Papers by Lei Sun

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

Challenge:	Existing benchmarks focus more on end-to-end performance, but neglect the underlying principles of knowledge acquisition and generalization.
Approach:	They propose a benchmark specifically designed to explore the problem-solving principles by decomposing 6.5K visual math problems into 10.9K step-level questions for evaluation.
Outcome:	The proposed benchmark covers 6.5K visual math problems and 10.9K step-level questions spanning 5 layers of knowledge granularity and 67 hierarchical knowledge concepts.

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

FuxiTranyu: A Multilingual Large Language Model Trained with Balanced Data (2024.emnlp-industry)

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

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Haoran Sun, Renren Jin, Shaoyang Xu, Leiyu Pan, null Supryadi, Menglong Cui, Jiangcun Du, Yikun Lei, Lei Yang, Ling Shi, Juesi Xiao, Shaolin Zhu, Deyi Xiong

Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification (2021.emnlp-main)

Challenge:	Large language models exhibit significant performance discrepancies between high- and low-resource languages.
Approach:	They present an open-source multilingual LLM with 8 billion parameters and a multilingual instruction dataset.
Outcome:	The proposed model achieves consistent multilingual representations across languages.

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Shuhuai Ren, Jinchao Zhang, Lei Li, Xu Sun, Jie Zhou

IEPile: Unearthing Large Scale Schema-Conditioned Information Extraction Corpus (2024.acl-short)

Challenge:	Data augmentation aims to alleviate the overfitting issue in low-resource or class-imbalanced situations.
Approach:	They propose a framework called Text AutoAugment to enhance training samples . they use a Bayesian optimization algorithm to search for the best policy .
Outcome:	The proposed framework outperforms baseline methods on six benchmark datasets.

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Honghao Gui, Lin Yuan, Hongbin Ye, Ningyu Zhang, Mengshu Sun, Lei Liang, Huajun Chen

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

Challenge:	Large Language Models exhibit a significant performance gap in Information Extraction (IE) high-quality instruction data is the vital key for enhancing LLMs' specific capabilities .
Approach:	They propose a bilingual (English and Chinese) IE instruction corpus that contains 0.32B tokens.
Outcome:	The proposed model improves the performance of LLMs for IE with zero-shot generalization.

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

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

CCSRD: Content-Centric Speech Representation Disentanglement Learning for End-to-End Speech Translation (2023.findings-emnlp)

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Xiaohu Zhao, Haoran Sun, Yikun Lei, Shaolin Zhu, Deyi Xiong

Rethinking Smoothness for Fast and Adaptable Entity Alignment Decoding (2025.findings-naacl)

Challenge:	Existing speech-to-text translation models can extract features from speech inputs, but they may include non-linguistic speech factors such as pitch, timbre and speaker identity.
Approach:	They propose a content-centric speech representation disentanglement learning framework for speech translation that decomposes speech representations into content representations and non-linguistic representations via representation disentanglement learning.
Outcome:	The proposed framework outperforms state-of-the-art speech translation models and cascaded models on five translation directions.

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Yuanyi Wang, Han Li, Haifeng Sun, Lei Zhang, Bo He, Wei Tang, Tianhao Yan, Qi Qi, Jingyu Wang

MentalSeek-Dx: Towards Progressive Hypothetico-Deductive Reasoning for Real-world Psychiatric Diagnosis (2026.acl-long)

Challenge:	Existing methods for integrating knowledge graphs rely on entity and relation embeddings . Fig. 1 shows how to decode knowledge graph in under 6 seconds .
Approach:	They propose a framework that only utilizes entity embeddings to decode knowledge graphs.
Outcome:	The proposed framework reconstructs KG representation by maximizing smoothness of entity embeddings.

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Xiao Sun, null Ymyang, Xinyi Jiang, Yu Tian, Junnan Zhu, Jiang Zhong, Qin Lei, Jingwang Huang, Haoyang Zeng, Xinyu Zhou, Xin Xiao, Kaiwen Wei

Reasoning

Challenge:	Mental health disorders represent a burgeoning global public health challenge . lack of ecological validity and fine-grained diagnostic supervision limits their utility .
Approach:	They propose a medical-specialized LLM trained to internalize clinical reasoning process through supervised trajectory construction and curriculum-based reinforcement learning.
Outcome:	The proposed model achieves state-of-the-art with only 14B parameters, establishing a clinically grounded framework for reliable psychiatric diagnosis.

Chart-MRAG: Benchmarking Multimodal Retrieval Augmented Generation on Chart-based Documents (2026.acl-long)

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null Ymyang, Jiang Zhong, Li Jin, Xiao Sun, Jingwang Huang, null Gaojinpeng, Qing Liu, Yang Bai, Jingyuan Zhang, Rui Jiang, Qin Lei, Kaiwen Wei

WaterBench: Towards Holistic Evaluation of Watermarks for Large Language Models (2024.acl-long)

Challenge:	Existing benchmarks focus on simple image-text interactions, overlooking complex visual formats like charts.
Approach:	They propose a semi-automatic framework for generating evaluation samples through multi-modal keypoint extraction, knowledge graph construction, and qa pair synthesis.
Outcome:	The proposed framework generates 4,738 question-answering pairs across 8 domains from real-world documents.

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Shangqing Tu, Yuliang Sun, Yushi Bai, Jifan Yu, Lei Hou, Juanzi Li

Cross-Modal Commentator: Automatic Machine Commenting Based on Cross-Modal Information (P19-1)

Challenge:	Recent studies have developed watermarking algorithms which restrict the generation process to leave an invisible trace for watermark detection.
Approach:	They propose a benchmarking procedure that compares different methods to ensure consistent watermarking strength and jointly evaluates their generation and detection performance.
Outcome:	The proposed benchmark compares 4 open-source watermarks on 2 LLMs under 2 watermarking strengths and observes the common struggles for current methods on maintaining the generation quality.

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Pengcheng Yang, Zhihan Zhang, Fuli Luo, Lei Li, Chengyang Huang, Xu Sun

Revealing Personality Traits: A New Benchmark Dataset for Explainable Personality Recognition on Dialogues (2024.emnlp-main)

Challenge:	Existing work on commenting based on textual content is focused on other modalities, such as graphics and images.
Approach:	They propose a task to integrate multiple modalities into automatic commenting . they construct a large-scale dataset and propose 'co-attention' model to capture dependency between textual and visual information.
Outcome:	The proposed model can achieve better performance than baselines.

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Lei Sun, Jinming Zhao, Qin Jin

Dialogue Systems & Conversational Agents

Challenge:	Current research treats personality recognition as a classification task, failing to reveal the supporting evidence for the recognized personality.
Approach:	They propose a task that aims to reveal the reasoning process as supporting evidence of the personality trait.
Outcome:	The proposed task reveals the reasoning process as supporting evidence of the personality trait.

ZoFia: Zero-Shot Fake News Detection with Entity-Guided Retrieval and Multi-LLM Interaction (2026.findings-acl)

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Lvhua Wu, Xuefeng Jiang, Sheng Sun, Yan Lei, Tian Wen, Yuwei Wang, Min Liu

SKA-Bench: A Fine-Grained Benchmark for Evaluating Structured Knowledge Understanding of LLMs (2025.findings-emnlp)

Challenge:	Large language models (LLMs) are limited by knowledge cutoff and can generate factual hallucinations when handling time-sensitive news.
Approach:	They propose a two-stage zero-shot fake news detection framework that uses a hierarchical salience and saliency-calibrated minimum margin of relevance algorithm to extract core entities accurately.
Outcome:	The proposed framework outperforms existing zero-shot baselines and even most few-shot methods on two public datasets.

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Zhiqiang Liu, Enpei Niu, Yin Hua, Mengshu Sun, Lei Liang, Huajun Chen, Wen Zhang

Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning (D18-1)

Challenge:	Existing evaluations for Structured Knowledge (SK) understanding are non-rigorous and focus on a single type of SK.
Approach:	They propose a structured knowledge understanding benchmark that includes four widely used structured knowledge forms.
Outcome:	The proposed benchmark is based on four widely used structured knowledge forms . it includes a question, an answer, positive knowledge units, and noisy knowledge units .

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Chen Shi, Qi Chen, Lei Sha, Sujian Li, Xu Sun, Houfeng Wang, Lintao Zhang

Dialogue Systems & Conversational Agents

Challenge:	Existing dialog datasets rely on human labeling, which is expensive, limited in size, and in low coverage.
Approach:	They propose a framework to automatically cluster dialogue intents and slots . they collect context features, leverage an autoencoder for feature assembly, and adapt a dynamic hierarchical clustering method for intent and slot labeling.
Outcome:	The proposed framework can promote human labeling cost to a great extent and achieve good intent clustering accuracy (84.1%) it also provides reasonable and instructive slot labeling results.

Enhancing Topic-to-Essay Generation with External Commonsense Knowledge (P19-1)

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Pengcheng Yang, Lei Li, Fuli Luo, Tianyu Liu, Xu Sun

Reasoning

Challenge:	Existing methods for topic-to-essay generation are insufficient for generating novel, diverse, and topic-consistent paragraph-level text with a set of topics.
Approach:	They propose to integrate commonsense from external knowledge base into the generator through dynamic memory mechanism and adversarial training to further improve topic-consistency.
Outcome:	The proposed task is more novel, diverse, and topic-consistent than existing methods in terms of both automatic and human evaluation.

ShieldLM: Empowering LLMs as Aligned, Customizable and Explainable Safety Detectors (2024.findings-emnlp)

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Zhexin Zhang, Yida Lu, Jingyuan Ma, Di Zhang, Rui Li, Pei Ke, Hao Sun, Lei Sha, Zhifang Sui, Hongning Wang, Minlie Huang

ConKI: Contrastive Knowledge Injection for Multimodal Sentiment Analysis (2023.findings-acl)

Challenge:	Existing tools for detecting safety issues in LLMs are expensive and inefficient.
Approach:	They propose an LLM-based safety detector which annotates the safety of queries and provides explanations for its decisions.
Outcome:	The proposed detector outperforms baselines on four sets of query-response pairs and is effective as a safety evaluator for advanced LLMs.

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Yakun Yu, Mingjun Zhao, Shi-ang Qi, Feiran Sun, Baoxun Wang, Weidong Guo, Xiaoli Wang, Lei Yang, Di Niu

Collaboration of Fusion and Independence: Hypercomplex-driven Robust Multi-Modal Knowledge Graph Completion (2026.acl-long)

Challenge:	Existing methods for multimodal sentiment analysis focus on general knowledge, which is inadequate to identify specific sentiments across modalities.
Approach:	They propose a method where specific-knowledge representations for each modality can be learned together with general knowledge representations via knowledge injection based on an adapter architecture.
Outcome:	The proposed method outperforms all prior methods on three popular benchmarks on multimodal sentiment analysis metrics.

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Zhiqiang Liu, Yichi Zhang, Mengshu Sun, Lei Liang, Wen Zhang

MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)

Challenge:	Existing multi-modal knowledge graphs lack modality-specific information and are limited in their ability to capture nuanced semantic interplay between modalities.
Approach:	They propose a multi-modal knowledge graph completion method which integrates both paradigms . they use a fine-grained Entity Representation Factorization module and a Robust Relation-aware Modality Fusion module to obtain robust representations for three independent modalities and one fused modality.
Outcome:	The proposed method achieves coexistence and collaboration of fused and independent modality representations while maintaining modality-specific information.

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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He

Green & Sustainable NLP

Challenge:	Document images are characterized by higher resolutions, denser content, and more complex structural layouts.
Approach:	They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition.
Outcome:	The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead.

ERRV: Eliciting Efficient Reasoning through Reasoning Vectors for Policy Optimization in Large Language Models (2026.findings-acl)

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Zhuowen Han, Lei Yang, Renren Jin, Dan Shi, Chenxi Sun, Deyi Xiong

CSMCIR: CoT-Enhanced Symmetric Alignment with Memory Bank for Composed Image Retrieval (2026.findings-acl)

Challenge:	Existing efforts to improve reasoning efficiency of large language models focus on modifying the reinforcement learning reward, such as adding length penalties.
Approach:	They propose a training framework that elicits efficient reasoning through reasoning vectors and a framework that allows the model to generate high-quality responses during reinforcement learning.
Outcome:	The proposed framework reduces reasoning length by 30% while maintaining stability, while retaining high accuracy.

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Zhipeng Qian, Zihan Liang, Yufei Ma, Ben Chen, Huangyu Dai, Yiwei Ma, Jiayi Ji, Chenyi Lei, Han Li, Xiaoshuai Sun

UniRE: A Unified Label Space for Entity Relation Extraction (2021.acl-long)

Challenge:	Existing approaches to search for images using single-modality are limited by representation space fragmentation.
Approach:	They propose a unified representation framework that achieves efficient query-target alignment . they introduce a multi-level Chain-of-Thought prompting strategy that guides MLMs to generate discriminative, semantically compatible captions for target images .
Outcome:	The proposed framework achieves efficient query-target alignment through synergistic components.

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Yijun Wang, Changzhi Sun, Yuanbin Wu, Hao Zhou, Lei Li, Junchi Yan

Towards a Deep Understanding of Multilingual End-to-End Speech Translation (2023.findings-emnlp)

Challenge:	Existing joint entity relation extraction models setup two separate label spaces for the two sub-tasks .
Approach:	They propose to eliminate the different treatment on the two sub-tasks’ label spaces by applying a unified classifier to predict each cell’s label.
Outcome:	The proposed model achieves competitive accuracy with the best extractor and is faster.

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Haoran Sun, Xiaohu Zhao, Yikun Lei, Shaolin Zhu, Deyi Xiong

E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning (2022.findings-acl)

Challenge:	Recent years have witnessed the rapid development of end-to-end speech-totext translation (ST) which has demonstrated remarkable performance and outperformed conventional cascaded systems.
Approach:	They employ Singular Value Canonical Correlation Analysis to analyze representations learnt in a multilingual end-to-end speech translation model trained over 22 languages.
Outcome:	The proposed approach outperforms existing cascaded systems in predicting phonetic features and improves translation quality.

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Jiangjie Chen, Rui Xu, Ziquan Fu, Wei Shi, Zhongqiao Li, Xinbo Zhang, Changzhi Sun, Lei Li, Yanghua Xiao, Hao Zhou

Know the Known and the Unknown: Reasonable Answer Generation with Knowledge-Informed Citations (2026.acl-long)

Challenge:	Existing benchmarks to test word analogy do not reveal the underneath process of analogical reasoning of neural models.
Approach:	They propose an explanation benchmark for analogical reasoning using a Civil Service exam . they use a free-text explanation scheme to explain whether an analogy should be drawn .
Outcome:	The proposed benchmark is very challenging for state-of-the-art models, it is found.

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Yichi Zhang, Zhuo Chen, Lingbing Guo, Jun Xu, Mengshu Sun, Zhizhen Liu, Lei Liang, Wen Zhang, Huajun Chen

Natural Language Interfaces

Challenge:	Existing approaches focus on generating multi-level citations linked to specific references, making it verifiable and trustworthy.
Approach:	They propose a new data construction pipeline and a benchmark to improve citation granularity and awareness of unknown information.
Outcome:	The proposed model improves on the existing benchmark and data construction pipeline and provides citation granularity and awareness of unknown information.

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

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

RPC-Bench: A Fine-grained Benchmark for Research Paper Comprehension (2026.acl-long)

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

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Yelin Chen, Fanjin Zhang, Suping Sun, Yunhe Pang, Yuanchun Wang, Jian Song, XiaoYan Li, Lei Hou, Shu Zhao, Jie Tang, Juanzi Li

Challenge:	Existing benchmarks for understanding research papers offer limited fine-grained evaluation at scale.
Approach:	They propose a large-scale question-answering benchmark built from review–rebuttal exchanges of high-quality computer science papers.
Outcome:	The proposed model is based on human-verified QA pairs and contains 15K questions.

KS-Lottery: Finding Certified Lottery Tickets for Multilingual Transfer in Large Language Models (2025.naacl-long)

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Fei Yuan, Chang Ma, Shuai Yuan, Qiushi Sun, Lei Li

Communication Efficient Federated Learning for Multilingual Neural Machine Translation with Adapter (2023.findings-acl)

Challenge:	Existing studies have shown that a small subset of parameters is highly effective in fine-tuning . prior work shows that there are a few additional parameters corresponding to an intrinsic dimension in a well-trained Large Language Model.
Approach:	They propose a method to identify a small subset of LLM parameters highly effective in multilingual fine-tuning.
Outcome:	The proposed method can find the certified winning tickets in the embedding layer, and fine-tuning on the found parameters is guaranteed to perform as well as full fine- tuning.

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Yi Liu, Xiaohan Bi, Lei Li, Sishuo Chen, Wenkai Yang, Xu Sun

Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models (2021.naacl-main)

Challenge:	Existing frameworks for federated multilingual neural machine translation (Fed-MNMT) are limited in language resources.
Approach:	They propose a framework that keeps PLMs frozen and only transfers lightweight adapter modules between clients.
Outcome:	The proposed framework reduces communication cost by over 98% while achieving similar or even better performance compared to baselines.

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Wenkai Yang, Lei Li, Zhiyuan Zhang, Xuancheng Ren, Xu Sun, Bin He

Representation Learning

Challenge:	Recent studies reveal a security threat to natural language processing models, called the Backdoor Attack.
Approach:	They propose to hack a model by modifying one single word embedding vector without sacrificing accuracy on clean samples.
Outcome:	The proposed method is more efficient and stealthier on sentiment analysis and sentence-pair classification tasks.

Pun-GAN: Generative Adversarial Network for Pun Generation (D19-1)

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Fuli Luo, Shunyao Li, Pengcheng Yang, Lei Li, Baobao Chang, Zhifang Sui, Xu Sun

Robustness in NLP

Challenge:	Existing methods for generating pun sentences with word senses lack large-scale corpus for supervised learning . a pun is a clever and amusing use of a word with two meanings (word senses)
Approach:	They propose an adversarial generative network for pun generation with a generator and a discriminator to distinguish between generated pun sentences and real sentences with specific word senses.
Outcome:	The proposed network generates sentences that are more ambiguous and diverse in both automatic and human evaluation.

Rethinking Document-level Neural Machine Translation (2022.findings-acl)

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Zewei Sun, Mingxuan Wang, Hao Zhou, Chengqi Zhao, Shujian Huang, Jiajun Chen, Lei Li

HIPO: A Hierarchical Prompt Optimization Framework with Task Awareness and Fine-Grained Debugging (2026.findings-acl)

Challenge:	Neural machine translation models are weak enough for document-level translation . current models only translate sentences individually, resulting in poor document coherence .
Approach:	They propose to use the original Transformer model to test document-level neural machine translation . they find that the original transformer models can achieve strong results for document translation if trained properly .
Outcome:	The proposed model outperforms sentence-level models on nine datasets and two sentence- level datasets across six languages.

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Lu Qi, Lei Chai, Hongrui Yu, Binhang Qi, Hailong Sun

Collab-Overcooked: Benchmarking and Evaluating Large Language Models as Collaborative Agents (2025.emnlp-main)

Challenge:	Existing methods for prompt optimization apply the same prompt across all samples . existing methods ignore variation in sample difficulty .
Approach:	They propose a framework that shifts the paradigm from dataset-level to sample-level optimization.
Outcome:	The proposed framework outperforms baselines on 27 tasks and reduces API calls, token consumption and overall cost by 1.2 to 80.

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Haochen Sun, Shuwen Zhang, Lujie Niu, Lei Ren, Hao Xu, Hao Fu, Fangkun Zhao, Caixia Yuan, Xiaojie Wang

TempCompass: Do Video LLMs Really Understand Videos? (2024.findings-acl)

Challenge:	Large Language Models (LLMs) based agent systems have made great strides in real-world applications beyond traditional NLP tasks.
Approach:	They propose a new LLM-based Multi-Agent System benchmark, Collab-Overcooked, built on the popular Overcooked-AI game with more applicable and challenging tasks in interactive environments.
Outcome:	The proposed benchmark provides a multi-agent framework supporting diverse tasks and objectives and encourages collaboration through natural language communication.

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Yuanxin Liu, Shicheng Li, Yi Liu, Yuxiang Wang, Shuhuai Ren, Lei Li, Sishuo Chen, Xu Sun, Lu Hou

Visual Data in NLP

Challenge:	Existing benchmarks on video large language models lack a comprehensive feedback on temporal perception ability . current models cannot distinguish between different temporal aspects and are limited in task formats .
Approach:	They propose a benchmark to evaluate temporal perception ability of video large language models . they construct conflicting videos that share the same static content but differ in a specific temporal aspect .
Outcome:	The proposed benchmarks show that video large language models exhibit poor temporal perception ability.

Investigating Cross-Modal Skill Injection: Scenarios, Methods, and Hyperparameters (2026.acl-long)

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Zhiyu Xu, Lean Wang, Yuanxin Liu, Lei Li, Hao Zhou, Fandong Meng, Jie Zhou, Xu Sun

PersonaTrace: Synthesizing Realistic Digital Footprints with LLM Agents (2026.eacl-industry)

Challenge:	Existing research lacks systematic analysis of the applicability and methodology of cross-modal skill injection.
Approach:	They investigate the applicability and methodology of cross-modal skill injection by integrating a domain-expert LLM into a VLM.
Outcome:	The proposed method enables transfer of domain-specific expertise from Large Language Models (LLMs) to VLMs without incurring additional training data requirements or significant computational overhead.

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Minjia Wang, Yunfeng Wang, Xiao Ma, Dexin Lv, Qifan Guo, Lynn Zheng, Benliang Wang, Lei Wang, Jiannan Li, Yongwei Xing, Junzhe Xu, Zheng Sun

Challenge:	Publicly available corpora cover only slivers of human activity, such as email threads, chat logs, purchase histories, sensor traces, and provide large-scale supervision for data-hungry machine-learning pipelines.
Approach:	They propose a method for synthesizing realistic digital footprints using large language model agents from a structured user profile.
Outcome:	The proposed method generates diverse sequences of user events, producing corresponding digital artifacts such as emails, messages, calendar entries, reminders, etc.

Leveraging Word-Formation Knowledge for Chinese Word Sense Disambiguation (2021.findings-emnlp)

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Hua Zheng, Lei Li, Damai Dai, Deli Chen, Tianyu Liu, Xu Sun, Yang Liu

Have We Designed Generalizable Structural Knowledge Promptings? Systematic Evaluation and Rethinking (2025.acl-long)

Challenge:	Word sense disambiguation (WSD) methods have not explored word-formations in parataxis languages like Chinese.
Approach:	They propose to leverage word-formation knowledge to enhance Chinese WSD by incorporating word-forms into sense disambiguation models.
Outcome:	The proposed model improves on baselines in Chinese word sense disambiguation (WSD) with word-formation knowledge, the results show.

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Yichi Zhang, Zhuo Chen, Lingbing Guo, Yajing Xu, Shaokai Chen, Mengshu Sun, Binbin Hu, Zhiqiang Zhang, Lei Liang, Wen Zhang, Huajun Chen

GLTW: Joint Improved Graph Transformer and LLM via Three-Word Language for Knowledge Graph Completion (2025.findings-acl)

Challenge:	Existing methods to integrate external knowledge into LLMs focus on specific problems, lacking a comprehensive exploration of the generalization and capability boundaries of SKP.
Approach:	They propose a new paradigm for structural knowledge prompting to integrate external structural knowledge into LLMs by incorporating structural representations.
Outcome:	The proposed benchmark SUBARU enables the evaluation of the generalization capabilities of SKP from four perspectives.

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Kangyang Luo, Yuzhuo Bai, Cheng Gao, Shuzheng Si, Zhu Liu, Yingli Shen, Zhitong Wang, Cunliang Kong, Wenhao Li, Yufei Huang, Ye Tian, Xuantang Xiong, Lei Han, Maosong Sun

On Transferability of Prompt Tuning for Natural Language Processing (2022.naacl-main)

Challenge:	Existing knowledge graphs lack the ability to integrate structural information into LLMs and output predictions deterministically.
Approach:	They propose a method which encodes structural information of KGs and merges it with LLMs to enhance KGC performance.
Outcome:	The proposed method improves the performance of KG Completion datasets on KGs by integrating structural information with LLMs.

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Yusheng Su, Xiaozhi Wang, Yujia Qin, Chi-Min Chan, Yankai Lin, Huadong Wang, Kaiyue Wen, Zhiyuan Liu, Peng Li, Juanzi Li, Lei Hou, Maosong Sun, Jie Zhou

MMLU-CF: A Contamination-free Multi-task Language Understanding Benchmark (2025.acl-long)

Challenge:	Pre-trained language models (PLMs) can achieve comparable performance to full-parameter fine-tuning by tuning a few soft prompts, but require much more training time than fine-timing.
Approach:	They empirically investigate the transferability of soft prompts across different downstream tasks and PLMs to determine what decides prompt transferability.
Outcome:	The proposed method can achieve comparable performance to full-parameter fine-tuning by tuning a few soft prompts, but requires much more training time than fine-timing.

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Qihao Zhao, Yangyu Huang, Tengchao Lv, Lei Cui, Qinzheng Sun, Shaoguang Mao, Xin Zhang, Ying Xin, Qiufeng Yin, Scarlett Li, Furu Wei

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

Challenge:	Multiple-choice question datasets like Massive Multitask Language Understanding (MMLU) have inevitably led to benchmark contamination, resulting in unreliable evaluation.
Approach:	They propose a contamination-free MCQ benchmark called MMLU-CF which reassesses LLMs’ understanding of world knowledge by averting both unintentional and malicious data contamination.
Outcome:	The proposed MMLU-CF reassesses LLMs’ understanding of world knowledge by averting both unintentional and malicious data contamination.

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

Learning Logic Rules for Document-Level Relation Extraction (2021.emnlp-main)

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

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Dongyu Ru, Changzhi Sun, Jiangtao Feng, Lin Qiu, Hao Zhou, Weinan Zhang, Yong Yu, Lei Li

OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems (2024.acl-long)

Challenge:	Existing models for document-level relation extraction relied on implicitly powerful representations, which makes the model less transparent.
Approach:	They propose a probabilistic model for document-level relation extraction by learning logic rules.
Outcome:	The proposed model outperforms baseline models in relation performance and logical consistency.

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Chaoqun He, Renjie Luo, Yuzhuo Bai, Shengding Hu, Zhen Thai, Junhao Shen, Jinyi Hu, Xu Han, Yujie Huang, Yuxiang Zhang, Jie Liu, Lei Qi, Zhiyuan Liu, Maosong Sun

Open-Set Living Need Prediction with Large Language Models (2025.findings-acl)

Challenge:	Large Language Models (LLMs) and Large Multimodal Models have exceeded general human capabilities in various tasks.
Approach:	They present an Olympiad-level bilingual multimodal scientific benchmark featuring 8,476 problems from Olympiad level mathematics and physics competitions.
Outcome:	The best performing model, GPT-4V, attains an average score of 17.97% on OlympiadBench, with a mere 10.74% in physics, highlighting the benchmark rigor and the intricacy of physical reasoning.

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Xiaochong Lan, Jie Feng, Yizhou Sun, Chen Gao, Jiahuan Lei, Xinleishi Xinleishi, Hengliang Luo, Yong Li

Act as you think: Reinforcing Consistent Reasoning in Medical Visual Question Answering (2026.acl-long)

Challenge:	Existing approaches to living need prediction treat it as a closed-set classification problem, severely limiting their ability to capture diversity and complexity of living needs.
Approach:	They propose a system leveraging large language models for unrestricted need prediction that leverages Maslow's hierarchy of needs to align predictions with human living needs.
Outcome:	The proposed system outperforms closed-set approaches on need-based life service recall by an average of 19.37% on real-world datasets.

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Songtao Jiang, Yuan Wang, Ruizhe Chen, Yan Zhang, Ruilin Luo, Bohan Lei, Yeying Jin, Sibo Song, ZhiBo Yang, Jimeng Sun, Jian Wu, Zuozhu Liu

Robustness in NLP

Challenge:	Recent advances have improved the accuracy of medical visual question answering (Med-VQA) however, the high stakes nature of the medical domain has precipitated a shift towards interpretability and transparency of reasoning processes.
Approach:	They propose a reinforcement learning from verifiable rewards framework that rewards internal consistency and logical coherence.
Outcome:	The proposed framework rewards internal consistency and logical coherence, and is highly versatile, the authors show.

A Survey on In-context Learning (2024.emnlp-main)

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Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Baobao Chang, Xu Sun, Lei Li, Zhifang Sui

Low-Resource NLP

Challenge:	In-context learning (ICL) is a new paradigm for natural language processing . large language models (LLMs) demonstrate the ability to learn from a few examples .
Approach:	They propose to explore ICL to evaluate and extrapolate the ability of large language models.
Outcome:	The proposed methods can be used to evaluate and extrapolate the ability of large language models.

KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answering (2025.findings-emnlp)

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Yushi Sun, Kai Sun, Yifan Ethan Xu, Xiao Yang, Xin Luna Dong, Nan Tang, Lei Chen

Structured Data in NLP

Challenge:	Traditional Knowledge Graph Question Answering (KGQA) methods rely on semantic parsing to retrieve knowledge strictly necessary for answer generation.
Approach:	They propose a retrieval-filtering-summarization pipeline that enhances QA coverage by retrieving a broader subgraph likely to contain relevant information.
Outcome:	The proposed pipeline surpasses state-of-the-art solutions by about 7% in quality and exceeds GPT-4o (Tool) by 10-21%.

Learning Collaborative Agents with Rule Guidance for Knowledge Graph Reasoning (2020.emnlp-main)

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Deren Lei, Gangrong Jiang, Xiaotao Gu, Kexuan Sun, Yuning Mao, Xiang Ren

Knowledge Representation

Challenge:	Walk-based models have shown their advantages in knowledge graph reasoning but are limited by their representations and generalizability.
Approach:	They propose a walk-based model that leverages high-quality rules generated by symbolic-based methods to provide reward supervision for walk- based agents.
Outcome:	Experiments on benchmark datasets show that RuleGuider improves the performance of walk-based models without losing interpretability.

LEVEN: A Large-Scale Chinese Legal Event Detection Dataset (2022.findings-acl)

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Feng Yao, Chaojun Xiao, Xiaozhi Wang, Zhiyuan Liu, Lei Hou, Cunchao Tu, Juanzi Li, Yun Liu, Weixing Shen, Maosong Sun

GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs (2026.findings-acl)

Challenge:	Existing legal event detection datasets only cover incomprehensive event types and have limited annotated data.
Approach:	They present a large-scale Chinese legal event detection dataset . they use legal events as side information to promote downstream applications .
Outcome:	The proposed method improves 2.2 points precision in low-resource judgment prediction and 1.5 points precision for unsupervised case retrieval.

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Meixiu Long, Duolin Sun, Dan Yang, Yihan Jiao, Lei Liu, Jiahai Wang, Binbin Hu, Yue Shen, Jie Feng, Zhehao Tan, Junjie Wang, Lianzhen Zhong, Jian Wang, Peng Wei, Jinjie Gu

GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation (2022.emnlp-main)

Challenge:	Existing rankers excel in lexical-matching scenarios, while they struggle with complex queries requiring deep reasoning.
Approach:	They propose a new paradigm that balances flexibility and context awareness to unlock the full potential of groupwise reranking.
Outcome:	The proposed approach achieves a state-of-the-art 65.2 NDCG@10 on BRIGHT and surpasses baselines by 2.1 points on R2MED while delivering a 6.4 inference speedup.

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Lunyiu Nie, Shulin Cao, Jiaxin Shi, Jiuding Sun, Qi Tian, Lei Hou, Juanzi Li, Jidong Zhai

Probabilistic Graph Reasoning for Natural Proof Generation (2021.findings-acl)

Challenge:	Existing approaches to neural semantic parsing are limited by the semantic gap between natural and formal languages.
Approach:	They propose a unified intermediate representation for graph query languages, named GraphQ IR, which has a natural-language-like expression that bridges the semantic gap and formally defined syntax that maintains the graph structure.
Outcome:	The proposed representation can convert user queries into graphQ IR, which can later be losslessly compiled into various downstream graph query languages.

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Changzhi Sun, Xinbo Zhang, Jiangjie Chen, Chun Gan, Yuanbin Wu, Jiaze Chen, Hao Zhou, Lei Li

Structured Data in NLP

Challenge:	Existing approaches to reasoning over formal representations do not explicitly consider inter-dependency between answers and proofs.
Approach:	They propose a novel approach for joint answer prediction and proof generation using an induced graphical model.
Outcome:	The proposed approach achieves 10%-30% improvement on QA accuracy in evaluations under diverse conditions.

OpenFraming: Open-sourced Tool for Computational Framing Analysis of Multilingual Data (2021.emnlp-demo)

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Vibhu Bhatia, Vidya Prasad Akavoor, Sejin Paik, Lei Guo, Mona Jalal, Alyssa Smith, David Assefa Tofu, Edward Edberg Halim, Yimeng Sun, Margrit Betke, Prakash Ishwar, Derry Tanti Wijaya

OneGen: Efficient One-Pass Unified Generation and Retrieval for LLMs (2024.findings-emnlp)

Challenge:	Existing frameworks for analyzing frames in multilingual text documents are available online and via an API.
Approach:	They propose a web-based system for analyzing frames in multilingual text documents . framework combines unsupervised and supervised machine learning and leverages a state-of-the-art multilingual language model .
Outcome:	The proposed framework can significantly improve frame prediction performance while requiring a small sample of manual annotations.

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Jintian Zhang, Cheng Peng, Mengshu Sun, Xiang Chen, Lei Liang, Zhiqiang Zhang, Jun Zhou, Huajun Chen, Ningyu Zhang

Green & Sustainable NLP

Challenge:	Recent advances in Large Language Models (LLMs) have significantly enhanced the generative capabilities for various NLP tasks, but they still suffer from hallucinations due to their exclusive reliance on parametric knowledge.
Approach:	They propose a framework that integrates retrieval tokens generated autoregressively into a single LLM to handle both tasks simultaneously in a unified forward pass.
Outcome:	The proposed framework bridges the traditionally separate training approaches for generation and retrieval by incorporating retrieval tokens generated autoregressively.

Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learning (2023.emnlp-main)

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Lean Wang, Lei Li, Damai Dai, Deli Chen, Hao Zhou, Fandong Meng, Jie Zhou, Xu Sun

FoldMoE: Efficient Long Sequence MoE Training via Attention-MoE Pipelining (2025.acl-long)

Challenge:	In-context learning (ICL) is a promising capability for large language models (LLMs) but its underlying mechanism remains unexplored.
Approach:	They propose a demonstration compression technique to expedite inference and an analysis framework for diagnosing ICL errors in GPT2-XL.
Outcome:	The proposed method improves ICL performance and expedites inference.

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Guichao Zhu, Lintian Lei, Yuhao Qing, Yichao Fu, Fanxin Li, Dong Huang, Zekai Sun, Heming Cui

Green & Sustainable NLP

Challenge:	Existing approaches to training LLMs with Mixture-of-Experts (MoE) architecture on long sequences are limited by the insufficient computation.
Approach:	They propose a MoE training system that enables token-level overlapping across entire Transformer blocks through novel attention-MoE pipelining.
Outcome:	The proposed system achieves 1.49x and 2.72x speedup over state-of-the-art token-level overlapping and non-overlapping baselines on GPT-MoE models with sequences up to 32K tokens.

MM-MATH: Advancing Multimodal Math Evaluation with Process Evaluation and Fine-grained Classification (2024.findings-emnlp)

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Kai Sun, Yushi Bai, Ji Qi, Lei Hou, Juanzi Li

Information Retrieval

Challenge:	Existing benchmarks for multimodal reasoning in large multimodal models are underperforming on multimodal tasks.
Approach:	They propose a benchmark for multimodal reasoning in large multimodal models, MM-MATH . MM's process evaluation employs LMM-as-a-judge to automatically analyze solution steps . diagram misinterpretation is the most common error, they find .
Outcome:	The proposed model achieves only 31% accuracy, compared to 82% for humans.

A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions (2026.acl-long)

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Zhiyin Yu, Yuchen Mou, Juncheng Yan, Junyu Luo, Chunchun Chen, Xing Wei, Yunhui Liu, Hongru Sun, Yuxing Zhang, Jun Xu, Yatao Bian, Ming Zhang, Wei Ye, Tieke He, Jie Yang, Guanjie Zheng, Zhonghai Wu, Bo Zhang, Lei Bai, Xiao Luo

From Mimicking to Integrating: Knowledge Integration for Pre-Trained Language Models (2022.findings-emnlp)

Challenge:	Existing research on reinforcement learning for LLMs under data scarcity has not been unified.
Approach:	They propose a top-up hierarchical framework built around three complementary perspectives: data-centric, training-centric and framework-centric.
Outcome:	The proposed framework provides a clear conceptual foundation for understanding the design space of data-efficient RL for large language models and to guide researchers working in this emerging area.

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Lei Li, Yankai Lin, Xuancheng Ren, Guangxiang Zhao, Peng Li, Jie Zhou, Xu Sun

CKDST: Comprehensively and Effectively Distill Knowledge from Machine Translation to End-to-End Speech Translation (2023.findings-acl)

Challenge:	Existing models for natural language processing (NLP) are fine-tuned and released for research and deployments.
Approach:	They propose a PLM reuse paradigm that merges teacher-PLM knowledge into a student model.
Outcome:	The proposed paradigm can reduce the computational cost and environmental side-effects of retraining the PLM from scratch.

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Yikun Lei, Zhengshan Xue, Xiaohu Zhao, Haoran Sun, Shaolin Zhu, Xiaodong Lin, Deyi Xiong

ODA: Observation-Driven Agent for integrating LLMs and Knowledge Graphs (2024.findings-acl)

Challenge:	End-to-end speech-totext translation (ST) data are limited due to the limited resources.
Approach:	They propose a knowledge distillation framework for speech translation that integrates knowledge from machine translation and decouples knowledge from non-target class knowledge.
Outcome:	The proposed framework outperforms state-of-the-art models on a benchmark dataset.

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Lei Sun, Zhengwei Tao, Youdi Li, Hiroshi Arakawa

Delving into the Openness of CLIP (2023.findings-acl)

Challenge:	Existing approaches to integrate large language models and knowledge graphs with LLMs often ignore the rich cognitive potential inherent in KGs.
Approach:	They propose an observation-driven agent framework that integrates KG reasoning abilities via global observation and integrates it into the action and reflection modules.
Outcome:	The proposed framework improves on several datasets and achieves 12.87% and 8.9% accuracy improvements.

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Shuhuai Ren, Lei Li, Xuancheng Ren, Guangxiang Zhao, Xu Sun

Visual Data in NLP

Challenge:	Contrastive Language-Image Pre-training (CLIP) allows for open-vocabulary visual recognition, where the model can recognize images from an open class set in a zero-shot manner.
Approach:	They propose to use image classification as an image-to-text matching task instead of discrete category IDs to achieve open-vocabulary visual recognition.
Outcome:	The proposed model can recognize images from an open vocabulary in a zero-shot manner, but its performance deteriorates as the vocabulary expands.

WantWords: An Open-source Online Reverse Dictionary System (2020.emnlp-demos)

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Fanchao Qi, Lei Zhang, Yanhui Yang, Zhiyuan Liu, Maosong Sun

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

Challenge:	Existing reverse dictionary systems only support English reverse dictionary queries . a reverse dictionary can help people who can't remember a word from memory .
Approach:	They propose an online reverse dictionary system that outperforms other reverse dictionary systems . it supports Chinese and English-Chinese as well as Chinese-English cross-lingual reverse dictionary queries .
Outcome:	The proposed reverse dictionary outperforms other reverse dictionary systems on performance . it supports Chinese and English-Chinese as well as Chinese-English queries .

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

BayesKD: Bayesian Knowledge Distillation for Compact LLMs in Constrained Fine-tuning Scenarios (2025.findings-acl)

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

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Wei Li, Lujun Li, Mark G. Lee, Shengjie Sun, Lei Zhang, Wei Xue, Yike Guo

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

Challenge:	Large language models (LLMs) have revolutionized various domains with their remarkable capabilities, but their massive parameter sizes pose significant challenges for fine-tuning and inference.
Approach:	They propose a Bayesian Knowledge Distillation framework for compact Large Language Models in resource-constrained fine-tuning scenarios that employs Logits Dual-Scaling, Knowledge Alignment Module, and Bayes Distillations Optimization.
Outcome:	The proposed framework outperforms baseline methods on various state-of-the-art LLMs, including LLaMA, Qwen2, Bloom, and Vicuna.

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

Multilingual Translation via Grafting Pre-trained Language Models (2021.findings-emnlp)

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

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Zewei Sun, Mingxuan Wang, Lei Li

SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis (2025.findings-emnlp)

Challenge:	Existing methods to graft pre-trained (masked) language models to multilingual data are limited, and they lack cross-attention component.
Approach:	They propose to graft separately pre-trained (masked) language models for machine translation using monolingual data and parallel data.
Outcome:	The proposed method achieves average improvements of 5.8 BLEU in x2en and 2.9 BLUE in en2x directions compared with the multilingual Transformer of the same size.

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Shuang Sun, Huatong Song, Yuhao Wang, Ruiyang Ren, Jinhao Jiang, Junjie Zhang, Fei Bai, Jia Deng, Xin Zhao, Zheng Liu, Lei Fang, Zhongyuan Wang, Ji-Rong Wen

Reasoning

Challenge:	Existing approaches to deep search training lack high-quality training trajectories, prohibitive computational costs and lack of high-fidelity training data.
Approach:	They propose a framework that synthesizes high-quality training data by simulating real user interactions in live web search environments.
Outcome:	The proposed framework synthesizes high-quality training data by simulating user interactions in live web search environments.

CodeEvo: Interaction-Driven Synthesis of Code-centric Data through Hybrid and Iterative Feedback (2026.acl-long)

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Qiushi Sun, Jingyang Gong, Lei Li, Qipeng Guo, Fei Yuan

Challenge:	Existing methods for generating instruction-code pairs rely on rigid heuristics and are labor-intensive.
Approach:	They propose a dual-agent architecture that integrates a Coder and a Reviewer to orchestrate the generation trajectory.
Outcome:	The proposed architecture outperforms baselines on a large-scale dataset of instruction-code pairs with stepped difficulty levels.

More Than Sum of Its Parts: Deciphering Intent Shifts in Multimodal Hate Speech Detection (2026.findings-acl)

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Runze Sun, Yu Zheng, Zexuan Xiong, Zhongjin Qu, Lei Chen, Jie Zhou, Jiwen Lu

Can Language Models Understand Physical Concepts? (2023.emnlp-main)

Challenge:	Existing systems struggle with multimodal content where the emergent meaning transcends the aggregation of individual modalities.
Approach:	They propose a framework to characterize semantic intent shifts where modalities interact to construct implicit hate from benign cues or neutralize toxicity through semantic inversion.
Outcome:	The proposed framework outperforms state-of-the-art benchmarks on H-VLI and on established benchmarks.

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Lei Li, Jingjing Xu, Qingxiu Dong, Ce Zheng, Xu Sun, Lingpeng Kong, Qi Liu

Speak No Evil, Just Prompt: Low-resource Multilingual Toxic Speech Detection with Audio Language Model (2026.findings-acl)

Challenge:	Existing language models do not understand basic physical concepts in the human world.
Approach:	They propose a method to transfer embodied knowledge from visual models to LMs . they use visual concepts and embodies concepts learned from interaction with the world .
Outcome:	The proposed method achieves comparable performance with scaling up parameters of LMs 134.

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Mingzi Zuo, Lei Zhang, Hailiang Sun, Shengzhi Huo, Changyu Dong, Xin Wang, Bo Wang, Hao Liu

Dynamic Knowledge Distillation for Pre-trained Language Models (2021.emnlp-main)

Challenge:	Existing methods for toxic speech detection rely on high-resource languages and lack acoustic cues.
Approach:	They propose a prompt-based adaptation framework that performs end-to-end toxicity detection without ASR.
Outcome:	The proposed framework achieves a micro-averaged ROC-AUC of 98.07% on polySpeechTox . it is based on a frozen audio language model and can perform end-to-end toxicity detection without ASR .

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Lei Li, Yankai Lin, Shuhuai Ren, Peng Li, Jie Zhou, Xu Sun

ENPAR:Enhancing Entity and Entity Pair Representations for Joint Entity Relation Extraction (2021.eacl-main)

Challenge:	Existing methods conduct knowledge distillation statically, e.g., student model aligns output distribution to teacher model on pre-defined training dataset.
Approach:	They propose a dynamic knowledge distillation that empowers the student to adjust the learning procedure according to its competency . they find it is promising and provide discussions on potential future directions towards more efficient methods .
Outcome:	The proposed method can boost student model performance while accelerating training . the proposed method reduces memory usage and accelerates model inference .

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Yijun Wang, Changzhi Sun, Yuanbin Wu, Hao Zhou, Lei Li, Junchi Yan

CascadeBERT: Accelerating Inference of Pre-trained Language Models via Calibrated Complete Models Cascade (2021.findings-emnlp)

Challenge:	Existing methods for joint entity relation extraction use multitask learning frameworks, but annotations for additional tasks are hard to obtain.
Approach:	They propose a pre-training method to improve the joint extraction performance with just extra entity annotations.
Outcome:	The proposed method outperforms existing methods on ACE05, SciERC, and NYT and outperformed BERT on other tasks.

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Lei Li, Yankai Lin, Deli Chen, Shuhuai Ren, Peng Li, Jie Zhou, Xu Sun

Combating Security and Privacy Issues in the Era of Large Language Models (2024.naacl-tutorials)

Challenge:	Experimental results show that CascadeBERT can achieve an overall 15% improvement under 4x speed-up compared with existing dynamic early exiting methods on six classification tasks.
Approach:	They propose a framework which emits predictions in internal layers without passing through the entire model.
Outcome:	The proposed framework can achieve 15% improvement under 4x speed-up compared with existing methods on six classification tasks yielding more calibrated and accurate predictions.

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Muhao Chen, Chaowei Xiao, Huan Sun, Lei Li, Leon Derczynski, Anima Anandkumar, Fei Wang

Proofread: Fixes All Errors with One Tap (2024.acl-demos)

Challenge:	a tutorial aims to provide a summary of risks and vulnerabilities in large language models . a number of studies have focused on security, privacy and copyright aspects of LLMs .
Approach:	This tutorial seeks to provide a systematic summary of risks and vulnerabilities in large language models . authors will discuss security, privacy and copyright aspects of LLMs .
Outcome:	This tutorial aims to provide a systematic summary of risks and vulnerabilities in large language models . it will also outline emerging challenges in security, privacy and reliability of LLMs .

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Renjie Liu, Yanxiang Zhang, Yun Zhu, Haicheng Sun, Yuanbo Zhang, Michael Huang, Shanqing Cai, Lei Meng, Shumin Zhai

LEGENT: Open Platform for Embodied Agents (2024.acl-demos)

Challenge:	Extensive experiments on a human-labeled golden set showed our tuned PaLM2-XS model achieved 85.56% good ratio.
Approach:	They propose a two-stage tuning approach to acquire the dedicated Large Language Model for the feature, followed by a reinforcement learning approach for targeted refinement.
Outcome:	The proposed model achieves 85.56% good quality on Rewrite and proofread tasks on human-labeled golden sets.

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Zhili Cheng, Zhitong Wang, Jinyi Hu, Shengding Hu, An Liu, Yuge Tu, Pengkai Li, Lei Shi, Zhiyuan Liu, Maosong Sun