Papers by Wei Cao

TURING: an Accurate and Interpretable Multi-Hypothesis Cross-Domain Natural Language Database Interface (2021.acl-demo)

Challenge:	Large language models have created significant safety concerns . factuality ability is crucial in determining whether they can be deployed and applied safely and compliantly within specific regions.
Approach:	They propose a benchmark to evaluate the factuality of large language models in China . they evaluate the models' ability to provide accurate and reliable information .
Outcome:	The proposed benchmark evaluates the factuality abilities of existing LLMs and compares them to LLM abilities.

Copied to clipboard

Peng Xu, Wenjie Zi, Hamidreza Shahidi, Ákos Kádár, Keyi Tang, Wei Yang, Jawad Ateeq, Harsh Barot, Meidan Alon, Yanshuai Cao

LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing (2024.emnlp-main)

Challenge:	Existing text-to-SQL semantic parsers cannot achieve high accuracy in cross-database setting . TURING is a NLDB system that can be used to democratize data-driven insights for non-technical users .
Approach:	They propose a TURING system that provides high-precision natural language explanations of SQL queries in a beam.
Outcome:	The proposed system achieves 75.1% execution accuracy and 78.3% top-5 beam execution accuracy on the Spider validation set.

Copied to clipboard

Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing, Cheng Jiayang, Zhaowei Wang, Ying Su, Raj Shah, Ruohao Guo, Jing Gu, Haoran Li, Kangda Wei, Zihao Wang, Lu Cheng, Surangika Ranathunga, Meng Fang, Jie Fu, Fei Liu, Ruihong Huang, Eduardo Blanco, Yixin Cao, Rui Zhang, Philip Yu, Wenpeng Yin

Challenge:	a comparative analysis of paper (meta-)reviews by large language models (LLMs) aims to identify and distinguish LLMs from human activities .
Approach:	They present a comparative analysis to identify and distinguish LLM activities from human activities.
Outcome:	The proposed analysis aims to improve recognition of instances when someone implicitly uses LLMs for reviewing activities.

AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation (2026.eacl-tutorials)

Copied to clipboard

Yufang Hou, Steffen Eger, Anne Lauscher, Wei Zhao, Yong Cao

Learning In-context Learning for Named Entity Recognition (2023.acl-long)

Challenge:	This tutorial provides an overview of recent advances in AI-assisted tools and models that support and enhance the scientific research process.
Approach:	This tutorial provides an overview of recent advances in AI-assisted tools and models that support and enhance the scientific research process.
Outcome:	This tutorial provides an overview of recent advances in AI-assisted tools and models that support and enhance the scientific research process.

Copied to clipboard

Jiawei Chen, Yaojie Lu, Hongyu Lin, Jie Lou, Wei Jia, Dai Dai, Hua Wu, Boxi Cao, Xianpei Han, Le Sun

Optimizing Deeper Transformers on Small Datasets (2021.acl-long)

Challenge:	Existing methods to recognize entities in text are limited by the diversity of entity types and the lack of high-quality annotations.
Approach:	They propose an in-context learning-based NER approach that can inject in-const NER ability into PLMs and recognize entities of novel types on-the-fly using only a few demonstrative instances.
Outcome:	The proposed method outperforms the PLMs+fine-tuning counterparts on 4 few-shot NER datasets and significantly outperformed the Plms+initialized extractors.

Copied to clipboard

Peng Xu, Dhruv Kumar, Wei Yang, Wenjie Zi, Keyi Tang, Chenyang Huang, Jackie Chi Kit Cheung, Simon J.D. Prince, Yanshuai Cao

Programming Languages in NLP

Challenge:	a common belief that training deep transformers from scratch requires large datasets is wrong . however, with proper initialization and optimization, the benefits of very deep transformer can carry over to challenging tasks with small datasets.
Approach:	They train 48 layers of transformers from pre-trained RoBERTa and 24 relation-aware layers from scratch.
Outcome:	The proposed scheme achieves state-of-the-art performance on a text-to-sql parsing benchmark . it uses 24 fine-tuned layers from pre-trained RoBERTa and 24 relation-aware layers from scratch .

To Diff or Not to Diff? Structure-Aware and Adaptive Output Formats for Efficient LLM-based Code Editing (2026.findings-acl)

Copied to clipboard

Wei Cheng, Yongchang Cao, Chen Shen, Binhua Li, Jue Chen, Yongbin Li, Wei Hu

Programming Languages in NLP

Challenge:	Large Language Models (LLMs) are increasingly used for code editing, yet the full-code generation paradigm suffers from severe efficiency bottlenecks.
Approach:	They propose to use a structure-aware diff format to train LLMs to choose the most token-efficient format between a given diff format and full code.
Outcome:	The proposed approach matches the most token-efficient format with full-code generation while reducing latency and cost by over 30% on long-code editing tasks.

Enhancing Self-Attention with Knowledge-Assisted Attention Maps (2022.naacl-main)

Copied to clipboard

Jiangang Bai, Yujing Wang, Hong Sun, Ruonan Wu, Tianmeng Yang, Pengfei Tang, Defu Cao, Mingliang Zhang1, Yunhai Tong, Yaming Yang, Jing Bai, Ruofei Zhang, Hao Sun, Wei Shen

Beyond Token Length: Step Pruner for Efficient and Accurate Reasoning in Large Language Models (2026.findings-acl)

Challenge:	Existing works of knowledge infusion depend on multi-task learning frameworks, which are inefficient and require large-scale retraining when new knowledge is considered.
Approach:	They propose a method which integrates knowledge-generated attention maps into the self-attention mechanism and integrates it into the model.
Outcome:	The proposed model outperforms existing methods on academic datasets and industry-scale ad relevance applications.

Copied to clipboard

Canhui Wu, Qiong Cao, Chang Li, Zhenfang Wang, Chao Xue, Yuwei Fan, Wei Xi, Xiaodong He

Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction (D19-1)

Challenge:	Existing reinforcement learning methods for large reasoning models suffer from excessive verbosity, known as "overthinking." Existing models penalize generated tokens to promote conciseness, but these methods encounter two challenges: they may develop hacking behavior in later stages of training by discarding reasoning steps.
Approach:	They propose a framework that steers large reasoning models toward more efficient reasoning . they prioritize correctness while imposing penalties for redundant steps .
Outcome:	The proposed framework reduces token usage by 69.7% on AIME24.

Copied to clipboard

Shun Zheng, Wei Cao, Wei Xu, Jiang Bian

Argue with Me Tersely: Towards Sentence-Level Counter-Argument Generation (2023.emnlp-main)

Challenge:	Existing event extraction methods are limited to extract event arguments within the sentence scope.
Approach:	They propose a model which generates an entity-based directed acyclic graph to fulfill document-level EE effectively.
Outcome:	The proposed model can generate entity-based directed acyclic graph to fulfill document-level EE effectively.

Copied to clipboard

Jiayu Lin, Rong Ye, Meng Han, Qi Zhang, Ruofei Lai, Xinyu Zhang, Zhao Cao, Xuanjing Huang, Zhongyu Wei

Text Generation

Challenge:	Existing work describes paragraph-level counter-argument generation task as paragraph-based . however, sentence-level generation can be quite different due to its unique constraints and brevity-focused challenges.
Approach:	They propose a benchmark framework for sentence-level counter-argument generation . they use an annotated debate forum dataset to generate high-quality counter-argments .
Outcome:	The proposed framework and evaluator are competitive in counter-argument generation tasks.

MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction (2022.findings-acl)

Copied to clipboard

Linhan Zhang, Qian Chen, Wen Wang, Chong Deng, ShiLiang Zhang, Bing Li, Wei Wang, Xin Cao

IW-Bench: Evaluating Large Multimodal Models for Converting Image-to-Web (2025.findings-acl)

Challenge:	Keyphrase extraction (KPE) extracts phrases in a document that provide a concise summary of the core content.
Approach:	They propose an unsupervised keyphrase extraction method that ranks candidates by similarity between embeddings of source document and masked document.
Outcome:	The proposed method outperforms state-of-the-art methods on six benchmarks . it achieves average 3.53 improvement over the existing method .

Copied to clipboard

Hongcheng Guo, Wei Zhang, Junhao Chen, Yaonan Gu, Jian Yang, Junjia Du, Shaosheng Cao, Binyuan Hui, Tianyu Liu, Jianxin Ma, Chang Zhou, Zhoujun Li

ELTLM: Evaluation of Longitudinal Temporal Large Multimodal Models in Clinical Scenarios (2026.findings-acl)

Challenge:	Existing models have been introduced to improve image comprehension, but there is no robust benchmark for imagetoweb conversion.
Approach:	They propose a benchmark to assess imagetoweb conversion proficiency of large multimodal models . they propose to measure layout information of web pages by parsing the Document Object Model tree .
Outcome:	The proposed benchmark measures the layout information of web pages—i.e., the positional relationships between elements—which has been overlooked by prior work.

Copied to clipboard

Gengyuan Hu, Haoxiang Liu, Chenhong Cao, Shilei Tan, Wei Gong

Two Heads Are Better Than One: Improving Fake News Video Detection by Correlating with Neighbors (2023.findings-acl)

Challenge:	Existing evaluation benchmarks focus on static evaluation of large multimodal models . existing evaluation paradigms neglect a critical aspect of clinical practice: longitudinal analysis .
Approach:	They propose a temporal perception and reasoning benchmark to assess models' temporal grounding and consistency.
Outcome:	ELTLM features a hierarchical task taxonomy comprising Temporal Perception QA and Temporal Reasoning QA.

Copied to clipboard

Peng Qi, Yuyang Zhao, Yufeng Shen, Wei Ji, Juan Cao, Tat-Seng Chua

Topology Matters: Measuring Memory Leakage in Multi-Agent LLMs (2026.findings-acl)

Challenge:	Existing frameworks for detecting fake news videos are limited . a new approach is proposed to integrate neighborhood information of new videos .
Approach:	They propose a framework for automatically detecting fake news videos . it integrates neighborhood relationship of new videos belonging to same event .
Outcome:	The proposed framework improves performance of existing detectors and graph aggregation and debunking rectification modules.

Copied to clipboard

Jinbo Liu, Defu Cao, Yifei Wei, Tianyao Su, Yuan Liang, Yushun Dong, Yan Liu, Yue Zhao, Xiyang Hu

Latent Suicide Risk Detection on Microblog via Suicide-Oriented Word Embeddings and Layered Attention (D19-1)

Challenge:	Multi-agent LLMs are rapidly moving from prototype to real-world use . network topology is a first-order security parameter in multi-aggent systems .
Approach:	They propose a framework for comparing topology-conditioned memory leakage in multi-agent LLM systems.
Outcome:	The proposed framework evaluates topology-conditioned memory leakage in multi-agent LLM systems.

Copied to clipboard

Lei Cao, Huijun Zhang, Ling Feng, Zihan Wei, Xin Wang, Ningyun Li, Xiaohao He

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

Challenge:	Existing approaches to detect suicidal ideation on social media are limited to a small group of people.
Approach:	They propose to use tree holes to embed words into microblogs to strengthen the sensibility of suicide-related lexicons and to use a two-layered attention mechanism to grasp intermittently changing points from individual's open blog streams.
Outcome:	The proposed approach can achieve over 91% accuracy with the use of suicide-oriented word embeddings and attention on a large-scale well-labelled suicide data set.

Copied to clipboard

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.

InquireMobile: Teaching VLM-based Mobile Agent to Request Human Assistance via Reinforcement Fine-Tuning (2026.acl-long)

Copied to clipboard

Qihang Ai, Pi Bu, Yue Cao, Yingyao Wang, Jihao Gu, Jingxuan Xing, Zekun Zhu, Wei Jiang, Zhicheng Zheng, Jun Song, Yuning Jiang

Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment (2022.acl-long)

Challenge:	Recent advances in Vision-Language Models (VLMs) have enabled mobile agents to perceive and interact with real-world mobile environments based on human instructions.
Approach:	They propose a vision-language model that actively seeks human confirmation at critical decision points and a model inspired by reinforcement learning.
Outcome:	The proposed model achieves an improvement of 46.8% in inquiry success rate and the best overall success rate among existing baselines on InquireBench.

Copied to clipboard

Zijie Huang, Zheng Li, Haoming Jiang, Tianyu Cao, Hanqing Lu, Bing Yin, Karthik Subbian, Yizhou Sun, Wei Wang

Rectified Sparse Attention for Efficient Long-Sequence Generation (2026.findings-acl)

Challenge:	Existing methods to predict missing facts in knowledge graphs are limited in language alignment . SS-AGA uses seed alignment as an edge type to fuses all KGs as a whole graph .
Approach:	They propose a self-supervised adaptive graph alignment method that fuses all KGs as a whole graph by regarding alignment as 'a new edge type' they propose SS-AGA method that uses relation-aware attention weights to capture potential alignment pairs in a new paradigm.
Outcome:	The proposed method can predict missing facts in a knowledge graph (KG) but language alignment is scarce and new alignment identification is noisy.

Copied to clipboard

Yutao Sun, Tianzhu Ye, Li Dong, Yuqing Xia, Jian Chen, Yizhao Gao, Shijie Cao, Jianyong Wang, Furu Wei

Explore More Guidance: A Task-aware Instruction Network for Sign Language Translation Enhanced with Data Augmentation (2022.findings-naacl)

Challenge:	Recent sparse decoding methods improve efficiency but suffer from KV cache misalignment, resulting in performance degradation.
Approach:	They propose a method that combines block-sparse attention with periodic dense rectification to bound error accumulation and preserve alignment with the pretraining distribution.
Outcome:	Experiments on math reasoning, language modeling, and retrieval tasks show that ReSA achieves near-lossless generation quality with significantly improved efficiency.

Copied to clipboard

Yong Cao, Wei Li, Xianzhi Li, Min Chen, Guangyong Chen, Long Hu, Zhengdao Li, Kai Hwang

Machine Translation

Challenge:	Existing studies focus on the recognition step, while paying less attention to sign language translation.
Approach:	They propose a task-aware instruction network, namely TIN-SLT, for sign language translation, by introducing the isntruction module and the learning-based feature fuse strategy into a Transformer network.
Outcome:	The proposed system outperforms existing solutions on two benchmark datasets, PHOENIX-2014-T and ASLG-PC12, and outperformed previous best solutions by 1.65 and 1.42 in terms of BLEU-4.

Reasoning over Precedents Alongside Statutes: Case-Augmented Deliberative Alignment for LLM Safety (2026.acl-long)

Copied to clipboard

Can Jin, Rui Wu, Tong Che, Qixin Zhang, Hongwu Peng, Jiahui Zhao, Zhenting Wang, Wenqi Wei, Ligong Han, Zhao Zhang, Yuan Cao, Ruixiang Tang, Dimitris N. Metaxas

CT-FineBench: A Diagnostic Fidelity Benchmark for Fine-Grained Evaluation of CT Report Generation (2026.acl-long)

Challenge:	OpenAI introduces deliberative alignment (DA) to enhance safety of its o-series models, but effectiveness of this approach in open-source LLMs is understudied.
Approach:	They propose a case-augmented deliberative alignment method for large language models . they propose to use reinforcement learning on self-generated safety reasoning chains .
Outcome:	The proposed method avoids narrowly enumerated rules and allows broader adaptability.

Copied to clipboard

Ruifeng Yuan, Wanxing Chang, Weiwei Cao, Bowen Shi, Zhongyu Wei, Ling Zhang, Jianpeng Zhang

Natural Language Interfaces

Challenge:	Existing evaluation metrics for radiology report generation focus on lexical overlap and entity matching.
Approach:	They propose a benchmark to evaluate the fine-grained factual consistency of CT reports . they use a question-answering process to query a machine-generated report .
Outcome:	The proposed benchmark evaluates the fine-grained factual consistency of CT reports . it correlates better with expert clinical assessment and is more sensitive to errors .

See the World, Discover Knowledge: A Chinese Factuality Evaluation for Large Vision Language Models (2025.findings-acl)

Copied to clipboard

Jihao Gu, Yingyao Wang, Pi Bu, Chen Wang, Ziming Wang, Tengtao Song, Donglai Wei, Jiale Yuan, Yingxiu Zhao, Yancheng He, Shilong Li, Jiaheng Liu, Meng Cao, Jun Song, Yingshui Tan, Xiang Li, Wenbo Su, Xiaoyong Zhu, Bo Zheng

GAPO: Robust Advantage Estimation for Real-World Code LLMs (2026.findings-acl)

Challenge:	Existing models for large vision language models do not fully reflect their knowledge capacity and reliability, resulting in erroneous outputs that do not align with the image content or provide answers lacking knowledge evidence.
Approach:	They propose a Chinese-based benchmark for visual factuality across 8 major topics and 56 subtopics and a multi-hop question construction.
Outcome:	The proposed model decouples visual factuality into two parts: seeing the world and discovering knowledge.

Copied to clipboard

Jianqing Zhang, Zhezheng Hao, Wei Xia, Hande Dong, Hong Wang, Chenxing Wei, Yuyan Zhou, Yubin Qi, Qiang Lin, Jian Cao

ChatSOP: An SOP-Guided MCTS Planning Framework for Controllable LLM Dialogue Agents (2025.acl-long)

Challenge:	Reinforcement learning (RL) is widely used for post-training large language models (LLMs) in code editing, but in real-world code editing scenarios, reward distributions are often skewed with unpredictable noise, leading to distorted advantage computation and increased rollout outliers.
Approach:	They propose a group-relative method that finds an interval with the highest SNR and uses the median of that interval as an adaptive Q to replace the group mean in advantage calculation.
Outcome:	The proposed method improves on nine instruction-tuned LLMs while remaining plug-and-play and efficient.

Copied to clipboard

Zhigen Li, Jianxiang Peng, Yanmeng Wang, Yong Cao, Tianhao Shen, Minghui Zhang, Linxi Su, Shang Wu, Yihang Wu, YuQian Wang, Ye Wang, Wei Hu, Jianfeng Li, Shaojun Wang, Jing Xiao, Deyi Xiong

FRSUM: Towards Faithful Abstractive Summarization via Enhancing Factual Robustness (2022.findings-emnlp)

Challenge:	Existing models that use Large Language Models (LLMs) show superior performance in various tasks, but lack of controllability leads to unfocused conversations or task failure.
Approach:	They propose a standard operating procedure (SOP) framework to regulate dialogue flow by integrating Chain of Thought reasoning and supervised fine-tuning for SOP prediction.
Outcome:	The proposed method achieves a 27.95% improvement in action accuracy compared to baseline models based on GPT-3.5 and also shows notable gains for open-source models.

Copied to clipboard

Wenhao Wu, Wei Li, Jiachen Liu, Xinyan Xiao, Ziqiang Cao, Sujian Li, Hua Wu

Pretrained Language Models for Dialogue Generation with Multiple Input Sources (2020.findings-emnlp)

Challenge:	Existing models of abstractive summarization are able to generate fluent and coherent summaries, but they still suffer from the unfaithful generation problem.
Approach:	They propose to improve the faithfulness of existing models by enhancing their factual robustness by using a novel training strategy, namely FRSUM, which teaches the model to defend against both explicit adversarial samples and implicit factual adversarials.
Outcome:	The proposed training strategy improves faithfulness of various models, such as T5, BART, and T5 .

Copied to clipboard

Yu Cao, Wei Bi, Meng Fang, Dacheng Tao

Improving Empathetic Response Generation by Recognizing Emotion Cause in Conversations (2021.findings-emnlp)

Challenge:	Large-scale pretrained language models have achieved outstanding performance on natural language understanding tasks.
Approach:	They propose to fuse attention information from multiple input sources to achieve better relevance with dialogue history than simple fusion baselines.
Outcome:	The proposed models deliver higher relevance with dialogue history than baselines.

Copied to clipboard

Jun Gao, Yuhan Liu, Haolin Deng, Wei Wang, Yu Cao, Jiachen Du, Ruifeng Xu

Continual Learning for Neural Machine Translation (2021.naacl-main)

Challenge:	Existing approaches to empathetic response generation ignore the emotion cause . existing dialogue systems lack emotion understanding and empathy .
Approach:	They propose a framework that integrates emotion cause information into empathetic response generation by predicting context emotion labels and sequence of emotion cause-oriented labels.
Outcome:	The proposed framework improves empathetic response generation by incorporating emotion cause information into the model.

Copied to clipboard

Yue Cao, Hao-Ran Wei, Boxing Chen, Xiaojun Wan

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

Challenge:	Neural machine translation models are data-driven and require large-scale training corpus . continual learning remains a big challenge for artificial intelligence systems and models .
Approach:	They propose a continual learning framework for NMT models that incorporates multiple stages of training to alleviate catastrophic forgetting problem.
Outcome:	The proposed framework achieves superior performance compared to baseline models in all settings.

Copied to clipboard

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

Disentangling Language and Culture for Evaluating Multilingual Large Language Models (2025.acl-long)

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

Copied to clipboard

Jiahao Ying, Wei Tang, Yiran Zhao, Yixin Cao, Yu Rong, Wenxuan Zhang

Global-to-Local Neural Networks for Document-Level Relation Extraction (2020.emnlp-main)

Challenge:	Extensive evaluations of large language models (LLMs) are conducted on a wide range of models, revealing a notable cultural-linguistic synergy phenomenon, where models exhibit better performance when questions are culturally aligned with the language.
Approach:	They propose a Dual Evaluation Framework to comprehensively assess the multilingual capabilities of large language models by decomposing evaluation along dimensions of linguistic medium and cultural context.
Outcome:	The proposed framework allows for a nuanced analysis of LLMs’ ability to process questions within both native and cross-cultural contexts cross-lingually.

Copied to clipboard

Difeng Wang, Wei Hu, Ermei Cao, Weijian Sun

LLMs-as-Instructors: Learning from Errors Toward Automating Model Improvement (2024.findings-emnlp)

Challenge:	Relation extraction (RE) aims to identify the semantic relations between named entities in text.
Approach:	They propose a novel relation extraction model that encodes document information in terms of entity global and local representations and context relation representations.
Outcome:	The proposed model achieves superior performance on two public datasets for document-level RE.

Copied to clipboard

Jiahao Ying, Mingbao Lin, Yixin Cao, Wei Tang, Bo Wang, Qianru Sun, Xuanjing Huang, Shuicheng Yan

Challenge:	Using advanced Large Language Models, instructors can improve training of smaller models by analyzing their own model's errors.
Approach:	They propose a framework that leverages advanced Large Language Models to enhance training of smaller target models.
Outcome:	The proposed framework outperforms ChatGPT on multiple benchmarks and shows that it improves on both in-domain and out-of-domain benchmarks.

Neural Label Search for Zero-Shot Multi-Lingual Extractive Summarization (2022.acl-long)

Copied to clipboard

Ruipeng Jia, Xingxing Zhang, Yanan Cao, Zheng Lin, Shi Wang, Furu Wei

Text Generation

Challenge:	Existing methods to translate sentences to other languages using heuristics are challenging.
Approach:	They propose a model that learns hierarchical weights for different sets of labels and applies them to other languages to translate them.
Outcome:	The proposed model can translate English datasets to other languages and obtain different sets of labels again using heuristics.

Hi-ArG: Exploring the Integration of Hierarchical Argumentation Graphs in Language Pretraining (2023.emnlp-main)

Copied to clipboard

Jingcong Liang, Rong Ye, Meng Han, Qi Zhang, Ruofei Lai, Xinyu Zhang, Zhao Cao, Xuanjing Huang, Zhongyu Wei

Retrieve-and-Sample: Document-level Event Argument Extraction via Hybrid Retrieval Augmentation (2023.acl-long)

Challenge:	Recent studies have discussed its capability to assist language models for various applications.
Approach:	They propose a structure to organize arguments using the Hierarchical Argumentation Graph (Hi-ArG) and propose two approaches to exploit Hi-AarG, including a text-graph multi-modal model GreaseArR and a framework augmented with graph information.
Outcome:	The proposed structure supersedes existing language models on two argumentation tasks while incorporating graph information during further training improves vanilla language models.

Copied to clipboard

Yubing Ren, Yanan Cao, Ping Guo, Fang Fang, Wei Ma, Zheng Lin

Information Retrieval

Challenge:	Recent studies show the effectiveness of retrieval augmentation in many generative NLP tasks.
Approach:	They investigate retrieval settings from the input and label distribution views . they further augment document-level EAE with pseudo demonstrations sampled from event semantic regions .
Outcome:	The proposed methods can augment document-level EAE with pseudo demonstrations . the methods can be used in generative NLP tasks such as dialogue response generation .

Retrieve, Rerank and Rewrite: Soft Template Based Neural Summarization (P18-1)

Copied to clipboard

Ziqiang Cao, Wenjie Li, Sujian Li, Furu Wei

Text Generation

Challenge:	Existing summarization systems rely on the source text to generate summaries, which tends to work unstably.
Approach:	They propose to use existing summaries as soft templates to guide the seq2seq model . they use a popular IR platform to Retrieve proper summary as candidate templates .
Outcome:	The proposed model outperforms state-of-the-art models in terms of informativeness and readability.

Where Did That Come From? Sentence-Level Error-Tolerant Attribution (2025.findings-emnlp)

Copied to clipboard

Ori Ernst, Aviv Slobodkin, Meng Cao, Sihui Wei, Jackie CK Cheung

Reasoning

Challenge:	Existing task definitions exclude unsupported or hallucinated content leaving them unattributed . authors propose a new definition for sentence-level error-tolerant attribution .
Approach:	They propose a new definition for sentence-level error-tolerant attribution that extends attribution to include incorrect or hallucinated content.
Outcome:	The proposed approach reduces annotation time and facilitates hallucination fixing.

K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters (2021.findings-acl)

Copied to clipboard

Ruize Wang, Duyu Tang, Nan Duan, Zhongyu Wei, Xuanjing Huang, Jianshu Ji, Guihong Cao, Daxin Jiang, Ming Zhou

Revisiting the Evaluation of End-to-end Event Extraction (2021.findings-acl)

Challenge:	Existing methods for injecting knowledge into pre-trained models are inconsistent and can flush out knowledge when multiple kinds of knowledge are injected.
Approach:	They propose a framework that retains the original parameters of pre-trained models fixed and supports the development of versatile knowledge-infused models.
Outcome:	The proposed framework retains the original parameters of the pre-trained model fixed and supports the development of versatile knowledge-infused models.

Copied to clipboard

Shun Zheng, Wei Cao, Wei Xu, Jiang Bian

MetaTS: Meta Teacher-Student Network for Multilingual Sequence Labeling with Minimal Supervision (2021.emnlp-main)

Challenge:	Existing EE research uses the role-averaged evaluation metric, but it is misleading to downstream applications.
Approach:	They propose two new evaluation metrics that explicitly penalize wrongly identified event arguments.
Outcome:	The proposed evaluation metrics improve the initial evaluation by 10% . the proposed training scheme is better than the existing one, the authors show .

Copied to clipboard

Zheng Li, Danqing Zhang, Tianyu Cao, Ying Wei, Yiwei Song, Bing Yin

O1-Pruner: Length-Harmonizing Fine-Tuning for O1-Like Reasoning Pruning (2026.findings-acl)

Challenge:	Sequence labeling aims to predict fine-grained sequences of labels for text, but lack of token-level annotated data hinders the effectiveness of supervised methods.
Approach:	They propose a Meta Teacher-Student (MetaTS) Network to alleviate data scarcity by leveraging large multilingual unlabeled data.
Outcome:	The proposed meta learning method alleviates data scarcity by leveraging large multilingual unlabeled data.

Copied to clipboard

Haotian Luo, Haiying He, Yibo Wang, Shiwei Liu, Wei Li, Xiaochun Cao, Dacheng Tao, Naiqiang Tan, Li Shen

Cross-lingual Feature Extraction from Monolingual Corpora for Low-resource Unsupervised Bilingual Lexicon Induction (2022.coling-1)

Challenge:	Recent long-thought reasoning models adopt extended reasoning processes similar to how humans ponder over complex problems.
Approach:	They propose a model that uses RL-style fine-tuning to reduce inference overhead while maintaining accuracy.
Outcome:	The proposed model reduces inference overhead while maintaining accuracy.

Copied to clipboard

Zihao Feng, Hailong Cao, Tiejun Zhao, Weixuan Wang, Wei Peng

Lexicon-Based Graph Convolutional Network for Chinese Word Segmentation (2021.findings-emnlp)

Challenge:	Unsupervised bilingual lexicon induction models fail on low-resource language pairs due to insufficient initialization.
Approach:	They propose a method to learn cross-lingual features from monolingual corpora for low-resource UBLI by integrating cross-linguistic representations with pre-trained word embeddings in a fully unsupervised initialization.
Outcome:	The proposed method outperforms state-of-the-art methods on low-resource language pairs and improves representational ability and robustness of existing embedding models.

Copied to clipboard

Kaiyu Huang, Hao Yu, Junpeng Liu, Wei Liu, Jingxiang Cao, Degen Huang

Syntactic Text Processing

Challenge:	Existing methods for Chinese word segmentation have high performance on benchmarks but are limited by the small-scale annotated corpus.
Approach:	They propose a framework that incorporates a lexicon-based graph convolutional network into the Transformer encoder to improve Chinese word segmentation (CWS) Chinese word is an essential and pre-processing step for many downstream NLP tasks.
Outcome:	The proposed framework captures the information of candidate words and improves performance on benchmarks and datasets.

DEIE: Benchmarking Document-level Event Information Extraction with a Large-scale Chinese News Dataset (2024.lrec-main)

Copied to clipboard

Yubing Ren, Yanan Cao, Hao Li, Yingjie Li, Zixuan ZM Ma, Fang Fang, Ping Guo, Wei Ma

Bridging the Domain Gaps in Context Representations for k-Nearest Neighbor Neural Machine Translation (2023.acl-long)

Challenge:	Existing event-based datasets mainly target sentence-level tasks . current models struggle with "document" annotation, a key feature of the current model .
Approach:	They propose a large-scale document-level event information extraction dataset with over 56,000+ events and 242,000+ arguments.
Outcome:	The proposed dataset has over 56,000+ events and 242,000+ arguments.

Copied to clipboard

Zhiwei Cao, Baosong Yang, Huan Lin, Suhang Wu, Xiangpeng Wei, Dayiheng Liu, Jun Xie, Min Zhang, Jinsong Su

Machine Translation

Challenge:	Existing methods to improve k-Nearest neighbor machine translation (kNN-MT) are based on the ability to non-parametrically adapt to new domains.
Approach:	They propose a method to boost the datastore retrieval of k-Nearest neighbor machine translation by reconstructing the original datastore.
Outcome:	The proposed method boosts the retrieval and translation quality of k-Nearest neighbor machine translation by reconstructing the original datastore.

Enabling Agents to Communicate Entirely in Latent Space (2026.acl-long)

Copied to clipboard

Zhuoyun Du, Runze Wang, Huiyu Bai, Zouying Cao, Xiaoyong Zhu, Yu Cheng, Bo Zheng, Wei Chen, Haochao Ying

Challenge:	Natural language is the de facto communication medium for LLM-based agents, but it presents a fundamental constraint . natural language downsampling limits the depth and nuance of information that can be transmitted . et al.: inter-agent latent space communication is a promising paradigm for solving complex tasks .
Approach:	They propose a paradigm that leverages the last hidden states of an LLM as a representation of its thought for direct communication.
Outcome:	The proposed paradigm outperforms fine-tuned chain-of-thought prompting and single-agent baselines even across heterogeneous models.

A Model-agnostic Data Manipulation Method for Persona-based Dialogue Generation (2022.acl-long)

Copied to clipboard

Yu Cao, Wei Bi, Meng Fang, Shuming Shi, Dacheng Tao

Perspective-driven Preference Optimization with Entropy Maximization for Diverse Argument Generation (2025.findings-emnlp)

Challenge:	Existing models for introducing explicit personas are expensive due to their expensive collection costs.
Approach:	They propose a data manipulation method which is model-agnostic to be packed with any persona-based dialogue generation model to improve their performance.
Outcome:	The proposed method is model-agnostic to be packed with any persona-based dialogue generation model to improve their performance.

Copied to clipboard

Yilin Cao, Ruike Zhang, Penghui Wei, Qingchao Kong, Wenji Mao

Reasoning

Challenge:	Argument generation with diverse perspectives is essential for fostering balanced discourse and mitigating bias.
Approach:	They propose a Perspective-aware Preference Optimization with Entropy Maximization framework for diverse argument generation.
Outcome:	The proposed framework enhances perspective diversity through preference optimization based on the constructed preference dataset .

CLIO: Role-interactive Multi-event Head Attention Network for Document-level Event Extraction (2022.coling-1)

Copied to clipboard

Yubing Ren, Yanan Cao, Fang Fang, Ping Guo, Zheng Lin, Wei Ma, Yi Liu

XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation (2020.emnlp-main)

Challenge:	Existing methods for document-level event extraction struggle due to two intrinsic challenges: nested arguments and multiple events.
Approach:	They propose a role-interactive multi-event head attention network to solve two challenges . they map different events to multiple subspaces and then determine whether the current event exists .
Outcome:	The proposed model improves on two widely used DEE datasets on the Internet.

Copied to clipboard

Yaobo Liang, Nan Duan, Yeyun Gong, Ning Wu, Fenfei Guo, Weizhen Qi, Ming Gong, Linjun Shou, Daxin Jiang, Guihong Cao, Xiaodong Fan, Ruofei Zhang, Rahul Agrawal, Edward Cui, Sining Wei, Taroon Bharti, Ying Qiao, Jiun-Hung Chen, Winnie Wu, Shuguang Liu, Fan Yang, Daniel Campos, Rangan Majumder, Ming Zhou

Plug-and-Play Data Module for Code RL: Adaptive Ambiguity Replay (2026.findings-acl)

Challenge:	XGLUE provides a benchmark dataset to train large-scale cross-lingual pre-trained models . XCLUE provides 11 diversified tasks that cover both understanding and generation scenarios .
Approach:	They introduce a new benchmark dataset to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora.
Outcome:	The proposed dataset is labeled in English and includes only natural language understanding tasks.

Copied to clipboard

Jianqing Zhang, Wei Xia, Zhezheng Hao, Hong Wang, Hande Dong, Qiang Lin, Yang Liu, Jian Cao, Qiang Yang

Attention Basin: Why Contextual Position Matters in Large Language Models (2026.acl-long)

Challenge:	Existing approaches to reinforcement learning (RL) rely on static, in-epoch metrics that overlook training dynamics, often introducing low-utility or outdated data.
Approach:	They propose a plug-and-play module that prioritizes cross-epoch ambiguous samples to neutralize the noise from stale experiences.
Outcome:	Extensive experiments on nine LLMs show that Adaptive Ambiguity Replay outperforms state-of-the-art baselines on real-world code editing tasks.

Copied to clipboard

Zihao Yi, Zhenqing Ling, Delong Zeng, Haohao Luo, Zhe Xu, Wei Liu, Jian Luan, Wanxia Cao, Ying Shen

EMGLLM: Data-to-Text Alignment for Electromyogram Diagnosis Generation with Medical Numerical Data Encoding (2025.findings-acl)

Challenge:	Large Language Models (LLMs) are sensitive to the contextual position of information in input.
Approach:	They introduce Attention-Driven Reranking (AttnRank) which estimates a model’s intrinsic positional attention preferences using a small calibration set and reorders retrieved documents or few-shot examples to align the most salient content with these high-attention positions.
Outcome:	Experiments on multi-hop QA and few-shot in-context learning tasks show that AttnRank achieves substantial improvements across 10 large language models of varying architectures and scales, without modifying model parameters or training procedures.

Copied to clipboard

Zefei Long, Zhenbiao Cao, Wei Chen, Zhongyu Wei

Structured Data in NLP

Challenge:	Existing Large Language Models struggle to interpret EMG tables . EMGLLM is a data-to-text model for medical examination tables based on electrical signals .
Approach:	They propose a data-to-text model that aligns EMG data into word embeddings that reflect health degree.
Outcome:	The proposed model outperforms baseline models in understanding EMG tables and generating high-quality diagnoses.

A + B: A General Generator-Reader Framework for Optimizing LLMs to Unleash Synergy Potential (2024.findings-acl)

Copied to clipboard

Wei Tang, Yixin Cao, Jiahao Ying, Bo Wang, Yuyue Zhao, Yong Liao, Peng Zhou

Information Retrieval

Challenge:	Retrieval-Augmented Generation (RAG) is an effective solution to supplement necessary knowledge to large language models.
Approach:	They propose a "generate-then-read" pipeline to replace retrieval stage with generation from the LLM itself.
Outcome:	The proposed framework outperforms single models in the base and chat versions and addresses safety and helpfulness post-adaptation challenges.

AFPQ: Asymmetric Floating Point Quantization for LLMs (2024.findings-acl)

Copied to clipboard

Yijia Zhang, Sicheng Zhang, Shijie Cao, DaYou Du, Jianyu Wei, Ting Cao, Ningyi Xu

BASS: Boosting Abstractive Summarization with Unified Semantic Graph (2021.acl-long)

Challenge:	Low-bit weight quantization can save memory and accelerate inference.
Approach:	They propose asymmetric FP quantization which sets separate scales for positive and negative values.
Outcome:	The proposed method leads to large accuracy improvements and can be easily plugged into other quantization methods, including GPTQ and AWQ, for better performance.

Copied to clipboard

Wenhao Wu, Wei Li, Xinyan Xiao, Jiachen Liu, Ziqiang Cao, Sujian Li, Hua Wu, Haifeng Wang

mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections (2022.emnlp-main)

Challenge:	Abstractive summarization for long-document or multi-document remains challenging for Seq2Seq as it does not analyze long-distance relations in text.
Approach:	They propose a framework for Boosting Abstractive Summarization based on a unified Semantic graph which aggregates co-referent phrases distributing across a long range of context and conveys rich relations between phrases.
Outcome:	The proposed framework improves document representation and summary generation process by leveraging the graph structure.

Copied to clipboard

Chenliang Li, Haiyang Xu, Junfeng Tian, Wei Wang, Ming Yan, Bin Bi, Jiabo Ye, He Chen, Guohai Xu, Zheng Cao, Ji Zhang, Songfang Huang, Fei Huang, Jingren Zhou, Luo Si

Visual Data in NLP

Challenge:	Existing pre-trained vision-language models suffer from inefficiency and linguistic signal overwhelmed by long visual sequences in cross-modal alignment.
Approach:	They propose a vision-language foundation model with cross-modal skip-connections that can be pre-trained end-to-end on large-scale image-text pairs with both discriminative and generative objectives.
Outcome:	The proposed model achieves state-of-the-art results on a wide range of vision-language downstream tasks, including image captioning, image-text retrieval, visual grounding and visual question answering.

GUI0: Self-Evolving Foundational GUI Agents in Super App Ecosystems (2026.acl-long)

Copied to clipboard

Xinyi Wang, Wei Dai, Kyle Qiao, Ke Wang, Peng Chen, Gang Cao, null Kangqin, Zhongpu Wang, Xiaode Zhang, Yanming Liu, Jihao Gu, Jingtao Xu, Gong Zhi

Responsible & Trustworthy NLP

Challenge:	Automated interaction with graphical user interfaces (GUIs) is central to general artificial intelligence, but remains challenging within Super App ecosystems.
Approach:	They propose a framework synergizing autonomous data synthesis with dual-agent co-evolution . GUI0 establishes a domain-aware foundation model via synthesized corpora and employs curriculum-driven reinforcement learning .
Outcome:	The proposed framework outperforms Gemini-2.5-Pro and Claude-4-Sonnet in the SuperAPP benchmark and has universal efficacy across base models.

Dialect-SQL: An Adaptive Framework for Bridging the Dialect Gap in Text-to-SQL (2025.emnlp-main)

Copied to clipboard

Jie Shi, Xi Cao, Bo Xu, Jiaqing Liang, Yanghua Xiao, Jia Chen, Peng Wang, Wei Wang

Programming Languages in NLP

Challenge:	Existing Text-to-SQL research focuses on specific database systems, limiting adaptability to different dialects.
Approach:	They propose a framework that employs Object Relational Mapping (ORM) code as an intermediate language to bridge this gap.
Outcome:	The proposed framework outperforms existing methods that generate SQL queries directly.

Bitnet.cpp: Efficient Edge Inference for Ternary LLMs (2025.acl-long)

Copied to clipboard

Jinheng Wang, Hansong Zhou, Ting Song, Shijie Cao, Yan Xia, Ting Cao, Jianyu Wei, Shuming Ma, Hongyu Wang, Furu Wei

Green & Sustainable NLP

Challenge:	1-bit large language models have spurred interest in ternary LLMs, but efficient edge inference is still scarce.
Approach:	They propose an inference system optimized for 1-bit large language models . they propose a new library that facilitates sub-2-bits-per-weight inference .
Outcome:	The proposed inference system achieves 6.25x speed increase over full-precision baselines and 2.32x over low-bit baselines.

FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models (2025.naacl-long)

Copied to clipboard

Xin Guo, Haotian Xia, Zhaowei Liu, Hanyang Cao, Zhi Yang, Zhiqiang Liu, Sizhe Wang, Jinyi Niu, Chuqi Wang, Yanhui Wang, Xiaolong Liang, Xiaoming Huang, Bing Zhu, Zhongyu Wei, Yun Chen, Weining Shen, Liwen Zhang

iPET: An Interactive Emotional Companion Dialogue System with LLM-Powered Virtual Pet World Simulation (2025.acl-demo)

Challenge:	Large language models have demonstrated outstanding performance in various natural language processing tasks, but their security capabilities in the financial domain have not been explored.
Approach:	They propose to use a benchmark to evaluate large language models' financial domain knowledge and practical abilities.
Outcome:	The proposed benchmark evaluates large language models' financial domain knowledge and practical abilities.

Copied to clipboard

Zheyong Xie, Shaosheng Cao, Zuozhu Liu, Zheyu Ye, Zihan Niu, Chonggang Lu, Tong Xu, Enhong Chen, Zhe Xu, Yao Hu, Wei Lu

QRMeM: Unleash the Length Limitation through Question then Reflection Memory Mechanism (2024.findings-emnlp)

Challenge:	Existing approaches to role-playing emotional companion products lack sustained personalization and contextual adaptability, limiting their effectiveness in real-world settings.
Approach:	They propose a virtual pet agent that can enhance user engagement through rich, dynamic pet behaviors and interactions tailored to individual preferences.
Outcome:	The proposed system has been deployed in a real-world, non-commercial product for 200 days and has demonstrated its effectiveness in practical applications.

Copied to clipboard

Bo Wang, Heyan Huang, Yixin Cao, Jiahao Ying, Wei Tang, Chong Feng

Question Answering

Challenge:	Existing methods for processing large textual content face insufficient adaptation to task-specific needs and missing multi-segmentation relationships.
Approach:	They propose a question then reflection memory mechanism which integrates a dual-structured memory pool and a structured graph guidance to facilitate a reflective trial-and-error approach for navigating and identifying relevant segments.
Outcome:	The proposed model achieves superior performance on multiple-choice questions and multi-doc QA.

EvoWiki: Evaluating LLMs on Evolving Knowledge (2025.acl-long)

Copied to clipboard

Wei Tang, Yixin Cao, Yang Deng, Jiahao Ying, Bo Wang, Yizhe Yang, Yuyue Zhao, Qi Zhang, Xuanjing Huang, Yu-Gang Jiang, Yong Liao

Challenge:	Existing knowledge evolution benchmarks are static and fail to capture the evolving nature of LLMs and knowledge.
Approach:	They propose an evolving dataset that categorizes information into stable, evolved, and uncharted states.
Outcome:	The proposed dataset is auto-updatable and enables evaluation of continuously changing knowledge and newly released LLMs.

EquiBench: Benchmarking Large Language Models’ Reasoning about Program Semantics via Equivalence Checking (2025.emnlp-main)

Copied to clipboard

Anjiang Wei, Jiannan Cao, Ran Li, Hongyu Chen, Yuhui Zhang, Ziheng Wang, Yuan Liu, Thiago S. F. X. Teixeira, Diyi Yang, Ke Wang, Alex Aiken

DPWriter: Reinforcement Learning with Diverse Planning Branching for Creative Writing (2026.acl-long)

Challenge:	EquiBench is a new benchmark to evaluate large language models' ability to reason about program semantics . Unlike natural language, code is executable.
Approach:	They propose a benchmark to evaluate large language models through equivalence checking . EquiBench consists of 2400 program pairs across four languages and six categories .
Outcome:	The proposed benchmark consists of 2400 program pairs across four languages and six categories.

Copied to clipboard

Qian Cao, Yahui Liu, Wei Bi, Yi Zhao, Ruihua Song, Xiting Wang, Ruiming Tang, Guorui Zhou, Han Li

Challenge:	Existing methods for enhancing large language models (LLMs) lack explicit mechanisms for guiding diverse exploration and instead prioritize efficiency and performance over diversity.
Approach:	They propose a reinforcement learning-based framework that decomposes the generation process into explicitly planned intermediate steps and introduces divergence at the planning phase based on diversity variation.
Outcome:	The proposed method significantly outperforms existing baselines on creative writing benchmarks on a semi-structured long chain-of-thought (CoT) it introduces divergence at the planning phase based on diversity variation, alongside a group-aware diversity reward to encourage distinct trajectories.

Subtle Signatures, Strong Shields: Advancing Robust and Imperceptible Watermarking in Large Language Models (2024.findings-acl)

Copied to clipboard

Yubing Ren, Ping Guo, Yanan Cao, Wei Ma

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

Challenge:	Large Language Models (LLMs) have led to an increase in AI-generated text on the Internet, presenting a crucial challenge to differentiate AI-created content from human-written text.
Approach:	They propose a novel approach to embed watermarks into LLMs that leverages token prior probabilities to improve detectability and maintain watermark imperceptibility.
Outcome:	The proposed method improves detectability and imperceptibility of watermarks by partitioning tokens into two distinct groups based on prior probabilities and employing tailored strategies for each group.

Copied to clipboard

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

iTAG: Inverse Design for Natural Text Generation with Accurate Causal Graph Annotations (2026.acl-long)

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.

Copied to clipboard

Wenshuo Wang, Boyu Cao, Nan Zhuang, Wei Li

Structured Data in NLP

Challenge:	Lack of causally annotated text data for use as ground truth hinders causal discovery . early template-based generation methods sacrifice text naturalness in exchange for high annotation costs .
Approach:	They propose a method which performs real-world concept assignment to nodes before converting causal graphs into text.
Outcome:	The proposed method shows high annotation accuracy and naturalness across extensive tests.

Mobile-R1: Towards Interactive Capability for VLM-Based Mobile Agent via Systematic Training (2026.acl-long)

Copied to clipboard

Jihao Gu, Qihang Ai, Yingyao Wang, Pi Bu, Jingxuan Xing, Yue Cao, Zekun Zhu, Wei Jiang, Ziming Wang, Yingxiu Zhao, Ming-Liang Zhang, Jun Song, Yuning Jiang, Bo Zheng