Papers by Tao Jiang

UnifiedMLLM: Enabling Unified Representation for Multi-modal Multi-tasks With Large Language Model (2025.findings-naacl)

Challenge:	Existing methods learned semantic representations with dual encoders or dual variational auto-encoders failed to capture the aligned semantics between question and answer.
Approach:	They propose to use two variational auto-encoders to generate questions with aligned answers and generating answers with align questions.
Outcome:	The proposed method outperforms the state-of-the-art answer retrieval method on SQuAD.

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Zhaowei Li, Wei Wang, YiQing Cai, Qi Xu, Pengyu Wang, Dong Zhang, Hang Song, Botian Jiang, Zhida Huang, Tao Wang

Risk Minimization for Zero-shot Sequence Labeling (2021.acl-long)

Challenge:	Representative models like LLaVA and MiniGPT-4 have great capabilities in various tasks.
Approach:	They propose a unified model to represent various multi-modal tasks using a single representation.
Outcome:	The proposed model outperforms existing models in a variety of tasks while maintaining generality and scalability.

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Zechuan Hu, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

Low-Resource NLP

Challenge:	Existing approaches to zero-shot sequence labeling are expensive and hard to obtain for lowresource languages/domains.
Approach:	They propose a framework for zero-shot sequence labeling with minimum risk training and a decomposable risk function that models the relations between predicted labels from the source models and the true labels.
Outcome:	The proposed framework outperforms state-of-the-art systems on 21 datasets.

The “Knowledge–Behavior Gap” in Cultural Taboo Safety of Large Language Models (2026.acl-long)

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Ying He, Sihang Jiang, Xingzhou Chen, Zhouhong Gu, Yiwei Gu, Minggui HE, Shimin Tao, null Mahongxia, Yanghua Xiao

Pre-training Cross-Modal Retrieval by Expansive Lexicon-Patch Alignment (2024.lrec-main)

Challenge:	Existing cultural benchmarks assess cultural knowledge or values biases, but ignore cultural taboos.
Approach:	They propose a benchmark to evaluate and improve the cultural taboo safety of large language models.
Outcome:	The proposed benchmark spans 77 countries and regions, and includes over 2,020 taboos.

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Yang Yiyuan, Guodong Long, Michael Blumenstein, Xiubo Geng, Chongyang Tao, Tao Shen, Daxin Jiang

LoRAMoE: Alleviating World Knowledge Forgetting in Large Language Models via MoE-Style Plugin (2024.acl-long)

Challenge:	Recent large-scale vision-language pre-training relies on image-text global alignment by contrastive learning and is further boosted by fine-grained alignment in a weakly contrastive manner for cross-modal retrieval.
Approach:	They propose expansive lexicon-patch alignment (ELA) to align image patches with a vocabulary rather than only the words explicitly in the text for annotation-free alignment and information augmentation.
Outcome:	The proposed method outperforms state-of-the-art methods on cross-modal retrieval and can learn representative fine-grained information.

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Shihan Dou, Enyu Zhou, Yan Liu, Songyang Gao, Wei Shen, Limao Xiong, Yuhao Zhou, Xiao Wang, Zhiheng Xi, Xiaoran Fan, Shiliang Pu, Jiang Zhu, Rui Zheng, Tao Gui, Qi Zhang, Xuanjing Huang

TexSmart: A System for Enhanced Natural Language Understanding (2021.acl-demo)

Challenge:	Experimental results show that, as the instruction data increases, LoRAMoE can significantly improve the ability to process downstream tasks, while maintaining the world knowledge stored in the LLM.
Approach:	They propose a framework that introduces several low-rank adapters and integrates them by using a router network to freeze the backbone model and force a portion of LoRAs to focus on leveraging world knowledge to solve downstream tasks.
Outcome:	The proposed framework freezes the backbone model and forces a portion of LoRAs to focus on leveraging world knowledge to solve downstream tasks, to alleviate world knowledge forgetting.

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Lemao Liu, Haisong Zhang, Haiyun Jiang, Yangming Li, Enbo Zhao, Kun Xu, Linfeng Song, Suncong Zheng, Botong Zhou, Dick Zhu, Xiao Feng, Tao Chen, Tao Yang, Dong Yu, Feng Zhang, ZhanHui Kang, Shuming Shi

LiteVL: Efficient Video-Language Learning with Enhanced Spatial-Temporal Modeling (2022.emnlp-main)

Challenge:	TexSmart supports fine-grained named entity recognition (NER) Large-scale fine-granular entity types are expected to provide richer semantic information for downstream NLP applications.
Approach:	They introduce TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities.
Outcome:	The proposed system supports fine-grained named entity recognition (NER) and enhanced semantic analysis functions.

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Dongsheng Chen, Chaofan Tao, Lu Hou, Lifeng Shang, Xin Jiang, Qun Liu

Dynamic Parallel Tree Search for Efficient LLM Reasoning (2025.acl-long)

Challenge:	Recent large-scale video-language pre-trained models have shown appealing performance on downstream tasks.
Approach:	They propose a video-text model that adapts a pre-trained image-language model into a text-based model without heavy pre-training.
Outcome:	The proposed model outperforms existing models on video-text retrieval and video question answering tasks without heavy pre-training.

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Yifu Ding, Wentao Jiang, Shunyu Liu, Yongcheng Jing, Jinyang Guo, Yingjie Wang, Jing Zhang, Zengmao Wang, Ziwei Liu, Bo Du, Xianglong Liu, Dacheng Tao

Rehearsal-free Continual Language Learning via Efficient Parameter Isolation (2023.acl-long)

Challenge:	Recent methods focus on search accuracy while overlooking computational efficiency.
Approach:	They propose a parallelism framework that dynamically optimizes reasoning path in inference.
Outcome:	The proposed framework improves efficiency by 2-4 on average while maintaining or even surpassing existing reasoning algorithms in accuracy.

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Zhicheng Wang, Yufang Liu, Tao Ji, Xiaoling Wang, Yuanbin Wu, Congcong Jiang, Ye Chao, Zhencong Han, Ling Wang, Xu Shao, Wenqiu Zeng

Mitigating Lost in Multi-turn Conversation via Curriculum RL with Verifiable Accuracy and Abstention Rewards (2026.acl-long)

Challenge:	Existing methods for learning continual tasks do not cache history data, which makes the problem more challenging.
Approach:	They propose a method that allocates a small portion of private parameters and learns them with a shared pre-trained model.
Outcome:	The proposed method is comparable to existing methods and comparable to those using historical data.

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Ming Li, Pei Chen, Zhenhao Zhang, Tao Yang, Xinyang Zhang, Han Li, Tianyu Cao, Ming Zeng, Zhuofeng Wu, Meng Jiang, Huasheng Li, Lihong Li, Bing Yin

RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement (2025.naacl-long)

Challenge:	Large Language Models exhibit strong capabilities in single-turn instruction following but suffer from Lost-in-Conversation (LiC) when instructions are revealed progressively in multi-turn settings, models get "Lost in Conversation"
Approach:	They propose a framework that encourages models to generate correct answers and judge solvability in multi-turn conversations.
Outcome:	The proposed framework improves models' ability to balance problem-solving with abstention . it reduces premature answering behaviors that cause lost-in-conversation (LiC)

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Jinhao Jiang, Jiayi Chen, Junyi Li, Ruiyang Ren, Shijie Wang, Xin Zhao, Yang Song, Tao Zhang

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

Challenge:	Existing large language models (LLMs) show exceptional problem-solving capabilities but struggle with complex reasoning tasks.
Approach:	They propose a novel RAG approach that integrates retrieved information to guide tree-based reasoning process based on LLMs.
Outcome:	The proposed approach outperforms existing methods in large language models . iteratively plans intermediate sub-queries and answers based on the LLM itself .

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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

AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling (2024.acl-long)

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.

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Jun Zhan, Junqi Dai, Jiasheng Ye, Yunhua Zhou, Dong Zhang, Zhigeng Liu, Xin Zhang, Ruibin Yuan, Ge Zhang, Linyang Li, Hang Yan, Jie Fu, Tao Gui, Tianxiang Sun, Yu-Gang Jiang, Xipeng Qiu

Structured Pruning for Efficient Generative Pre-trained Language Models (2023.findings-acl)

Challenge:	Existing language models that use discrete representations for unified processing of various modalities are limited to text generation and do not include multimodal output.
Approach:	They propose a multimodal language model that utilizes discrete representations for unified processing of various modalities.
Outcome:	The proposed model can be trained stably without any alterations to existing models or training paradigms.

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Chaofan Tao, Lu Hou, Haoli Bai, Jiansheng Wei, Xin Jiang, Qun Liu, Ping Luo, Ngai Wong

Towards General Agentic Intelligence via Environment Scaling (2026.findings-acl)

Challenge:	Large-scale generative Pre-trained Language Models (PLMs) are limited in their deployment in real-world applications.
Approach:	They propose to prune the feed-forward networks of generative pre-trained language models to smaller widths without designing extra operators.
Outcome:	The proposed method achieves 1.51x/6.96x inference speedup on GPU/CPU with 67% size reduction.

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Runnan Fang, Shihao Cai, Baixuan Li, Jialong Wu, Guangyu Li, Wenbiao Yin, Xinyu Wang, Xiaobin Wang, Liangcai Su, Zhen Zhang, Shibin Wu, Zhengwei Tao, Yong Jiang, Pengjun Xie, Ningyu Zhang, Fei Huang, Wentao Zhang, Jingren Zhou

ADAM: Dense Retrieval Distillation with Adaptive Dark Examples (2024.findings-acl)

Challenge:	Diverse real-world APIs require precise, robust function-calling intelligence, which needs agents to develop these capabilities through interaction in varied environments.
Approach:	They propose a framework that scales up environments to enable agentic intelligence . they use a two-phase agent fine-tuning strategy to first endow agents with basic agentic capabilities, then specializing them for domain-specific contexts.
Outcome:	Experiments on -bench, -Bench, and ACEBench show that the model significantly enhances the models’ function-calling capability.

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Chongyang Tao, Chang Liu, Tao Shen, Can Xu, Xiubo Geng, Binxing Jiao, Daxin Jiang

PFDial: A Structured Dialogue Instruction Fine-tuning Method Based on UML Flowcharts (2025.findings-acl)

Challenge:	Existing methods to retrieve data from multiple encoders are too trivial for the teacher to distinguish, preventing the teacher from transferring abundant dark knowledge to the student.
Approach:	They propose a knowledge distillation framework that can better transfer the dark knowledge held in the teacher with adaptive dark examples.
Outcome:	The proposed framework can better transfer the dark knowledge held in the teacher with adaptive dark examples.

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Ming Zhang, Yuhui Wang, Yujiong Shen, Tingyi Yang, Changhao Jiang, Yilong Wu, Shihan Dou, Qinhao Chen, Zhiheng Xi, Zhihao Zhang, Yi Dong, Zhen Wang, Zhihui Fei, Mingyang Wan, Tao Liang, Guojun Ma, Qi Zhang, Tao Gui, Xuanjing Huang

Word Reordering for Zero-shot Cross-lingual Structured Prediction (2021.emnlp-main)

Challenge:	Large Language Models (LLMs) have shown remarkable progress in dialogue and reasoning, but they struggle to solve strictly constrained dialogue tasks.
Approach:	They construct a dataset that contains 12,705 high-quality Chinese dialogue instructions from 440 flowcharts containing 5,055 process nodes.
Outcome:	The proposed model outperforms GPT-4o models on backward transitions and outperformed GPT-42 models on the same dataset.

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Tao Ji, Yong Jiang, Tao Wang, Zhongqiang Huang, Fei Huang, Yuanbin Wu, Xiaoling Wang

Low-Resource NLP

Challenge:	Current sentence encoders are word order sensitive, resulting in poor performance . Adapting word order from one language to another is key in cross-lingual structured prediction.
Approach:	They propose a new module to organize words following the source language order . they build structured prediction models with bag-of-words inputs and introduce a module to do this .
Outcome:	The proposed model significantly improves target language performance for languages that are distant from the source language.

Benchmarking Large Language Models Under Data Contamination: A Survey from Static to Dynamic Evaluation (2025.emnlp-main)

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Simin Chen, Yiming Chen, Zexin Li, Yifan Jiang, Zhongwei Wan, Yixin He, Dezhi Ran, Tianle Gu, Haizhou Li, Tao Xie, Baishakhi Ray

Compression of Generative Pre-trained Language Models via Quantization (2022.acl-long)

Challenge:	In the era of evaluating large language models, data contamination is an increasingly prominent concern . static benchmarking has been used for evaluation, but there are limitations of dynamic benchmarks .
Approach:	They propose a series of optimal design principles for dynamic benchmarking and analyze the limitations of existing static benchmarks.
Outcome:	The proposed benchmarks highlight a critical gap in the evaluation of LLMs.

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Chaofan Tao, Lu Hou, Wei Zhang, Lifeng Shang, Xin Jiang, Qun Liu, Ping Luo, Ngai Wong

DeReA: Improving Idiom Translation with Detect-Retrieve-Arbitrate Reasoning (2026.acl-long)

Challenge:	Existing methods to compress generative pre-trained language models fail on generative tasks due to homogeneous word embeddings and limited memory.
Approach:	They propose a token-level contrastive distillation method to learn distinguishable word embeddings and a module-wise dynamic scaling method to make quantizers adaptive to different modules.
Outcome:	The proposed method outperforms the state-of-the-art compression methods on generative PLMs by a clear margin.

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Rongqing Jiang, Xuebo Liu, Shengxin Liu, Yutong Wang, Min Zhang, Shimin Tao, Daimeng Wei, Min Zhang

Machine Translation

Challenge:	Existing approaches to idiom translation are limited by the constraints of static parametric memory and retrieval noise . idiomatic expressions are non-compositional units where figurative meanings diverge from literal interpretations .
Approach:	They propose a detect-retrieve-arbitrate framework that detects idiomatic spans by reasoning over semantic conflicts between literal and contextual meanings.
Outcome:	The proposed framework improves GPT-5-mini and Emerging Slang datasets on various model scales.

AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments (2025.acl-long)

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Zhiheng Xi, Yiwen Ding, Wenxiang Chen, Boyang Hong, Honglin Guo, Junzhe Wang, Xin Guo, Dingwen Yang, Chenyang Liao, Wei He, Songyang Gao, Lu Chen, Rui Zheng, Yicheng Zou, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang

PCL: Peer-Contrastive Learning with Diverse Augmentations for Unsupervised Sentence Embeddings (2022.emnlp-main)

Challenge:	Large language models (LLMs) are promising foundations to build generally-capable agents . however, the community lacks a unified interactive framework that covers diverse environments for comprehensive evaluation of agents.
Approach:	They propose a framework that features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction.
Outcome:	The proposed framework features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction.

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Qiyu Wu, Chongyang Tao, Tao Shen, Can Xu, Xiubo Geng, Daxin Jiang

Synergistic Interplay between Search and Large Language Models for Information Retrieval (2024.acl-long)

Challenge:	Existing approaches to learning sentence embeddings in unsupervised manner depend on mono-augmenting . existing approaches depend on augmenting biases and thus corrupt the quality of sentence embeds.
Approach:	They propose a method to augment a sentence with a semantically-close positive instance to construct contrastive pairs in unsupervised manner.
Outcome:	The proposed method improves performance on STS benchmarks and compares with existing methods.

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Jiazhan Feng, Chongyang Tao, Xiubo Geng, Tao Shen, Can Xu, Guodong Long, Dongyan Zhao, Daxin Jiang

ARM: An Alignment-and-Replacement Module for Chinese Spelling Check Based on LLMs (2024.emnlp-main)

Challenge:	Information retrieval (IR) is an indispensable technique for locating relevant resources from vast amounts of data.
Approach:	They propose a framework that facilitates information refinement through synergy between RMs and LLMs.
Outcome:	The proposed framework improves the performance of large-scale retrieval benchmarks on web searches and low-resource retrieval tasks.

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Changchun Liu, Kai Zhang, Junzhe Jiang, Zirui Liu, Hanqing Tao, Min Gao, Enhong Chen

Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning (2021.acl-long)

Challenge:	Chinese Spelling Check (CSC) aims to identify and correct spelling errors in Chinese texts, where enhanced semantic understanding of a sentence can significantly improve correction accuracy.
Approach:	They propose a plug-and-play Alignment-and -Replacement module that enhances existing Chinese CSC models without retraining or fine-tuning.
Outcome:	The proposed module improves existing models while reducing retraining and fine-tuning.

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Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

Improving Large Language Models Function Calling and Interpretability via Guided-Structured Templates (2025.emnlp-main)

Challenge:	Recent work shows document-level contexts can significantly improve Named Entity Recognition models.
Approach:	They propose to find external contexts of a sentence by retrieving and selecting a set of semantically relevant texts through a search engine with the original sentence as the query.
Outcome:	The proposed approach can achieve new state-of-the-art performance on 8 NER data sets across 5 domains.

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Hy Dang, Tianyi Liu, Zhuofeng Wu, Jingfeng Yang, Haoming Jiang, Tao Yang, Pei Chen, Zhengyang Wang, Helen Wang, Huasheng Li, Bing Yin, Meng Jiang

LLMEval-Fair: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models (2026.acl-long)

Challenge:	Large language models (LLMs) have strong reasoning and tool-use capabilities, yet fail in real-world tool-interactions due to incorrect parameterization, poor tool selection, or misinterpretation of user intent.
Approach:	They propose a curriculum-inspired framework that leverages structured reasoning templates to guide LLMs through more deliberate step-by-step instructions for generating function calls.
Outcome:	The proposed framework reduces tool-use errors and improves interpretability and transparency of tool-using agents.

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Ming Zhang, Yujiong Shen, Jingyi Deng, Yuhui Wang, Huayu Sha, Kexin Tan, Qiyuan Peng, Yue Zhang, Junzhe Wang, Shichun Liu, Yueyuan Huang, Jingqi Tong, Changhao Jiang, Yilong Wu, Zhihao Zhang, Mingqi Wu, Mingxu Chai, Zhiheng Xi, Shihan Dou, Tao Gui, Qi Zhang, Xuanjing Huang

ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification (2022.acl-long)

Challenge:	Existing evaluation of Large Language Models on static benchmarks is vulnerable to data contamination and leaderboard overfitting.
Approach:	LLMEval-Fair framework provides a framework for dynamic evaluation of Large Language Models . evaluators use a proprietary bank of 220k graduate-level questions to analyze model data .
Outcome:	LLMEval-Fair provides robust and credible evaluation framework for Large Language Models . it provides a strong empirical validation for the dynamic evaluation paradigm .

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Yucheng Zhou, Tao Shen, Xiubo Geng, Guodong Long, Daxin Jiang

Reinforcement Learning on Pre-Training Data (2026.acl-long)

Challenge:	Existing work on event-centric reasoning fails to model event-level correlations . Existing studies limit their scope to specific scenarios or overlook event- level correlations.
Approach:	They propose to pre-train a general Correlation-aware context-to-Event Transformer for event-centric reasoning by highlighting event-level correlations with effective training.
Outcome:	The proposed model is applicable to a wide range of event-centric reasoning scenarios, considering its versatility of event correlation types, application formulations, and reasoning types.

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Siheng Li, Kejiao Li, Zenan Xu, Guanhua Huang, Kun Li, Haoyuan Wu, null Wujiajia, Zihao Zheng, Chenchen Zhang, Kun Shi, Xue Gong, Qi Yi, Ruibin Xiong, Tingqiang Xu, Yuhao Jiang, Jianfeng Yan, Yuyuan Zeng, Guanghui Xu, Jinbao Xue, Zhijiang xu, Zheng Fang, Shuai LI, Qibin Liu, Xiaoxue Li, Zhuoyu Li, Yangyu Tao, Fei Gao, Cheng Jiang, Bochao Wang, Kai Liu, Jianchen Zhu, Wai Lam, Bo Zhou, Di Wang

Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base (D19-1)

Challenge:	Recent progress in large language models is driven by scaling of training compute through pre-training with nexttoken prediction (NTP) or post-training (RL) Pre-training using NTP enables models to acquire extensive knowledge and skills from general data, but it suffers from data inefficiency and catastrophic forgetting in continual learning settings.
Approach:	They propose to scale training compute through pre-training with next-token prediction (NTP) or post-training by scaling reinforcement learning (RL) to improve learning from general data.
Outcome:	Experiments on multiple benchmarks and models show that the proposed approach improves continual pre-training and provides a strong foundation for post-training on Qwen3-8B-Base.

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Tao Shen, Xiubo Geng, Tao Qin, Daya Guo, Duyu Tang, Nan Duan, Guodong Long, Daxin Jiang

CORE: Cooperative Training of Retriever-Reranker for Effective Dialogue Response Selection (2023.acl-long)

Challenge:	Recent approaches to handle large knowledge base decompose tasks into subtasks and solve them sequentially.
Approach:	They propose a multi-task learning framework that resolves coreference in conversations . they propose enabling shared supervisions and type-aware entity detection model .
Outcome:	The proposed framework improves overall F1 score from 67% to 79% on a large-scale conversational question answering dataset.

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Chongyang Tao, Jiazhan Feng, Tao Shen, Chang Liu, Juntao Li, Xiubo Geng, Daxin Jiang

EvoR: Evolving Retrieval for Code Generation (2024.findings-emnlp)

Challenge:	Existing methods to train retrieval-based dialogue systems are suboptimal . existing methods to optimize retrieval and rerank modules are sub-optimal, causing sub-optimum performance.
Approach:	They propose a retrieval-based dialogue system with a fast retriever and a smart response reranker that combine the best of both worlds.
Outcome:	The proposed method can learn from each other and evolve together . it can be used in industrial applications and has powered industrial applications.

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Hongjin Su, Shuyang Jiang, Yuhang Lai, Haoyuan Wu, Boao Shi, Che Liu, Qian Liu, Tao Yu

Text Generation

Challenge:	Existing pipelines for retrieval-augmented code generation (RACG) use static knowledge bases with a single source, limiting adaptation capabilities of Large Language Models (LLMs) Extensive experiments demonstrate that EVOR achieves two to four times of execution accuracy compared to other methods such as Reflexion.
Approach:	They propose a retrieval-augmented code generation pipeline that employs the synchronous evolution of queries and diverse knowledge bases.
Outcome:	The proposed pipeline achieves two to four times of execution accuracy compared to other methods.

Structural Knowledge Distillation: Tractably Distilling Information for Structured Predictor (2021.acl-long)

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Xinyu Wang, Yong Jiang, Zhaohui Yan, Zixia Jia, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

Social Norms-Grounded Machine Ethics in Complex Narrative Situation (2022.coling-1)

Challenge:	Knowledge distillation is a technique to transfer knowledge between models, typically from a large model (the teacher) to a more fine-grained one (the student).
Approach:	They propose a factorized form of the knowledge distillation objective for structured prediction which is tractable for many typical choices of the teacher and student models.
Outcome:	The proposed model is able to transfer knowledge between teacher and student models without loss of accuracy under four different scenarios.

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Tao Shen, Xiubo Geng, Daxin Jiang

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

Challenge:	Recent studies focus on data-driven methods to judge the ethics of complex real-world narratives but face two major challenges: they cannot handle dilemma situations due to a lack of basic knowledge about social norms; and they focus on sparse situation-level judgment regardless of the social norm.
Approach:	They propose to complement a complex situation with grounded social norms by a norm-supported ethical judgment model in line with neural module networks to alleviate dilemma situations and improve norm-level explainability.
Outcome:	The proposed model improves state-of-the-art performance on two narrative judgment benchmarks.

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

Nested Browser-Use Learning for Agentic Information Seeking (2026.acl-long)

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

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Baixuan Li, Jialong Wu, Wenbiao Yin, Kuan Li, Zhongwang Zhang, Huifeng Yin, Zhengwei Tao, Liwen Zhang, Pengjun Xie, Jingren Zhou, Yong Jiang, Wentao Zhang, Zhiqiang Gao

Challenge:	Existing information-seeking (IS) agents rely on the web for their information acquisition.
Approach:	They propose a browser-action framework that decouples interaction control from page exploration through a nested structure.
Outcome:	Empirical results show that NestBrowse offers clear benefits in practice.

Automated Concatenation of Embeddings for Structured Prediction (2021.acl-long)

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Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

Representation Learning

Challenge:	Recent work shows that better word representations can be obtained by concatenating different types of embeddings.
Approach:	They propose to automate the process of finding better concatenated embeddings for structured prediction tasks by concatending different types of embeddables.
Outcome:	The proposed approach outperforms baselines and achieves state-of-the-art with fine-tuned embeddings on 6 tasks and 21 datasets.

PromDA: Prompt-based Data Augmentation for Low-Resource NLU Tasks (2022.acl-long)

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Yufei Wang, Can Xu, Qingfeng Sun, Huang Hu, Chongyang Tao, Xiubo Geng, Daxin Jiang

Low-Resource NLP

Challenge:	Existing approaches to build labeled training data from domain-specific data are expensive to obtain.
Approach:	They propose a Prompt-based Data Augmentation model which only trains small-scale Soft Promptes in frozen Pre-trained Language Models.
Outcome:	The proposed model outperforms several baseline models on four benchmarks and is complementary with unlabeled in-domain data.

FAA: Fine-grained Attention Alignment for Cascade Document Ranking (2023.acl-long)

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Zhen Li, Chongyang Tao, Jiazhan Feng, Tao Shen, Dongyan Zhao, Xiubo Geng, Daxin Jiang

Information Retrieval

Challenge:	Contemporary document ranking methods focus on transforming documents into passages to handle long inputs, but intensive query-irrelevant content may lead to harmful distraction and high query latency.
Approach:	They propose a fine-grained attention alignment approach to jointly optimize a cascade document ranking model.
Outcome:	Experiments on MS MARCO and TREC DL show that the proposed method is effective in document ranking tasks.

Multi-View Cross-Lingual Structured Prediction with Minimum Supervision (2021.acl-long)

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Zechuan Hu, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

Multilinguality

Challenge:	Existing work on cross-lingual transfer learning focuses on transferring knowledge from high-resource languages to low-resourced ones.
Approach:	They propose a multi-view framework that integrates multiple source models into an aggregated source view and transfers it to a target view based on a task-specific model.
Outcome:	The proposed framework improves on three structured prediction tasks on 16 datasets.

Towards Interpretable Reasoning over Paragraph Effects in Situation (2020.emnlp-main)

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Mucheng Ren, Xiubo Geng, Tao Qin, Heyan Huang, Daxin Jiang

SDAR-VL: Stable and Efficient Block-wise Diffusion for Vision-Language Understanding (2026.acl-long)

Challenge:	Existing models ignore complex reasoning process and solve it with a one-step "black box" approach.
Approach:	They propose a sequential approach which explicitly models each step of the reasoning process with neural network modules.
Outcome:	The proposed model is more interpretable and more accurate than existing models.

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Shuang Cheng, Yuhua Jiang, Zineng Zhou, Dawei Liu, Tao Wang, Linfeng Zhang, Biqing Qi, Bowen Zhou

A Unified Encoding of Structures in Transition Systems (2021.emnlp-main)

Challenge:	Existing block-wise discrete diffusion models lack robust autoregressive (AR) decoders.
Approach:	They propose a block-wise discrete diffusion framework for large-scale vision-language understanding with a progressive beta noise curriculum.
Outcome:	The proposed framework improves training efficiency, convergence stability, and task performance over conventional block diffusion.

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Tao Ji, Yong Jiang, Tao Wang, Zhongqiang Huang, Fei Huang, Yuanbin Wu, Xiaoling Wang

Hierarchical Relation-Guided Type-Sentence Alignment for Long-Tail Relation Extraction with Distant Supervision (2022.findings-naacl)

Challenge:	Existing approaches to encode dynamic structures only encode partial information of structures.
Approach:	They propose a new attention-based encoder unifying all structures in a transition system.
Outcome:	The proposed method significantly improves the test speed and achieves the best transition-based model.

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Yang Li, Guodong Long, Tao Shen, Jing Jiang

HeterMPC: A Heterogeneous Graph Neural Network for Response Generation in Multi-Party Conversations (2022.acl-long)

Challenge:	Distant supervision uses triple facts to label corpus for relation extraction, leading to wrong labeling and long-tail problems.
Approach:	They propose a model to enrich distantly-supervised sentences with entity types by injecting context-free and -related backgrounds into sentences to alleviate sentence-level wrong labeling.
Outcome:	The proposed model achieves state-of-the-art on benchmarks and in overall and long-tail performance.

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Jia-Chen Gu, Chao-Hong Tan, Chongyang Tao, Zhen-Hua Ling, Huang Hu, Xiubo Geng, Daxin Jiang

Improving Long-Tail Relation Extraction with Collaborating Relation-Augmented Attention (2020.coling-main)

Challenge:	Experimental results show that HeterMPC outperforms various baseline models for response generation in multi-party conversations.
Approach:	They propose a heterogeneous graph-based neural network for response generation in multi-party conversations which models the semantics of utterances and interlocutors simultaneously with two types of nodes in a graph.
Outcome:	The proposed model outperforms baseline models on the Ubuntu Internet Relay Chat (IRC) channel.

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Yang Li, Tao Shen, Guodong Long, Jing Jiang, Tianyi Zhou, Chengqi Zhang

FaD-VLP: Fashion Vision-and-Language Pre-training towards Unified Retrieval and Captioning (2022.emnlp-main)

Challenge:	Existing approaches to handle wrong labeling and long-tail relations are labor-intensive and scarce training data.
Approach:	They propose a neural network to handle wrong labeling and long-tail relations by collaborating relation-augmented attention.
Outcome:	The proposed neural network improves the state-of-the-art on the NYT dataset .

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Suvir Mirchandani, Licheng Yu, Mengjiao Wang, Animesh Sinha, Wenwen Jiang, Tao Xiang, Ning Zhang

Modeling Event-Pair Relations in External Knowledge Graphs for Script Reasoning (2021.findings-acl)

Challenge:	Prior work on multimodal fashion tasks has been limited by the data in individual benchmarks or has leveraged generic vision-and-language pre-training but have not taken advantage of the characteristics of fashion data.
Approach:	They propose a fashion-specific pre-training framework based on weakly-supervised triplets constructed from fashion image-text pairs.
Outcome:	The proposed framework is based on weakly-supervised triplets constructed from fashion image-text pairs and is competitive on a diverse set of fashion tasks.

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Yucheng Zhou, Xiubo Geng, Tao Shen, Jian Pei, Wenqiang Zhang, Daxin Jiang

Knowledge Representation

Challenge:	Existing methods focus on graph triples with event overlap, but ignore more supportive triples . Script reasoning relies on understanding the relationship between two events .
Approach:	They propose a model to learn the inferential relations between events from the whole eventuality KG . they propose 'script adapter' to extend the model to infer the associated relations between an event chain and a subsequent event candidate.
Outcome:	The proposed model is compared with baselines using external KG or not on a script reasoning task.

TegTok: Augmenting Text Generation via Task-specific and Open-world Knowledge (2022.findings-acl)

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Chao-Hong Tan, Jia-Chen Gu, Chongyang Tao, Zhen-Hua Ling, Can Xu, Huang Hu, Xiubo Geng, Daxin Jiang

ITA: Image-Text Alignments for Multi-Modal Named Entity Recognition (2022.naacl-main)

Challenge:	Generating natural and informative texts has been a long-standing problem in NLP.
Approach:	They propose to augment TExt Generation via Task-specific and Open-world Knowledge in a unified framework.
Outcome:	The proposed model can learn what and how to generate on two text generation tasks.

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Xinyu Wang, Min Gui, Yong Jiang, Zixia Jia, Nguyen Bach, Tao Wang, Zhongqiang Huang, Kewei Tu

Tensorized Self-Attention: Efficiently Modeling Pairwise and Global Dependencies Together (N19-1)

Challenge:	Recent work on Multi-modal Named Entity Recognition (MNER) relies on image information to model interactions between image and text representations.
Approach:	They propose to align image features into the textual space to better utilize attention mechanisms . they use regional object tags, captions and optical characters as visual contexts .
Outcome:	The proposed model can achieve state-of-the-art accuracy on multi-modal Named Entity Recognition datasets even without image information.

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Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang

LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation (2025.findings-emnlp)

Challenge:	Neural networks equipped with self-attention have parallelizable computation and the ability to capture both long-range and local dependencies.
Approach:	They propose a novel attention mechanism called "Multi-mask Tensorized Self-Attention" it captures pairwise and global dependencies by a compatibility function composed of dot-product and additive attentions .
Outcome:	The proposed model outperforms CNN-/RNN-/attention-based models on nine NLP benchmarks with compelling memory- and time-efficiency.

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Ming Zhang, Yujiong Shen, Zelin Li, Huayu Sha, Binze Hu, Yuhui Wang, Chenhao Huang, Shichun Liu, Jingqi Tong, Changhao Jiang, Mingxu Chai, Zhiheng Xi, Shihan Dou, Tao Gui, Qi Zhang, Xuanjing Huang

TCSinger 2: Customizable Multilingual Zero-shot Singing Voice Synthesis (2025.findings-acl)

Challenge:	Current medical benchmarks have limitations in question design, data sources and evaluation methods.
Approach:	They propose a new benchmark covering five core medical areas . it includes 2,996 questions created from real-world electronic health records .
Outcome:	The proposed model covers five core medical areas and includes 2,996 questions created from real-world electronic health records and expert-designed clinical scenarios.

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Yu Zhang, Wenxiang Guo, Changhao Pan, Dongyu Yao, Zhiyuan Zhu, Ziyue Jiang, Yuhan Wang, Tao Jin, Zhou Zhao

Structure-Level Knowledge Distillation For Multilingual Sequence Labeling (2020.acl-main)

Challenge:	Existing zero-shot singing voice synthesis models depend on phoneme and note boundary annotations, limiting their robustness and producing poor transitions between phonemes and notes.
Approach:	They propose a multi-task multilingual zero-shot SVS model with style transfer and style control based on various prompts.
Outcome:	Experimental results show that TCSinger 2 outperforms baseline models in subjective and objective metrics across multiple related tasks.

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Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Fei Huang, Kewei Tu

Retrieval-Augmented Retrieval: Large Language Models are Strong Zero-Shot Retriever (2024.findings-acl)

Challenge:	Existing multilingual models still underperform individual monolingual models due to model capacity limitations.
Approach:	They propose to distill the structural knowledge of several monolingual models (teachers) to the unified multilingual model (student).
Outcome:	The proposed model outperforms strong baseline models and teacher models on 4 multilingual tasks with 25 datasets and has stronger zero-shot generalizability.

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Tao Shen, Guodong Long, Xiubo Geng, Chongyang Tao, Yibin Lei, Tianyi Zhou, Michael Blumenstein, Daxin Jiang

MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations (2021.emnlp-main)

Challenge:	Large-scale retrieval is indispensable in information-seeking tasks such as open-domain question answering and knowledgegrounded dialogue.
Approach:	They propose to use a large language model (LLM) to augment a query with its potential answers by prompting LLMs with a composition of the query and the query’s in-domain candidates.
Outcome:	The proposed method breaks brute-force combinations of retrievers with LLMs and lifts the performance of zero-shot retrieval to be very competitive on benchmark datasets.

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Xinyin Ma, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Weiming Lu

Representation Learning

Challenge:	Recent advances in entity retrieval ignore the property that meanings of entity mentions diverge in different contexts and are related to various portions of descriptions.
Approach:	They propose a novel approach that constructs multi-view representations for entity descriptions and approximates the optimal view for mentions via a heuristic searching method.
Outcome:	The proposed approach achieves state-of-the-art performance on ZESHEL and improves quality of candidates on three standard Entity Linking datasets.

Digest the Knowledge: Large Language Models empowered Message Passing for Knowledge Graph Question Answering (2025.acl-long)

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Junhong Wan, Tao Yu, Kunyu Jiang, Yao Fu, Weihao Jiang, Jiang Zhu

MIE: A Medical Information Extractor towards Medical Dialogues (2020.acl-main)

Challenge:	Existing methods to augment large language models (LLMs) with external knowledge are unorganized and unorganized.
Approach:	They propose a method that learns a concise facts graph and encodes it into multi-level lists of texts to augment LLMs.
Outcome:	The proposed method improves on all 5 knowledge graph question answering datasets and offers human-level semantic explainability.

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Yuanzhe Zhang, Zhongtao Jiang, Tao Zhang, Shiwan Liu, Jiarun Cao, Kang Liu, Shengping Liu, Jun Zhao

More Embeddings, Better Sequence Labelers? (2020.findings-emnlp)

Challenge:	EMRs are important but many doctors suffer from writing them, which is time-consuming and tedious.
Approach:	They propose an automatic conversion of medical dialogues to EMRs using a window-sliding style . they propose a medical information extractor (MIE) that extracts medical information from medical dialogue .
Outcome:	The proposed model extracts medical information from doctor-patient dialogues.

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Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

CCPrefix: Counterfactual Contrastive Prefix-Tuning for Many-Class Classification (2024.eacl-long)

Challenge:	Existing work suggests contextual embeddings improve sequence labeling accuracy . but, there is no definite conclusion on whether concatenating different kinds of embeddables is effective .
Approach:	They propose a family of contextual embeddings that improves sequence labeling accuracy . they conduct extensive experiments on 3 tasks over 18 datasets and 8 languages .
Outcome:	The proposed family of contextual embeddings improves the accuracy of sequence labelers over non-contextual embedders.

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Yang Li, Canran Xu, Guodong Long, Tao Shen, Chongyang Tao, Jing Jiang

Improving Zero-Shot Cross-lingual Transfer for Multilingual Question Answering over Knowledge Graph (2021.naacl-main)

Challenge:	Existing methods to improve pre-trained language models for many-class classification suffer from verbalizer ambiguity . a significant disparity exists between the pre-training and fine-tuning stages of the model .
Approach:	They propose a method to tune pre-trained language models to a broad spectrum of tasks . they use an instance-dependent soft prefix to complement language verbalizers in many-class classification .
Outcome:	The proposed method outperforms baselines on many-class datasets.

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Yucheng Zhou, Xiubo Geng, Tao Shen, Wenqiang Zhang, Daxin Jiang

Trust Within? Seek Beyond? Knowledge Boundary Aware Policy Optimization for Agentic Search (2026.acl-long)

Challenge:	Existing approaches to solve multilingual question answering over knowledge graph (KGQA) use of bilingual lexicon induction to map training questions into those in target language circumvents language inconsistency .
Approach:	They propose to use bilingual lexicon induction to map training questions in source and target languages as augmented training data to minimize syntax-disorder.
Outcome:	The proposed model narrows the gap in zero-shot cross-lingual transfer between source and target languages.

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Tao Feng, Xinke Jiang, Xinyan Hu, Yonggang Zhang, Zhen Tao, Wentao Zhang, Boyang Liu, Wenhao Jiang, Chao Wu

Maria: A Visual Experience Powered Conversational Agent (2021.acl-long)

Challenge:	Existing approaches to augment large language models with external knowledge suffer from a lack of calibration regarding the model’s knowledge boundary.
Approach:	They propose a reinforcement learning framework that explicitly aligns retrieval decisions with quantified knowledge states.
Outcome:	The proposed framework outperforms strong baselines while exhibiting reduced hallucination rates.

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Zujie Liang, Huang Hu, Can Xu, Chongyang Tao, Xiubo Geng, Yining Chen, Fan Liang, Daxin Jiang

An Investigation of Potential Function Designs for Neural CRF (2020.findings-emnlp)

Challenge:	Existing studies focus on grounding conversational agents on text-only corpora, but they lack the perception ability to our physical world.
Approach:	They propose to ground conversational agents on images retrieved from large-scale image indexes . they propose to use visual knowledge to generate informative responses based on the extracted knowledge .
Outcome:	The proposed agent outperforms state-of-the-art methods on automatic metrics and human evaluation.

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Zechuan Hu, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

Representation Learning

Challenge:	Existing approaches to sequence labeling are based on the neural linear-chain CRF model.
Approach:	They propose a series of increasingly expressive potential functions for neural CRF models that integrate emission and transition functions and explicitly take contextual words as input.
Outcome:	The proposed model consistently achieves the best performance on the decomposed quadrilinear potential function based on the representations of two neighboring labels and two neighbored words.

MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding (2021.acl-long)

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Jia-Chen Gu, Chongyang Tao, Zhenhua Ling, Can Xu, Xiubo Geng, Daxin Jiang

Learn Beyond The Answer: Training Language Models with Reflection for Mathematical Reasoning (2024.emnlp-main)

Challenge:	Existing models for multi-party conversation represent interlocutors and utterances individually . existing methods ignore complicated structure of MPC which may provide crucial interlocutor and tertiary semantics.
Approach:	They propose a pre-trained model for multi-party conversation that considers learning who says what to whom in a unified model with elaborated self-supervised tasks.
Outcome:	The proposed model outperforms existing models on three downstream tasks at two benchmarks.

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Zhihan Zhang, Tao Ge, Zhenwen Liang, Wenhao Yu, Dian Yu, Mengzhao Jia, Dong Yu, Meng Jiang

Towards Robust Ranker for Text Retrieval (2023.findings-acl)

Challenge:	Existing studies focus on broadening the training set with data augmentation techniques to maximize such benefits.
Approach:	They propose a method that embeds problem reflection into each training instance.
Outcome:	The proposed method enhances performance in standard and complex scenarios that require reflective thinking.

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Yucheng Zhou, Tao Shen, Xiubo Geng, Chongyang Tao, Can Xu, Guodong Long, Binxing Jiao, Daxin Jiang

OS Agents: A Survey on MLLM-based Agents for Computer, Phone and Browser Use (2025.acl-long)

Challenge:	Existing methods for text retrieval are based on a 'retrieval & rerank' pipeline, which uses a fast retriever to fetch a set of top document candidates, while a robust ranker is based upon a weak negative mining during contrastive learning.
Approach:	They propose a multi-adversarial training strategy that leverages multiple retrievers as generators to challenge a ranker.
Outcome:	The proposed model outperforms the existing de facto ranker training paradigms on the passage retrieval benchmarks using BM25-reranking, full-ranking and retriever distillation.

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Xueyu Hu, Tao Xiong, Biao Yi, Zishu Wei, Ruixuan Xiao, Yurun Chen, Jiasheng Ye, Meiling Tao, Xiangxin Zhou, Ziyu Zhao, Yuhuai Li, Shengze Xu, Shenzhi Wang, Xinchen Xu, Shuofei Qiao, Zhaokai Wang, Kun Kuang, Tieyong Zeng, Liang Wang, Jiwei Li, Yuchen Eleanor Jiang, Wangchunshu Zhou, Guoyin Wang, Keting Yin, Zhou Zhao, Hongxia Yang, Fan Wu, Shengyu Zhang, Fei Wu

Find Your Optimal Teacher: Personalized Data Synthesis via Router-Guided Multi-Teacher Distillation (2026.acl-long)

Challenge:	a new generation of (M)LLMs is enabling the creation of superintelligent AI assistants . OS Agents can complete tasks autonomously and have the potential to significantly enhance the lives of billions of users worldwide.
Approach:	They propose to build OS Agents that operate within operating systems' GUIs and GUIs . they examine evaluation metrics and benchmarks to identify promising directions .
Outcome:	The proposed agents are based on operating systems (OS) and operating systems frameworks.

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Hengyuan Zhang, Shiping Yang, Xiao Liang, Chenming Shang, Yuxuan Jiang, Chaofan Tao, Jing Xiong, Hayden Kwok-Hay So, Ruobing Xie, Angel X. Chang, Ngai Wong

Feedback-Driven Tool-Use Improvements in Large Language Models via Automated Build Environments (2026.findings-acl)

Challenge:	Existing studies show that stronger models are not always optimal teachers, suggesting a mismatch between the teacher’s output and the student’s learning ability.
Approach:	They propose a method that routes each prompt to its optimal teacher via a query-level router that jointly considers the student models’ learnability and teacher models’ response quality.
Outcome:	The proposed method outperforms baselines on six benchmarks including instruct tuning and math reasoning settings.

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Junjie Ye, Changhao Jiang, Zhengyin Du, Yufei Xu, Xuesong Yao, Zhiheng Xi, Xiaoran Fan, Qi Zhang, Tao Gui, Xuanjing Huang, Jiecao Chen

CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases (D19-1)

Challenge:	Currently, there are no efficient reinforcement learning (RL) frameworks specifically designed for tool use.
Approach:	They propose an automated environment construction pipeline that incorporates scenario decomposition, document generation, function integration, complexity scaling, and localized deployment to enable high-quality training environments without external tools.
Outcome:	The proposed framework significantly improves the models’ tool-use performance without degrading their general capabilities.

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Tao Yu, Rui Zhang, Heyang Er, Suyi Li, Eric Xue, Bo Pang, Xi Victoria Lin, Yi Chern Tan, Tianze Shi, Zihan Li, Youxuan Jiang, Michihiro Yasunaga, Sungrok Shim, Tao Chen, Alexander Fabbri, Zifan Li, Luyao Chen, Yuwen Zhang, Shreya Dixit, Vincent Zhang, Caiming Xiong, Richard Socher, Walter Lasecki, Dragomir Radev

Programming Languages in NLP

Challenge:	CoSQL is a corpus for building cross-domain, general-purpose database querying dialogue systems.
Approach:	They present a corpus for building cross-domain, general-purpose database querying dialogue systems . they use a Wizard-of-Oz collection of 3k turns plus 10k+ annotated SQL queries .
Outcome:	The proposed corpus is based on a Wizard-of-Oz dataset of 3k dialogues querying 200 complex DBs spanning 138 domains.

RatE: Relation-Adaptive Translating Embedding for Knowledge Graph Completion (2020.coling-main)

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Hao Huang, Guodong Long, Tao Shen, Jing Jiang, Chengqi Zhang

From Scores to Preferences: Redefining Evaluation Paradigm for Speech Quality Reward Modeling (2026.findings-acl)

Challenge:	Existing approaches for knowledge graph embedding have limitations in complex vector space . embeddability of one-to-many relations is not explicitly alleviated .
Approach:	They propose a relation-adaptive translating embedding function that can be extended to complex vector space.
Outcome:	The proposed translation function improves expressive power and alleviates embedding ambiguity problem.

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Yifei Cao, Changhao Jiang, Jiabao Zhuang, Jiajun Sun, Ming Zhang, Zhiheng Xi, Hui Li, Shihan Dou, Yuran Wang, Yunke Zhang, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang

AIN: Fast and Accurate Sequence Labeling with Approximate Inference Network (2020.emnlp-main)

Challenge:	Experimental results show that the MOS-aware GRM significantly improves fine-grained speech quality discrimination.
Approach:	They propose a MOS-aware reward model that incorporates MOS gap into reward function during reinforcement learning.
Outcome:	The proposed model significantly improves fine-grained speech quality discrimination.

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Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

Challenge:	Existing approaches to sequence labeling require sequential computation that makes parallelization impossible.
Approach:	They propose to employ a parallelizable approximate variational inference algorithm for the CRF model.
Outcome:	The proposed approach improves decoding speed and accuracy with long sentences and is parallelizable for faster training and prediction.

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization (2026.acl-long)

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Yihong Dong, Xue Jiang, Yongding Tao, Huanyu Liu, Kechi Zhang, Lili Mou, Rongyu Cao, Yingwei MA, Jue Chen, Binhua Li, Zhi Jin, Fei Huang, Yongbin Li, Ge Li

Beyond Scaling: Measuring and Predicting the Upper Bound of Knowledge Retention in Language Model Pre-Training (2026.acl-long)

Challenge:	Reinforcement Learning with Verifiable Reward (RLVR) has significantly advanced the complex reasoning abilities of Large Language Models (LLMs).
Approach:	They propose a hybrid-policy optimization approach that synergizes internal exploitation with external data to achieve stronger reasoning capabilities.
Outcome:	The proposed approach achieves state-of-the-art performance on six math reasoning benchmarks and superior performance on out-of distribution reasoning tasks.

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Changhao Jiang, Ming Zhang, Yifei Cao, Junjie Ye, Xiaoran Fan, Shihan Dou, Zhiheng Xi, Jiajun Sun, Yi Dong, Yujiong Shen, Jingqi Tong, Baoyu Fan, Tao Gui, Qi Zhang, Xuanjing Huang

KuiLeiXi: a Chinese Open-Ended Text Adventure Game (2021.acl-demo)

Challenge:	Existing methods to predict performance of large language models are lacking . authors propose a size-dependent mutual information predictor for closed-book question answering accuracy .
Approach:	They propose a size-dependent mutual information predictor that integrates knowledge frequency, knowledge specificity, and model size to forecast closed-book question answering accuracy.
Outcome:	The proposed method outperforms baseline models and achieves R2 > 0.7 in predicting QA accuracy without additional training.

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Yadong Xi, Xiaoxi Mao, Le Li, Lei Lin, Yanjiang Chen, Shuhan Yang, Xuhan Chen, Kailun Tao, Zhi Li, Gongzheng Li, Lin Jiang, Siyan Liu, Zeng Zhao, Minlie Huang, Changjie Fan, Zhipeng Hu

Neuron-Level Sequential Editing for Large Language Models (2025.acl-long)

Challenge:	Recent advances in pre-trained language models have made it possible to generate human-like text.
Approach:	They propose to integrate an open-ended text adventure game in Chinese, named KuiLeiXi, where players interact with the AI until the plot goals are reached.
Outcome:	The proposed game lacks incentives and relies on players to explore on their own.

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Houcheng Jiang, Junfeng Fang, Tianyu Zhang, Baolong Bi, An Zhang, Ruipeng Wang, Tao Liang, Xiang Wang