Li, Qifan Wang, Yongfeng Zhang, Ruixiang Tang

CSLM: A Framework for Question Answering Dataset Generation through Collaborative Small Language Models (2024.findings-emnlp)

Challenge:	Recent studies have shown that Large Language Models (LLMs) are susceptible to backdoor attacks, where triggers embedded in poisoned data can maliciously alter LLMs’ behaviors.
Approach:	They propose to leverage LLMs' generative capabilities to generate human-readable explanations for their decisions, enabling direct comparisons between explanations of clean and poisoned data.
Outcome:	The proposed model produces coherent explanations for clean inputs but logically flawed explanations on poisoned data.

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Yiming Wang, Yang Liu, Lingchen Wang, An Xiao

KwaiChat: A Large-Scale Video-Driven Multilingual Mixed-Type Dialogue Corpus (2025.findings-naacl)

Challenge:	Collecting high-quality question-answer (QA) pairs is vital for training large language models, but computational demands and associated costs often render such approaches prohibitive for the average researcher.
Approach:	They propose a small-scaled, open-source solution that generates QA pairs from documents or raw corpora using large-scale models like Llama-70B.
Outcome:	Experiments on domain-specific datasets show that the proposed model can generate high-quality QA pairs, making it accessible to a broader range of researchers.

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Xiaoming Shi, Zeming Liu, Yiming Lei, Chenkai Zhang, Haitao Leng, Chuan Wang, Qingjie Liu, Wanxiang Che, Yunhong Wang

RouteMoA: Dynamic Routing without Pre-Inference Boosts Efficient Mixture-of-Agents (2026.acl-long)

Challenge:	Currently, video-based dialogue systems rely on a single dialogue type, hindering their versatility in practical applications.
Approach:	They propose to generate video-driven multilingual mixed-type dialogues using KwaiChat . they propose to create a video-based multilingual mix of 4 dialogue types, 30 domains, 4 languages, 13 topics .
Outcome:	The proposed model performs best on KwaiChat but is not perfect in this situation.

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Jize Wang, Han Wu, Zhiyuan You, Yiming Song, Yijun Wang, Zifei Shan, Yining Li, Songyang Zhang, Xinyi Le, Cailian Chen, Xinping Guan, Dacheng Tao

Green & Sustainable NLP

Challenge:	Existing methods for mixing-of-agents (MoA) lack model selection criteria and struggle with large model pools.
Approach:	They propose a mixture-of-agents framework with dynamic routing that uses a lightweight scorer to perform initial screening and refines the model scores through self- and cross-assessment.
Outcome:	The proposed framework outperforms existing methods for large model pools and tasks . it reduces cost by 89.8% and latency by 63.6% in the large-scale model pool.

PACE: Prefix-Protected and Difficulty-Aware Compression for Efficient Reasoning (2026.findings-acl)

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Ruixiang Feng, Yuntao Wen, Silin Zhou, Ke Shi, Yifan Wang, Ran Le, Zhenwei An, Zongchao Chen, Chen Yang, Guangyue Peng, Yiming Jia, Dongsheng Wang, Tao Zhang, Lisi Chen, Yang Song, Shen Gao, Shuo Shang

CharBERT: Character-aware Pre-trained Language Model (2020.coling-main)

Challenge:	Existing LRMs often suffer from "overthinking" and excessively long reasoning traces . a dual-level framework for length compression of LRM is proposed .
Approach:	They propose a framework for prefix-protected and difficulty-aware compression under hierarchical supervision.
Outcome:	The proposed framework reduces token usage while improving accuracy on math benchmarks.

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Wentao Ma, Yiming Cui, Chenglei Si, Ting Liu, Shijin Wang, Guoping Hu

CLUE: A Chinese Language Understanding Evaluation Benchmark (2020.coling-main)

Challenge:	Pre-trained language models (PLMs) construct word representations at subword level with Byte-Pair Encoding (BPE) or its variations . but these methods split a word into subword units and make it incomplete and fragile .
Approach:	They propose a character-aware pre-trained language model to tackle OOV problems . they construct contextual word embedding for each token from sequential character representations .
Outcome:	The proposed model improves on the existing models on multiple NLP benchmarks.

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Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, Zhenzhong Lan

Challenge:	Existing language evaluation benchmarks for English are limited to English . lack of such benchmarks makes it difficult to replicate success in other languages .
Approach:	They introduce a large-scale Chinese language understanding evaluation benchmark . the benchmark uses a set of current state-of-the-art pre-trained Chinese models .
Outcome:	The first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark is released . the benchmark evaluates models across a wide range of tasks on original Chinese text . existing language evaluation benchmarks are mostly limited to English .

RECOST: External Knowledge Guided Data-efficient Instruction Tuning (2024.findings-acl)

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Qi Zhang, Yiming Zhang, Haobo Wang, Junbo Zhao

Multimodal Invariant Sentiment Representation Learning (2025.findings-acl)

Challenge:	Considering the high computing power overhead, data-efficient instruction tuning is proposed to reduce the training data size.
Approach:	They propose a framework to improve instruction tuning by integrating external knowledge into a single pipeline.
Outcome:	The proposed method achieves better results with only 1% of the full dataset.

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Aoqiang Zhu, Min Hu, Xiaohua Wang, Jiaoyun Yang, Yiming Tang, Ning An

CINO: A Chinese Minority Pre-trained Language Model (2022.coling-1)

Challenge:	Existing methods for multimodal sensing ignore significant sentiment distribution imbalances and cross-modal sentiment conflicts, hindering performance improvement.
Approach:	They propose a method to learn stable multimodal invariant sentiment representations by incorporating distributional discrepancies and sentiment conflicts into the model training.
Outcome:	The proposed method improves MSA performance and achieves new state-of-the-art.

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Ziqing Yang, Zihang Xu, Yiming Cui, Baoxin Wang, Min Lin, Dayong Wu, Zhigang Chen

Multilinguality

Challenge:	Existing multilingual pre-trained language models do not perform well on some low-resource languages.
Approach:	They propose a multilingual pre-trained language model for Chinese minority languages . they collect documents from Wikipedia and construct two classification datasets .
Outcome:	The proposed model outperforms baseline models on various classification tasks.

Fine-Grained Data Ordering Improves Fine-Tuning for Large Language Models (2026.findings-acl)

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Xiaomeng Hu, Yixuan Tang, Haoze Li, Hao Chen, Qi Zhang, Zhanming Shen, Yiming Zhang, Haobo Wang, Junbo Zhao

PESCO: Prompt-enhanced Self Contrastive Learning for Zero-shot Text Classification (2023.acl-long)

Challenge:	Prior work focused on data preprocessing, focusing on filtering and cleaning data . a study aimed to improve fine-grained scheduling of data order in epochs .
Approach:	They propose a fine-grained scheduling method of data order in epochs to fill this gap . they define data difficulty based on relevance between data and model .
Outcome:	The proposed method improves on pre-training and small-scale fine-tuning experiments 2.4% over baselines.

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Yau-Shian Wang, Ta-Chung Chi, Ruohong Zhang, Yiming Yang

CogBench: Benchmarking Cognitive Alignment of Large Language Models in Educational Question Answering (2026.findings-acl)

Challenge:	Existing text classification frameworks require large amounts of human-labeled documents to train .
Approach:	They propose a contrastive learning framework that improves zero-shot text classification . they add prompts to enhance label retrieval and use retrieved labels to enrich training .
Outcome:	The proposed framework achieves state-of-the-art on four benchmark text classification datasets.

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Tong Lu, Zhichun Wang, Yuanhao Sun, Yaoyu Zhou, Mingrui Li, Yiming Guan, Zhiyong Bai

Question Answering

Challenge:	Large language models (LLMs) possess strong capabilities in language understanding and generation, as well as remarkable problem-solving abilities.
Approach:	They propose a benchmark to assess the cognitive alignment capabilities of large language models in educational QA.
Outcome:	The proposed evaluation benchmark assesses the cognitive alignment capabilities of large language models in educational QA.

RAM-SD: Retrieval-Augmented Multi-agent framework for Sarcasm Detection (2026.acl-long)

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Ziyang Zhou, Ziqi Liu, Yan Wang, Yiming Lin, Yangbin Chen

Retrieval-enriched zero-shot image classification in low-resource domains (2024.emnlp-main)

Challenge:	Existing approaches to sarcastic detection use a uniform reasoning strategy . existing approaches lack a framework to deal with the diverse analytical demands of sarcasm .
Approach:	They propose a Retrieval-Augmented Multi-Agent framework for Sarcasm Detection . the framework provides transparent and interpretable reasoning traces .
Outcome:	The proposed framework outperforms existing methods on four benchmarks and outperformed the strong GPT-4o+CoC baseline.

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Nicola Dall’Asen, Yiming Wang, Enrico Fini, Elisa Ricci

Information Retrieval

Challenge:	Low-resource domains are those where data or annotations are scarce.
Approach:	They propose a retrieval-based method for low-resource domains that trains without training . they use web-crawled databases to retrieve relevant textual information from query images .
Outcome:	The proposed method outperforms existing training-based methods in low-resource domains . it retrieves relevant textual information from large web-crawled databases .

Mitigating Context Interference for Reliable and Efficient Search Agents (2026.acl-long)

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Boyang Xue, Bin Wu, Shuofei Qiao, Sheng Wang, Rui Wang, Yiming Du, Hongru Wang, Jeff Z. Pan, Emine Yilmaz, Kam-Fai Wong, Aldo Lipani

Green & Sustainable NLP

Challenge:	Recent research empowers Large Language Models (LLMs) as multi-turn search agents to iteratively retrieve and generate outputs until complex tasks are solved.
Approach:	They propose a distill-based context refiner to dynamically mitigate context interference . they also propose RLs that refine contexts to generate outputs .
Outcome:	The proposed refiner can mitigate context interference in multi-turn search agents.

Noise-injected Consistency Training and Entropy-constrained Pseudo Labeling for Semi-supervised Extractive Summarization (2022.coling-1)

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Yiming Wang, Qianren Mao, Junnan Liu, Weifeng Jiang, Hongdong Zhu, Jianxin Li

Dataset for the First Evaluation on Chinese Machine Reading Comprehension (L18-1)

Challenge:	Existing studies on semi-supervised learning methods focus on how to effectively utilize abundant unlabeled data.
Approach:	They propose a semi-supervised consistency training method to regularize model predictions and a pseudo-labeling strategy to obtain high-confidence labels from unlabeled predictions.
Outcome:	The proposed method improves extractive summarization over an insufficient labeled dataset.

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Yiming Cui, Ting Liu, Zhipeng Chen, Wentao Ma, Shijin Wang, Guoping Hu

CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models (2024.findings-acl)

Challenge:	Existing reading comprehension datasets are mostly in English .
Approach:	They propose a Chinese reading comprehension dataset to add diversity to existing reading comprehension data . proposed dataset contains cloze-style reading comprehension and user query reading comprehension .
Outcome:	The proposed dataset is based on a Chinese reading comprehension dataset . it includes two types of cloze-style and user query reading comprehension . the proposed dataset hosted the 1st Evaluation on Chinese Machine Reading Comprehension (CMRC-2017)

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Yizhi Li, Ge Zhang, Xingwei Qu, Jiali Li, Zhaoqun Li, Noah Wang, Hao Li, Ruibin Yuan, Yinghao Ma, Kai Zhang, Wangchunshu Zhou, Yiming Liang, Lei Zhang, Lei Ma, Jiajun Zhang, Zuowen Li, Wenhao Huang, Chenghua Lin, Jie Fu

LogicGame: Benchmarking Rule-Based Reasoning Abilities of Large Language Models (2025.findings-acl)

Challenge:	a recent study shows that large language models have limited generalization in low-resource languages like Chinese.
Approach:	They propose to evaluate the zero-shot generalizability of large language models to the Chinese language . they release only half of the dataset publicly, with the remainder kept private .
Outcome:	The Chinese Instruction-Following Benchmark evaluates the generalizability of LLMs to the Chinese language.

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Jiayi Gui, Yiming Liu, Jiale Cheng, Xiaotao Gu, Xiao Liu, Hongning Wang, Yuxiao Dong, Jie Tang, Minlie Huang

A Sentence Cloze Dataset for Chinese Machine Reading Comprehension (2020.coling-main)

Challenge:	Large Language Models (LLMs) have demonstrated notable capabilities across various tasks, showcasing complex problem-solving abilities.
Approach:	They propose a benchmark to evaluate the rule-based logical reasoning capabilities of Large Language Models (LLMs) they create simulated scenarios in which models execute or plan operations to achieve specific outcomes.
Outcome:	The proposed benchmark evaluates the performance of large language models on a variety of scenarios with varying difficulty levels.

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Yiming Cui, Ting Liu, Ziqing Yang, Zhipeng Chen, Wentao Ma, Wanxiang Che, Shijin Wang, Guoping Hu

TextBrewer: An Open-Source Knowledge Distillation Toolkit for Natural Language Processing (2020.acl-demos)

Challenge:	Using cloze-style reading comprehension, Chinese machine reading comprehension datasets are becoming more and more popular . a new task is proposed to fill the right candidate sentence into the passage with several blanks .
Approach:	They propose a Chinese task to fill the right candidate sentence into a passage with blanks . they build a dataset to evaluate the difficulty of the task and make fake candidates .
Outcome:	The proposed task fills the right candidate sentence into the passage with blanks . the proposed dataset contains over 100K blanks within over 10K passages based on Chinese narrative stories .

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Ziqing Yang, Yiming Cui, Zhipeng Chen, Wanxiang Che, Ting Liu, Shijin Wang, Guoping Hu

Thinking with Map: Reinforced Parallel Map-Augmented Agent for Geolocalization (2026.findings-acl)

Challenge:	Large pre-trained language models have hundreds of millions of parameters and take several gigabytes of memory to train and inference.
Approach:	They propose an open-source knowledge distillation toolkit designed for natural language processing that provides a set of predefined distillation methods and can be extended with custom code.
Outcome:	The proposed method is comparable with or even higher than the public distilled BERT models with similar numbers of parameters.

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Yuxiang Ji, Yong Wang, Ziyu Ma, Yiming Hu, Hailang Huang, Xuecai Hu, Guanhua Chen, Liaoni Wu, Xiangxiang Chu

Visual Data in NLP

Challenge:	Existing large vision-language model (LVLM) approaches overlook a common strategy used by humans — using maps.
Approach:	They propose a method to equip a vision-language model with the ability to think with maps and optimize it using agentic reinforcement learning and parallel test-time scaling.
Outcome:	The proposed method outperforms open- and closed-source models on most metrics.

MPrompt: Exploring Multi-level Prompt Tuning for Machine Reading Comprehension (2023.findings-emnlp)

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Guoxin Chen, Yiming Qian, Bowen Wang, Liangzhi Li

M2PT: Multimodal Prompt Tuning for Zero-shot Instruction Learning (2024.emnlp-main)

Challenge:	Existing soft prompt methods focus on designing the input-independent prompts that steer the model to fit the domain of the new dataset.
Approach:	They propose a multi-level prompt tuning method that utilizes prompts at task-specific, domain-specific and context-specific levels to enhance the comprehension of input semantics.
Outcome:	The proposed method improves on 12 benchmarks on various QA formats and achieves an average improvement of 1.94% over the state-of-the-art methods.

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Taowen Wang, Yiyang Liu, James Liang, Junhan Zhao, Yiming Cui, Yuning Mao, Shaoliang Nie, Jiahao Liu, Fuli Feng, Zenglin Xu, Cheng Han, Lifu Huang, Qifan Wang, Dongfang Liu

Does ChatGPT Know That It Does Not Know? Evaluating the Black-Box Calibration of ChatGPT (2024.lrec-main)

Challenge:	Multimodal Large Language Models (MLLMs) exhibit remarkable performance across a wide range of domains.
Approach:	They propose a multimodal prompt tuning approach for efficient instruction tuning of MLLMs.
Outcome:	The proposed approach shows superior performance on multimodal evaluation datasets compared to state-of-the-art methods.

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Youliang Yuan, Wenxuan Wang, Qingshuo Guo, Yiming Xiong, Chihao Shen, Pinjia He

Exploiting Contextual Knowledge in LLMs through 𝒱-usable Information based Layer Enhancement (2025.acl-long)

Challenge:	Recent performance of ChatGPT in downstream tasks is questionable, but does it know that it does not know?
Approach:	They propose to use three types of proxy confidence to evaluate ChatGPT's black-box calibration ability.
Outcome:	The proposed model exhibits a positive correlation with accuracy in TruthfulQA and a negative correlation in the ModAr dataset.

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Xiaowei Yuan, Zhao Yang, Ziyang Huang, Yequan Wang, Siqi Fan, Yiming Ju, Jun Zhao, Kang Liu

Challenge:	Existing approaches to enhance the context-faithfulness of Large Language Models (LLMs) ignore the fundamental mechanism of how contextual information is processed within LLMs’ internal states.
Approach:	They propose a method that enhances the utilization of contextual knowledge within LLMs’ internal representations by employing V-usable information analysis.
Outcome:	The proposed method improves context-faithfulness generation in Question-Answering tasks, particularly in scenarios involving unknown or conflicting contextual knowledge.

MHGRL: An Effective Representation Learning Model for Electronic Health Records (2024.lrec-main)

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Feiyan Liu, Liangzhi Li, Xiaoli Wang, Feng Luo, Chang Liu, Jinsong Su, Yiming Qian

Live-Aid: A Large-Scale Dialogue Dataset and Benchmark for Interleaved Multi-party Interactions in Live Streaming (2026.findings-acl)

Challenge:	Effective EHR representations are key to achieving high performance in healthcare applications.
Approach:	They propose a multimodal heterogeneous graph-enhanced representation learning to learn EHR representations using medical ontology and textual notes.
Outcome:	The proposed model outperforms baseline models on two real clinical datasets in downstream tasks.

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Yiming Lei, Yize Fan, Zeming Liu, Jiaji Dong, Hui Qiu, Haitao Leng, Qingjie Liu, Kehai Chen, Tingting Gao, Yunhong Wang

ChatMusician: Understanding and Generating Music Intrinsically with LLM (2024.findings-acl)

Challenge:	Existing Multimodal Large Language Models struggle with dynamic interactions due to the scarcity of high-quality interleaved data.
Approach:	They propose a large-scale interleaved live interaction Chinese dataset with human-annotated video responses.
Outcome:	The proposed model can be used to evaluate live interactions in Chinese over 1,100 hours and 80,037 dialogue turns.

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Ruibin Yuan, Hanfeng Lin, Yi Wang, Zeyue Tian, Shangda Wu, Tianhao Shen, Ge Zhang, Yuhang Wu, Cong Liu, Ziya Zhou, Liumeng Xue, Ziyang Ma, Qin Liu, Tianyu Zheng, Yizhi Li, Yinghao Ma, Yiming Liang, Xiaowei Chi, Ruibo Liu, Zili Wang, Chenghua Lin, Qifeng Liu, Tao Jiang, Wenhao Huang, Wenhu Chen, Jie Fu, Emmanouil Benetos, Gus Xia, Roger Dannenberg, Wei Xue, Shiyin Kang, Yike Guo

Debt Collection Negotiations with Large Language Models: An Evaluation System and Optimizing Decision Making with Multi-Agent (2025.findings-acl)

Challenge:	Despite LLMs' impressive capabilities in musical knowledge, music reasoning remains an unsolved task.
Approach:	They propose an open-source large language model (LLM) that integrates intrinsic musical abilities into LLaMA2 and GPT-3.5.
Outcome:	The proposed model can understand and generate music with a pure text tokenizer without external multi-modal neural structures or tokenizers.

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Xiaofeng Wang, Zhixin Zhang, Jin Guang Zheng, Yiming Ai, Rui Wang

A Span-Extraction Dataset for Chinese Machine Reading Comprehension (D19-1)

Challenge:	Debt collection negotiations (DCN) are vital for managing non-performing loans (NPLs) prior systems lacking dynamic negotiation and real-time decision-making capabilities.
Approach:	They propose a framework for debt negotiation that incorporates planning and judging modules to improve decision rationality.
Outcome:	The proposed framework improves decision rationality and integrates planning and judging modules to improve decision rationalness.

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Yiming Cui, Ting Liu, Wanxiang Che, Li Xiao, Zhipeng Chen, Wentao Ma, Shijin Wang, Guoping Hu

Machine Reading Comprehension

Challenge:	Existing reading comprehension datasets are mostly in English . MRC is a new field of research that aims to comprehend the context of articles and answer the questions based on them.
Approach:	They propose a Span-Extraction dataset for Chinese machine reading comprehension to add language diversities to existing reading comprehension datasets.
Outcome:	The proposed dataset is composed of 20,000 real questions annotated on Wikipedia paragraphs by human experts.

Generation-driven Contrastive Self-training for Zero-shot Text Classification with Instruction-following LLM (2024.eacl-long)

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Ruohong Zhang, Yau-Shian Wang, Yiming Yang

Context-Sensitive Generation of Open-Domain Conversational Responses (C18-1)

Challenge:	a novel method to train a smaller model with LLMs for zero-shot text classification requires immense computational resources due to their substantial model size.
Approach:	They propose a method which leverages the generative power of large language models to train a smaller model.
Outcome:	The proposed method outperforms state-of-the-art methods when limited data is available.

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Weinan Zhang, Yiming Cui, Yifa Wang, Qingfu Zhu, Lingzhi Li, Lianqiang Zhou, Ting Liu

Understanding the Dark Side of LLMs’ Intrinsic Self-Correction (2025.acl-long)

Challenge:	Existing studies on single-turn conversation generation focus on coherence and context-sensitive generation of open-domain conversational responses.
Approach:	They propose static and dynamic attention based approaches for context-sensitive generation of open-domain conversational responses.
Outcome:	The proposed model outperforms all baselines on automatic and human evaluation on two public datasets.

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Qingjie Zhang, Di Wang, Haoting Qian, Yiming Li, Tianwei Zhang, Minlie Huang, Ke Xu, Hewu Li, Liu Yan, Han Qiu

Challenge:	Recent studies show that LLMs’ intrinsic self-correction fails without oracle labels as feedback.
Approach:	They propose to use one simple task and three complex tasks with state-of-the-art LLMs like ChatGPT, Llama, and DeepSeek to interpret LLM's intrinsic self-correction.
Outcome:	The proposed methods reveal the dark side of LLMs’ intrinsic self-correction for different tasks, especially for those failure cases.

EventWeave: A Dynamic Framework for Capturing Core and Supporting Events in Dialogue Systems (2026.acl-long)

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Zhengyi Zhao, Shubo Zhang, Yiming Du, Bin Liang, Baojun Wang, Zhongyang Li, Binyang Li, Kam-Fai Wong

Can MLLMs Understand the Deep Implication Behind Chinese Images? (2025.acl-long)

Challenge:	Existing dialogue systems process conversational turns in isolation, overlooking event structures that guide natural interactions.
Approach:	They propose a framework that explicitly models relationships between conversational events to generate more contextually appropriate dialogue responses.
Outcome:	Experiments on three dialogue datasets show that the proposed approach produces more natural responses while requiring less computational overhead.

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Chenhao Zhang, Xi Feng, Yuelin Bai, Xeron Du, Jinchang Hou, Kaixin Deng, Guangzeng Han, Qinrui Li, Bingli Wang, Jiaheng Liu, Xingwei Qu, Yifei Zhang, Qixuan Zhao, Yiming Liang, Ziqiang Liu, Feiteng Fang, Min Yang, Wenhao Huang, Chenghua Lin, Ge Zhang, Shiwen Ni

Is Graph Structure Necessary for Multi-hop Question Answering? (2020.emnlp-main)

Challenge:	MLLMs perform poorly on traditional culture images, indicating limitations in understanding high-level semantics and lacking a deep knowledge base of Chinese traditional culture.
Approach:	They propose to use Chinese images to assess MLLMs' higher-order perception and understanding of Chinese visual content.
Outcome:	The proposed model incorporates images that represent Chinese traditional culture, such as famous Chinese traditional paintings, to ensure the authenticity of the Chinese context.

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Nan Shao, Yiming Cui, Ting Liu, Shijin Wang, Guoping Hu

Structured Data in NLP

Challenge:	Existing studies focus on multi-hop question answering across multiple documents or paragraphs.
Approach:	They propose a graph neural network to deal with graph structure in textual multi-hop reasoning . they propose 'self-attention' and propose removing entire graph structure may not hurt the final results .
Outcome:	The proposed model shows that graph-attention or the entire graph structure can be replaced by self-attention . hotpotQA is a widely used benchmark for multi-hop question answering .

Revisiting the Knowledge Injection Frameworks (2023.emnlp-main)

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Peng Fu, Yiming Zhang, Haobo Wang, Weikang Qiu, Junbo Zhao

CasEE: A Joint Learning Framework with Cascade Decoding for Overlapping Event Extraction (2021.findings-acl)

Challenge:	Injecting unaligned knowledge tuple into large language models achieves comparable (and sometimes better) results than aligned knowledge.
Approach:	They propose a technique to inject random knowledge into large language models to improve performance.
Outcome:	The proposed technique overcomes the sanity problem and pushes the performance limit.

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Jiawei Sheng, Shu Guo, Bowen Yu, Qian Li, Yiming Hei, Lihong Wang, Tingwen Liu, Hongbo Xu

Revisiting Pre-Trained Models for Chinese Natural Language Processing (2020.findings-emnlp)

Challenge:	Existing methods assume that events appear in sentences without overlaps . overlapping event extraction is a challenging task in natural language understanding .
Approach:	They propose a joint learning framework with cascade decoding for overlapping event extraction . they sequentially perform type detection, trigger extraction and argument extraction based on the specific former prediction .
Outcome:	The proposed framework improves on a public event extraction benchmark . it sequentially performs type detection, trigger extraction and argument extraction .

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Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Shijin Wang, Guoping Hu

Chart2Code53: A Large-Scale Diverse and Complex Dataset for Enhancing Chart-to-Code Generation (2025.emnlp-main)

Challenge:	Existing pre-trained language models have shown tremendous improvements across various NLP tasks.
Approach:	They propose to revisit Chinese pre-trained language models to examine their effectiveness in a non-English language and release the Chinese pretrained model series to the community.
Outcome:	The proposed model improves on RoBERTa in several ways, especially the masking strategy that adopts MLM as correction (Mac).

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Tianhao Niu, Yiming Cui, Baoxin Wang, Xiao Xu, Xin Yao, Qingfu Zhu, Dayong Wu, Shijin Wang, Wanxiang Che

Programming Languages in NLP

Challenge:	Existing Chart2code-related training datasets suffer from limited scale, limited type coverage, and inadequate complexity.
Approach:	They propose to synthesize chart2code-related training datasets using web plotting code and chart images to address these challenges.
Outcome:	The proposed dataset exhibits the greatest diversity and higher complexity compared to other open-source Chart2code related datasets.

CoEvolve: Training LLM Agents via Agent-Data Mutual Evolution (2026.acl-long)

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Shidong Yang, Ziyu Ma, Tongwen Huang, Yiming Hu, Yong Wang, Xiangxiang Chu

Challenge:	Extensive experiments on AppWorld and BFCL demonstrate consistent and significant improvements over strong base models, yielding absolute gains of 19.43%, 15.58%, and 18.14%, respectively.
Approach:	They propose a framework that extracts feedback signals such as forgetting and uncertainty from rollout trajectories and utilizes them to guide LLM-based task synthesis.
Outcome:	Extensive experiments on AppWorld and BFCL show that the proposed framework improves over strong base models.

Self-Evolving GPT: A Lifelong Autonomous Experiential Learner (2024.acl-long)

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Jinglong Gao, Xiao Ding, Yiming Cui, Jianbai Zhao, Hepeng Wang, Ting Liu, Bing Qin

Proxy-Driven Robust Multimodal Sentiment Analysis with Incomplete Data (2025.acl-long)

Challenge:	Existing approaches to provide LLMs with textual task-solving experience rely on manual efforts to acquire and apply such experience for each task.
Approach:	They propose a lifelong autonomous experiential learning framework based on LLMs that learns and accumulates experience through experience transfer and induction.
Outcome:	The proposed framework performs reliably in each intermediate step and improves GPT-3.5 and GPT-4 on widely used NLP datasets.

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Aoqiang Zhu, Min Hu, Xiaohua Wang, Jiaoyun Yang, Yiming Tang, Ning An

Robustness in NLP

Challenge:	Existing studies focus on optimizing model structures to handle uncertain missingness, but models still face challenges when dealing with uncertain missing data.
Approach:	They propose a data-centric robust multimodal sentiment analysis method, Proxy-Driven Robust Multimodal Fusion, which maps unimodal data to the latent space of Gaussian distributions to capture core features and structure.
Outcome:	The proposed method outperforms existing models in noise resistance and achieves state-of-the-art performance on multiple benchmark datasets.

Can LLMs Act as Historians? Evaluating Historical Research Capabilities of LLMs via the Chinese Imperial Examination (2026.acl-long)

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Lirong Gao, Zeqing Wang, Yuyan Cai, Jiayi Deng, Yanmei Gu, Yiming Zhang, Jia Zhou, Yanfei Zhang, Junbo Zhao

COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values (2026.findings-eacl)

Challenge:	Existing benchmarks assess basic knowledge breadth or lexical understanding, failing to capture higher-order skills that are central to historical research.
Approach:	They propose a benchmark anchored in the Chinese Imperial Examination system that assesses historical knowledge and lexical understanding.
Outcome:	The new benchmark aims to assess the ability of LLMs to process historical materials and documents.

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Siwei Wu, JinCheng Ren, Xeron Du, Shuyue Guo, Xingwei Qu, Yiming Liang, Jie Liu, Yunwen Li, Tyler Loakman, Tianyu Zheng, Boyu Feng, Huaqing Yuan, Zili Wang, Jiaheng Liu, Wenhao Huang, Chenglin Cai, Haoran Que, Jian Yang, Yuelin Bai, Zekun Moore Wang, Zhouliang Yu, Qunshu Lin, Ding Pan, Yuchen Eleanor Jiang, Tiannan Wang, Wangchunshu Zhou, Shenzhi Wang, Xingyuan Bu, Minghao Liu, Guoyin Wang, Ge Zhang, Chenghua Lin

Long-tailed Extreme Multi-label Text Classification by the Retrieval of Generated Pseudo Label Descriptions (2023.findings-eacl)

Challenge:	Existing Chinese preference datasets suffer from limited scale, restricted domain coverage, and insufficiently rigorous data validation.
Approach:	They propose an LLM-based data annotation pipeline with no human intervention to annotate Chinese preference datasets.
Outcome:	The proposed pipeline outperforms existing Chinese preference datasets on AlignBench and Chinese Reward Benchmark.

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Ruohong Zhang, Yau-Shian Wang, Yiming Yang, Donghan Yu, Tom Vu, Likun Lei

Information Retrieval

Challenge:	Extreme Multi-label text classification (XMTC) is a tough challenge due to the sheer size of the label spaces and the severe data scarcity problem associated with the long tail of rare labels in highly skewed distributions.
Approach:	They propose to use a trained bag-of-words classifier to generate pseudo label descriptions from a training bag- of-word classifier.
Outcome:	The proposed approach outperforms the existing models in the tail label prediction problem and achieves state-of-the-art (SOTA) performance on XMTC benchmark datasets.

D-NET: A Pre-Training and Fine-Tuning Framework for Improving the Generalization of Machine Reading Comprehension (D19-58)

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Hongyu Li, Xiyuan Zhang, Yibing Liu, Yiming Zhang, Quan Wang, Xiangyang Zhou, Jing Liu, Hua Wu, Haifeng Wang

Translationese-index: Using Likelihood Ratios for Graded and Generalizable Measurement of Translationese (2025.emnlp-main)

Challenge:	MRC requires machines to understand text and answer questions about the text.
Approach:	They propose a simple system Baidu submitted for MRQA 2019 Shared Task that focused on generalization of machine reading comprehension (MRC) models.
Outcome:	The proposed system is ranked at top 1 of all participants in terms of averaged F1 score.

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Yikang Liu, Wanyang Zhang, Yiming Wang, Jialong Tang, Pei Zhang, Baosong Yang, Fei Huang, Rui Wang, Hai Hu

Machine Translation

Challenge:	Translationese is a linguistic property that is often introduced in the translation process that is different from those of original texts.
Approach:	They propose to use synthesized translations and translations in the wild to evaluate T-index's generalizability in cross-domain settings and its validity against human judgments.
Outcome:	The proposed measure can generalize to unseen genres, authors, and language pairs.

MIND: Towards Immersive Psychological Healing with Multi-Agent Inner Dialogue (2025.findings-emnlp)

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Yujia Chen, Changsong Li, Yiming Wang, Tianjie Ju, Qingqing Xiao, Nan Zhang, Zifan Kong, Peng Wang, Binyu Yan

R-Judge: Benchmarking Safety Risk Awareness for LLM Agents (2024.findings-emnlp)

Challenge:	Mental health issues are worsening in today’s competitive society, such as depression and anxiety.
Approach:	They propose a multi-agent inner dialogue paradigm that provides more immersive psychological healing environments.
Outcome:	The proposed paradigm provides more immersive psychological healing environments.

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Tongxin Yuan, Zhiwei He, Lingzhong Dong, Yiming Wang, Ruijie Zhao, Tian Xia, Lizhen Xu, Binglin Zhou, Fangqi Li, Zhuosheng Zhang, Rui Wang, Gongshen Liu

Challenge:	Large language models (LLMs) have shown compelling abilities in reasoning, decision-making, and instruction following.
Approach:	They propose a benchmark to evaluate the proficiency of large language models (LLMs) in judging and identifying safety risks given agent interaction records.
Outcome:	The proposed model outperforms the best-performing model, GPT-4o, while no other models significantly exceed the random.

Fast Adaptation via Prompted Data: An Efficient Cross-Domain Fine-tuning Method for Large Language Models (2024.lrec-main)

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Yiming Zhang, Hantao Yang, Haobo Wang, Jake Zhao

DaNet: Dual-Aware Enhanced Alignment Network for Multimodal Aspect-Based Sentiment Analysis (2025.findings-acl)

Challenge:	Large language models (LLMs) have been successful in a variety of natural language understanding tasks, but domain discrepancies between the downstream task and the pre-training corpora may have hindered LLMs to excel further in the vertical applications.
Approach:	They propose a Fast Adaptation method for LLMs via Prompted Data that integrates downstream text corpora, gold labels and external knowledge sources into a highly controllable prompt.
Outcome:	The proposed method bridges the gap between the downstream task and the pre-training corpora and integrates downstream text corpors, gold labels and external knowledge sources into a highly controllable prompt.

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Aoqiang Zhu, Min Hu, Xiaohua Wang, Jiaoyun Yang, Yiming Tang, Ning An

Sentiment Analysis

Challenge:	Existing methods assume a direct alignment between images and aspects, matching the entire image with a corresponding aspect. Existing algorithms assume 'direct alignment' between images, introducing noise.
Approach:	They propose a Dual-Aware Enhanced Alignment Network (DaNet) that can enhance fine-grained multimodal aspect-image alignment and denoising.
Outcome:	The proposed system outperforms existing methods in three subtasks and is available on https://github.com/***/DaNet.

Analyzing and Evaluating Faithfulness in Dialogue Summarization (2022.emnlp-main)

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Bin Wang, Chen Zhang, Yan Zhang, Yiming Chen, Haizhou Li

TCRA-LLM: Token Compression Retrieval Augmented Large Language Model for Inference Cost Reduction (2023.findings-emnlp)

Challenge:	Existing studies on faithfulness of text summarization have not been conducted on abstractive summarizing.
Approach:	They propose a method to evaluate faithfulness of dialogue summarization models by multi-choice questions.
Outcome:	The proposed method can facilitate the development of dialogue summarization systems.

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Junyi Liu, Liangzhi Li, Tong Xiang, Bowen Wang, Yiming Qian

Flexibly Utilize Memory for Long-Term Conversation via a Fragment-then-Compose Framework (2025.emnlp-main)

Challenge:	ChatGPT and GPT-4 are commercial large language models (LLMs) however, they may produce vague responses or incorrect answers in certain specialized domains.
Approach:	They propose a token compression scheme that uses summarization and semantic compression to reduce the token size of LLMs.
Outcome:	The proposed method reduces token size by doing summarization and semantic compression while reducing token size with only 1.6% accuracy drop.

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Cai Ke, Yiming Du, Bin Liang, Yifan Xiang, Lin Gui, Zhongyang Li, Baojun Wang, Yue Yu, Hui Wang, Kam-Fai Wong, Ruifeng Xu

Benchmarking Robustness of Machine Reading Comprehension Models (2021.findings-acl)

Challenge:	Large language models extract useful information from conversation history to enhance the response in long-term conversations.
Approach:	They propose a Fragment-then-Compose framework to optimize memory utilization for long-term open-domain conversation.
Outcome:	The proposed framework can be used to extract useful information from conversation history . it can be adapted to different situations and improve response generation .

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Chenglei Si, Ziqing Yang, Yiming Cui, Wentao Ma, Ting Liu, Shijin Wang

Robustness in NLP

Challenge:	Existing benchmarks only evaluate models' robustness under test-time perturbations or adversarial attacks.
Approach:	They propose a model-agnostic benchmark to evaluate models' robustness under adversarial attacks.
Outcome:	The proposed model-agnostic benchmark evaluates models under four different types of adversarial attacks.

SynthAgent: Adapting Web Agents with Synthetic Supervision (2026.acl-long)

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Zhaoyang Wang, Yiming Liang, Xuchao Zhang, Qianhui Wu, Siwei Han, Anson Bastos, Rujia Wang, Chetan Bansal, Baolin Peng, Jianfeng Gao, Saravan Rajmohan, Huaxiu Yao

Autonomous Workflow for Multimodal Fine-Grained Training Assistants Towards Mixed Reality (2024.findings-acl)

Challenge:	Existing studies have focused on synthetic supervision but have encountered data quality issues.
Approach:	They propose a fully synthetic supervision framework that aims at improving data quality via dual refinement of both tasks and trajectories.
Outcome:	The proposed framework outperforms existing methods on standardized benchmarks and shows promising results on a standardized test.

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Jiahuan Pei, Irene Viola, Haochen Huang, Junxiao Wang, Moonisa Ahsan, Fanghua Ye, Jiang Yiming, Yao Sai, Di Wang, Zhumin Chen, Pengjie Ren, Pablo Cesar

Aligning Large Multimodal Models with Factually Augmented RLHF (2024.findings-acl)

Challenge:	a fine-grained, comprehensive understanding of multimodal environments remains under-explored.
Approach:	They propose an automated workflow for integrating AI agents into extended reality (XR) they propose a cerebral language agent that integrates LLM with memory, planning, and interaction with XR tools and a vision-language agent .
Outcome:	The proposed workflow integrates AI agents seamlessly into extended reality (XR) applications for fine-grained training.

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Zhiqing Sun, Sheng Shen, Shengcao Cao, Haotian Liu, Chunyuan Li, Yikang Shen, Chuang Gan, Liangyan Gui, Yu-Xiong Wang, Yiming Yang, Kurt Keutzer, Trevor Darrell

GaLa: Hypergraph-Guided Visual Language Models for Procedural Planning (2026.findings-acl)

Challenge:	Large Multimodal Models (LMMs) are built across modalities and the misalignment between two modality can result in "hallucination" . developing LMMs faces challenges such as a lack of data and a limited number of data sets.
Approach:	They propose a new algorithm that augments the reward model with additional factual information such as image captions and ground-truth multi-choice options.
Outcome:	The proposed approach improves on the LLaVA-Bench dataset with the 96% performance level of the text-only GPT-4 and an improvement of 60% on MMHAL-BENCH over other baselines.

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Kun Wang, Li Yiming, Mingcheng Qu, Aqiang Zhang, Guang Yang, Tonghua Su

Conversational Word Embedding for Retrieval-Based Dialog System (2020.acl-main)

Challenge:	Existing methods for procedural planning over-rely on visual inputs and lack structured semantic information.
Approach:	They propose a vision–language framework for multimodal procedural planning that exploits implicit spatial relations and deep semantics encoded in object attributes.
Outcome:	The proposed framework outperforms existing methods in terms of execution success rate, LCS, and planning correctness.

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Wentao Ma, Yiming Cui, Ting Liu, Dong Wang, Shijin Wang, Guoping Hu

COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning (2025.findings-naacl)

Challenge:	Existing word embedding methods for retrieval-based dialog systems are based on co-occurrence statistics and train them based upon the same co-existence statistics.
Approach:	They propose a conversational word embedding method which uses the conversation pairs post, reply, and 'reply' they introduce a word alignment model from statistical machine translation and train it on word-level and sentence-level.
Outcome:	The proposed method improves the quality of the selected response on retrieval-based dialog systems.

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Yuelin Bai, Xeron Du, Yiming Liang, Leo Jin, Junting Zhou, Ziqiang Liu, Feiteng Fang, Mingshan Chang, Tianyu Zheng, Xincheng Zhang, Nuo Ma, Zekun Moore Wang, Ruibin Yuan, Haihong Wu, Hongquan Lin, Wenhao Huang, Jiajun Zhang, Chenghua Lin, Jie Fu, Min Yang, Shiwen Ni, Ge Zhang

AlignSum: Data Pyramid Hierarchical Fine-tuning for Aligning with Human Summarization Preference (2024.findings-emnlp)

Challenge:	Existing datasets for Chinese instruction tuning are not well-aligned with Chinese users’ interaction patterns.
Approach:	They propose to use Chinese instruction tuning datasets to improve instruction fine-tuning for Chinese users.
Outcome:	The proposed dataset shows that Chinese models achieve competitive performance in diverse benchmarks.

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Yang Han, Yiming Wang, Rui Wang, Lu Chen, Kai Yu

KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering (2022.acl-long)

Challenge:	Text summarization tasks employ Pre-trained Language Models (PLMs) to fit diverse datasets.
Approach:	They propose a human summarization preference alignment framework to align PLMs with human preferences.
Outcome:	The proposed framework narrows the gap between automatic and human evaluations by integrating three components.

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Donghan Yu, Chenguang Zhu, Yuwei Fang, Wenhao Yu, Shuohang Wang, Yichong Xu, Xiang Ren, Yiming Yang, Michael Zeng

BLEURT Has Universal Translations: An Analysis of Automatic Metrics by Minimum Risk Training (2023.acl-long)

Challenge:	Open-Domain Question Answering (ODQA) models typically include a retrieving module and a reading module.
Approach:	They propose a new open-domain question-answering framework that uses a knowledge-enhanced version of FiD to improve the approach.
Outcome:	The proposed model improves on ODQA benchmark datasets with less than 40% computation cost.

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Yiming Yan, Tao Wang, Chengqi Zhao, Shujian Huang, Jiajun Chen, Mingxuan Wang

Text Generation

Challenge:	Automated metrics are used for machine translation, but they are often considered to be black boxes with potential biases that are difficult to detect.
Approach:	They analyze automatic metrics from the perspective of their guidance for machine translation training.
Outcome:	The proposed measures improve the performance of machine translation models.

Element-aware Summarization with Large Language Models: Expert-aligned Evaluation and Chain-of-Thought Method (2023.acl-long)

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Yiming Wang, Zhuosheng Zhang, Rui Wang

OpenRLHF: A Ray-based Easy-to-use, Scalable and High-performance RLHF Framework (2025.emnlp-demos)

Challenge:	Experimental results show that automatic summarization generates concise summaries that contain key ideas of source documents.
Approach:	They propose to use Element-aware test sets to annotate news-related reference summaries to focus on more fine-grained news elements objectively and comprehensively.
Outcome:	The proposed method outperforms state-of-the-art fine-tuned PLMs and zero-shot LLMs by +4.33/+4.77 on the two datasets, respectively.

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Jian Hu, Xibin Wu, Wei Shen, Jason Klein Liu, Weixun Wang, Songlin Jiang, Haoran Wang, Hao Chen, Bin Chen, Wenkai Fang, null Xianyu, Yu Cao, Haotian Xu, Yiming Liu

Automated Tone Transcription and Clustering with Tone2Vec (2024.findings-emnlp)

Challenge:	Existing RLHF frameworks face inference bottlenecks and complexity barriers restricting their accessibility for newcomers.
Approach:	They propose an open-source RLHF framework that can be used to train large language models.
Outcome:	The proposed framework achieves superior training efficiency with speedups ranging from 1.22 to 1.68 across different model sizes compared to state-of-the-art frameworks, while requiring significantly fewer lines of code for implementation.

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Yi Yang, Yiming Wang, ZhiQiang Tang, Jiahong Yuan

Text Clustering

Challenge:	Lexical tones play a crucial role in Sino-Tibetan languages, but current phonetic fieldwork relies on manual effort.
Approach:	They propose a pitch-based similarity representations for tone transcription called Tone2Vec . they propose an open-source package that facilitates automated fieldwork and analysis .
Outcome:	Experiments on dialect clustering and variance show that Tone2Vec captures fine-grained tone variation.

Transformer-based Speech Model Learns Well as Infants and Encodes Abstractions through Exemplars in the Poverty of the Stimulus Environment (2025.coling-main)

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Yi Yang, Yiming Wang, Jiahong Yuan