Papers by Yue Huang

TRAVEL: Tag-Aware Conversational FAQ Retrieval via Reinforcement Learning (2023.emnlp-main)

Challenge:	Document-level event argument extraction (EAE) is a critical task in natural language processing.
Approach:	They propose an LLM-driven HiErarchical Rule Optimization framework that iteratively generates and selects optimal hierarchical rules.
Outcome:	The proposed framework outperforms few-shot supervised methods and outperformed state-of-the-art prompting baselines.

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Yue Chen, Dingnan Jin, Chen Huang, Jia Liu, Wenqiang Lei

Towards Faithful Dialogues via Focus Learning (2023.acl-long)

Challenge:	Existing methods aim to fully utilize the dynamic conversation context to enhance the semantic association between the user query and FAQ questions, but they are limited by noise and e.g., users may click questions they don't like, leading to inaccurate semantics modeling.
Approach:	They propose to introduce tags of FAQ questions to reduce noise in the conversation context and integrate them into a reinforcement learning framework to minimize the negative impact of irrelevant information.
Outcome:	The proposed method can eliminate irrelevant information and minimize negative impact of irrelevant information in the dynamic conversation context.

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Yifan Deng, Xingsheng Zhang, Heyan Huang, Yue Hu

Distilling Instruction-following Abilities of Large Language Models with Task-aware Curriculum Planning (2024.findings-emnlp)

Challenge:	Existing knowledge-grounded models rely on elaborate data engineering or increasing the model’s parameters ignoring to track the tokens that significantly influence losses, which is decisive for the optimization direction of the model in each iteration.
Approach:	They propose a novel learning approach that adjusts the contribution of each token to the optimization direction by directly scaling the corresponding objective loss.
Outcome:	The proposed approach achieves the new state-of-the-art results and generates more reliable responses while maintaining training stability.

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Yuanhao Yue, Chengyu Wang, Jun Huang, Peng Wang

HAF-RM: A Hybrid Alignment Framework for Reward Model Training (2025.acl-long)

Challenge:	Instruction tuning aims to align large language models (LLMs) with open-domain instructions and human-preferred responses.
Approach:	They propose a multi-round distillation framework that uses an oracle LLM to select instructions that are difficult for a student LLM.
Outcome:	The proposed framework outperforms large language models and user-tuned models on several widely recognized benchmarks and multiple student LLMs.

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Shujun Liu, Xiaoyu Shen, Yuhang Lai, Siyuan Wang, Shengbin Yue, Zengfeng Huang, Xuanjing Huang, Zhongyu Wei

TreeBoN: Enhancing Inference-Time Alignment with Speculative Tree-Search and Best-of-N Sampling (2025.findings-emnlp)

Challenge:	Recent studies have focused on enhancing reward models through data improvements, following the conventional training framework for reward models that directly optimizes the predicted rewards.
Approach:	They propose a hybrid alignment framework HAF-RM that incorporates additional constraint on token-level policy probabilities in addition to the reward score.
Outcome:	The proposed framework can supervise the internal preference model at the token level and optimize the mapping layer of the reward model at sequence level.

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Jiahao Qiu, Yifu Lu, Yifan Zeng, Jiacheng Guo, Jiayi Geng, Chenhao Zhu, Xinzhe Juan, Ling Yang, Huazheng Wang, Kaixuan Huang, Yue Wu, Mengdi Wang

Information Retrieval

Challenge:	Best-of-N (BoN) sampling generates multiple responses and selects the best one, achieving improved performance but with a high computational cost.
Approach:	They propose a framework that integrates a speculative tree-search strategy into Best-of-N (BoN) Sampling.
Outcome:	The proposed framework outperforms Best-of-N (BoN) sampling but has high computational cost . tree-search strategy reduces computational overhead while maintaining high output quality .

A Unified Span-Based Approach for Opinion Mining with Syntactic Constituents (2021.naacl-main)

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Qingrong Xia, Bo Zhang, Rui Wang, Zhenghua Li, Yue Zhang, Fei Huang, Luo Si, Min Zhang

Syntactic Text Processing

Challenge:	Existing methods for fine-grained opinion mining (OM) are based on span-based annotations, but they are not effective.
Approach:	They propose a unified span-based approach for the end-to-end OM setting using syntactic constituents and multi-task learning to integrate them into the proposed model.
Outcome:	The proposed approach achieves significant improvements over previous work on the MPQA 2.0 dataset and reduces the number of wrongly-predicted opinion expressions and roles.

Synergetic Event Understanding: A Collaborative Approach to Cross-Document Event Coreference Resolution with Large Language Models (2024.acl-long)

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Qingkai Min, Qipeng Guo, Xiangkun Hu, Songfang Huang, Zheng Zhang, Yue Zhang

Open Domain Event Extraction Using Neural Latent Variable Models (P19-1)

Challenge:	Existing approaches to cross-document event coreference resolution are prone to learning simple co-occurrences due to the complexity of contexts.
Approach:	They propose a collaborative approach to cross-document event coreference resolution that leverages both a universally capable LLM and a task-specific SLM.
Outcome:	The proposed approach surpasses the performance of both large and small language models individually, underscoring its effectiveness in diverse scenarios.

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Xiao Liu, Heyan Huang, Yue Zhang

Revisiting Data Reconstruction Attacks on Real-world Dataset for Federated Natural Language Understanding (2024.lrec-main)

Challenge:	Existing work on extracting events from news documents focuses on a set of pre-specified event types.
Approach:	They propose a latent variable neural model which is scalable to large corpus.
Outcome:	The proposed model performs better than the state-of-the-art method for event schema induction.

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Zhuo Zhang, Jintao Huang, Xiangjing Hu, Jingyuan Zhang, Yating Zhang, Hui Wang, Yue Yu, Qifan Wang, Lizhen Qu, Zenglin Xu

MiRAGeNews: Multimodal Realistic AI-Generated News Detection (2024.findings-emnlp)

Challenge:	Existing DRA methods fail to accurately recover the original text of real-world privacy data.
Approach:	They propose to use a real-world privacy dataset to examine the performance of federated learning (FL) methods.
Outcome:	The proposed method improves on a real-world privacy dataset and shows that the tokens within a recovery sentence are disordered and intertwined with tokens from other sentences in the same training batch.

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Runsheng Huang, Liam Dugan, Yue Yang, Chris Callison-Burch

1+1>2: Can Large Language Models Serve as Cross-Lingual Knowledge Aggregators? (2024.emnlp-main)

Challenge:	inflammatory “fake” news content is increasingly common, but it is also difficult to detect by humans.
Approach:	They propose a dataset of 12,500 high-quality real and AI-generated image-caption pairs from state-of-the-art generators to combat the spread of fake news.
Outcome:	The proposed dataset improves on image-caption pairs from out-of-domain image generators and news publishers.

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Yue Huang, Chenrui Fan, Yuan Li, Siyuan Wu, Tianyi Zhou, Xiangliang Zhang, Lichao Sun

STYLE: Improving Domain Transferability of Asking Clarification Questions in Large Language Model Powered Conversational Agents (2024.findings-acl)

Challenge:	Large Language Models (LLMs) have been recognized for their impressive capabilities in natural language processing (NLP).
Approach:	They propose a method to enhance the multilingual performance of Large Language Models by aggregating knowledge from diverse languages.
Outcome:	The proposed method reduces the performance disparity across languages and offers valuable insights for further exploration.

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Yue Chen, Chen Huang, Yang Deng, Wenqiang Lei, Dingnan Jin, Jia Liu, Tat-Seng Chua

CityEQA: A Hierarchical LLM Agent on Embodied Question Answering Benchmark in City Space (2025.emnlp-main)

Challenge:	Existing methods for addressing ambiguities in conversational search systems are one-size-fits-all and struggle to achieve effective domain transferability.
Approach:	They propose a method to provide search engines with strategies regarding when to ask clarification questions in a post-hoc manner.
Outcome:	The proposed method improves search performance 10% on four unseen domains.

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Yong Zhao, Kai Xu, Zhengqiu Zhu, Yue Hu, Zhiheng Zheng, Yingfeng Chen, Yatai Ji, Chen Gao, Yong Li, Jincai Huang

PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoning (2026.acl-long)

Challenge:	Embodied Question Answering (EQA) tasks are primarily focused on indoor environments, leaving the complexities of urban settings unexplored.
Approach:	They propose a task where an embodied agent answers open-vocabulary questions in dynamic city spaces.
Outcome:	The proposed agent achieves 60.7% of human-level answering accuracy compared to baselines . the proposed agent outperforms existing agents in open-ended city spaces .

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Jingcheng Hu, Yinmin Zhang, Shijie Shang, Xiaobo Yang, Yue Peng, Zhewei Huang, Hebin Zhou, Xin Wu, Jie Cheng, Fanqi Wan, Xiangwen Kong, Chengyuan Yao, Kaiwen Yan, Ailin Huang, Hongyu Zhou, Qi Han, Zheng Ge, Xiangyu Zhang, Heung-Yeung Shum

NaSGEC: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Texts (2023.findings-acl)

Challenge:	Parallel Coordinated Reasoning (PaCoRe) overcomes a central limitation of contemporary language models: their inability to scale test-time compute (TTC) far beyond sequential reasoning under a fixed context window.
Approach:	They propose a training-and-inference framework to overcome a central limitation of language models: their inability to scale test-time compute (TTC) under a fixed context window.
Outcome:	The proposed model scales to multi-million-token effective TTC without exceeding context limits.

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Yue Zhang, Bo Zhang, Haochen Jiang, Zhenghua Li, Chen Li, Fei Huang, Min Zhang

Syntactic Text Processing

Challenge:	Recent studies on Chinese grammatical error correction focus on learning essays.
Approach:	They propose a Chinese grammatical error correction dataset that annotates multiple references for 12,500 sentences from three native domains.
Outcome:	The proposed dataset can be used to facilitate research on Chinese grammatical error correction (CGEC) for native speaker texts from multiple domains.

ExploraCoder: Advancing Code Generation for Multiple Unseen APIs via Planning and Chained Exploration (2025.acl-long)

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Yunkun Wang, Yue Zhang, Zhen Qin, Chen Zhi, Binhua Li, Fei Huang, Yongbin Li, Shuiguang Deng

Programming Languages in NLP

Challenge:	Large language models face intrinsic limitations in coding with unseen APIs in training corpora.
Approach:	They propose a training-free framework that empowers LLMs to invoke multiple unseen APIs in code solution by planning a complex problem into several API invocation subtasks and experimenting with correct API usage at intermediate steps.
Outcome:	The proposed framework significantly improves performance for models lacking prior API knowledge, achieving 11.99% over retrieval-based approaches and 17.28% over pretraining-based methods in pass@10.

LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey (2026.findings-acl)

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Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu, Jizhou Guo, Yankai Chen, Chunyu Miao, Hoang H Nguyen, Yue Zhou, Weizhi Zhang, Liancheng Fang, Hanrong Zhang, Fangxin Wang, Pengfei Zhang, Langzhou He, Yangning Li, Dongyuan Li, Renhe Jiang, Philip S. Yu

Intent-Aware and Hate-Mitigating Counterspeech Generation via Dual-Discriminator Guided LLMs (2024.lrec-main)

Challenge:	Recent advances in large language models (LLMs) have sparked growing interest in building fully autonomous agents.
Approach:	They propose to integrate human-provided information, feedback, or control into the agent system to enhance system performance, reliability, and safety.
Outcome:	The proposed systems improve system performance, reliability, and safety by integrating human-provided information, feedback, or control into the agent system.

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Haiyang Wang, Zhiliang Tian, Xin Song, Yue Zhang, Yuchen Pan, Hongkui Tu, Minlie Huang, Bin Zhou

Temporal Consistency for LLM Reasoning Process Error Identification (2025.findings-emnlp)

Challenge:	Hate speech is an aggressive expression that incites hatred towards specific groups based on their group identity.
Approach:	They propose an LLMs-based framework for counterspeech generation that uses intent-aware discriminators to decode intents of LLM models.
Outcome:	The proposed framework matches intents with hate mitigation intents and performs well.

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Jiacheng Guo, Yue Wu, Jiahao Qiu, Kaixuan Huang, Xinzhe Juan, Ling Yang, Mengdi Wang

Teaching Large Language Models to Translate on Low-resource Languages with Textbook Prompting (2024.lrec-main)

Challenge:	Empirical evaluations show consistent performance improvements over baseline methods . 7B/8B distilled models outperform all 70B/72B models and GPT-4o on ProcessBench .
Approach:	They propose a temporal consistency method that leverages consistency in a sequence of self-reflection actions to improve verification accuracy.
Outcome:	The proposed method outperforms existing methods on three benchmarks . it leverages consistency in a sequence of self-reflection actions to improve accuracy .

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Ping Guo, Yubing Ren, Yue Hu, Yunpeng Li, Jiarui Zhang, Xingsheng Zhang, Heyan Huang

Machine Translation

Challenge:	Large Language Models (LLMs) have demonstrated impressive results in Machine Translation by following instructions, even without training on parallel data.
Approach:	They propose a Translate After LEarNing Textbook approach which aims to enhance LLMs’ ability to translate low-resource languages by learning from a textbook.
Outcome:	The proposed approach improves translation performance by 14.8% using 112 low-resource languages from FLORES-200 with two LLMs: ChatGPT and BLOOMZ.

From Selection to Generation: A Survey of LLM-based Active Learning (2025.acl-long)

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Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie, Junda Wu, Xintong Li, Ryan Aponte, Hanjia Lyu, Joe Barrow, Hongjie Chen, Franck Dernoncourt, Branislav Kveton, Tong Yu, Ruiyi Zhang, Jiuxiang Gu, Nesreen K. Ahmed, Yu Wang, Xiang Chen, Hanieh Deilamsalehy, Sungchul Kim, Zhengmian Hu, Yue Zhao, Nedim Lipka, Seunghyun Yoon, Ting-Hao Kenneth Huang, Zichao Wang, Puneet Mathur, Soumyabrata Pal, Koyel Mukherjee, Zhehao Zhang, Namyong Park, Thien Huu Nguyen, Jiebo Luo, Ryan A. Rossi, Julian McAuley

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

Challenge:	Large Language Models (LLMs) have been used for selection and training of data for active learning.
Approach:	They propose an intuitive taxonomy that categorizes LLM-based active learning techniques and discuss the transformative roles they can play in the active learning loop.
Outcome:	The proposed model can generate entirely new data instances and provide more cost-effective annotations with fewer labeled data instances.

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

Which Sense Dominates Multisensory Semantic Understanding? A Brain Decoding Study (2024.lrec-main)

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

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Dandan Huang, Lu Cao, Zhenting Li, Yue Zhang

SalaMAnder: Shapley-based Mathematical Expression Attribution and Metric for Chain-of-Thought Reasoning (2025.findings-emnlp)

Challenge:	Decoding semantic meanings from brain activity is open to multisensory stimulation, as word meanings can be delivered by both auditory and visual inputs.
Approach:	They aim to develop a computational model to probing what information from the act of language understanding is represented in human brain.
Outcome:	The proposed model dissociates multisensory integration of word understanding into written text, spoken text and image perception respectively, exploring the decoding efficiency and reliability of unisensory information in the brain representation.

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Yue Xin, Chen Shen, Shaotian Yan, Xiaosong Yuan, Yaoming Wang, Xiaofeng Zhang, Chenxi Huang, Jieping Ye

RHGN: Relation-gated Heterogeneous Graph Network for Entity Alignment in Knowledge Graphs (2023.findings-acl)

Challenge:	Chain-of-Thought prompting improves the math reasoning capability of large language models.
Approach:	They propose a method for attribution of component-level contributions in CoT reasoning using Shapley value and a stratified sampling algorithm that significantly reduces computational complexity.
Outcome:	The proposed method reduces computational complexity and provides robust correlations with model performance.

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Xukai Liu, Kai Zhang, Ye Liu, Enhong Chen, Zhenya Huang, Linan Yue, Jiaxian Yan

Structured Data in NLP

Challenge:	Existing methods for entity alignment fail to account for heterogeneity among KGs and distinction between KG entities and relations.
Approach:	They propose a Relation-gated Heterogeneous Graph Network (RHGN) that uses a relation-gate based convolutional layer to distinguish relations and entities in the KG.
Outcome:	Extensive experiments on four datasets show that the proposed method is superior to state-of-the-art methods.

TaxPraBen: A Scalable Benchmark for Structured Evaluation of LLMs in Chinese Real-World Tax Practice (2026.acl-long)

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Gang Hu, Yating Chen, Haiyan Ding, Wang Gao, Huang Jiajia, Min Peng, Qianqian Xie, Kun Yue

Challenge:	Large Language Models excel in general domains but lack real-world practical capabilities.
Approach:	They propose a benchmark for Chinese taxation practice that combines 10 traditional application tasks with 3 pioneering real-world scenarios.
Outcome:	The proposed benchmark combines 10 traditional tasks with 3 pioneering real-world scenarios.

Gradient-Guided Multi-Judge Prompt Optimization (2026.acl-long)

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ChenZhuo Zhao, Xinda Wang, Pu Zhao, Yue Huang, Junting Lu, Ziqian Liu, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang

Stepwise Perplexity-Guided Refinement for Efficient Chain-of-Thought Reasoning in Large Language Models (2025.findings-acl)

Challenge:	Existing approaches to prompt optimization trade off signal quality against computational cost.
Approach:	They propose a framework that uses a first-order gradient approximation to score segment importance in a continuous masking direction.
Outcome:	The proposed framework improves efficiency and robustness by using a first-order gradient approximation to score segment importance in a continuous masking direction.

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Yingqian Cui, Pengfei He, Jingying Zeng, Hui Liu, Xianfeng Tang, Zhenwei Dai, Yan Han, Chen Luo, Jing Huang, Zhen Li, Suhang Wang, Yue Xing, Jiliang Tang, Qi He

Model Surgery: Modulating LLM’s Behavior Via Simple Parameter Editing (2025.naacl-long)

Challenge:	Chain-of-Thought (CoT) reasoning has improved the performance of large language models (LLMs) however, the detailed reasoning process in CoT often incurs long generation times and high computational costs due to the inclusion of unnecessary steps.
Approach:	They propose a method to identify critical reasoning steps using perplexity as a measure of their importance.
Outcome:	The proposed method achieves a better balance between reasoning accuracy and efficiency of CoT.

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Huanqian Wang, Yang Yue, Rui Lu, Jingxin Shi, Andrew Zhao, Shenzhi Wang, Shiji Song, Gao Huang

Challenge:	Current approaches for detoxification or preventing jailbreaking involve fine-tuning billions of parameters through gradient descent with substantial computational cost.
Approach:	They propose to use supervised fine-tuning and Reinforcement Learning from human feedback to modify LLMs' behavior by directly editing a small subset of parameters.
Outcome:	Experiments show that editing a small subset of parameters can modulate specific behaviors of LLMs, such as detoxification and resistance to jailbreak, with only inference-level computational resources.

Aligning Paralinguistic Understanding and Generation in Speech LLMs via Multi-Task Reinforcement Learning (2026.eacl-industry)

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Minseok Kim, Jingxiang Chen, Seong-Gyun Leem, Yin Huang, Rashi Rungta, Zhicheng Ouyang, Haibin Wu, Surya Teja Appini, Ankur Bansal, Yang Bai, Yue Liu, Florian Metze, Ahmed A Aly, Anuj Kumar, Ariya Rastrow, Zhaojiang Lin

Social Welfare Function Leaderboard: On the Emergence of LLM Agents as the Welfare Dictator (2026.findings-acl)

Challenge:	Using paralinguistic cues is challenging for speech large language models, authors say . limited training data, annotation difficulty, and models exploiting lexical shortcuts are challenges . a recent study shows that modeling paralinguistic reasoning with multitask RL improves paralinguistics understanding .
Approach:	They propose multi-task reinforcement learning with chain-of-thought prompting that elicits explicit affective reasoning.
Outcome:	The proposed model improves paralinguistics understanding over baselines and strong proprietary models by 8-12% on Expresso, IEMOCAP, and RAVDESS.

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Zhengliang Shi, Ruotian Ma, Jen-tse Huang, Xinbei Ma, Xingyu Chen, Mengru Wang, Qu Yang, Yue Wang, Fanghua Ye, Ziyang Chen, Shanyi Wang, Cixing LI, Wenxuan Wang, Zhaopeng Tu, Xiaolong Li, Zhaochun Ren, Liefeng Bo

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

Challenge:	Large language models (LLMs) are increasingly entrusted with high-stakes decisions that affect human welfare.
Approach:	They evaluate 20 state-of-the-art Large language models (LLMs) and 20 LLM dictators to create a social welfare function benchmark.
Outcome:	The proposed model creates dilemma between maximizing collective efficiency and ensuring distributive fairness.

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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

Beyond Single-Value Metrics: Evaluating and Enhancing LLM Unlearning with Cognitive Diagnosis (2025.findings-acl)

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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Yicheng Lang, Kehan Guo, Yue Huang, Yujun Zhou, Haomin Zhuang, Tianyu Yang, Yao Su, Xiangliang Zhang

TopKG: Target-oriented Dialog via Global Planning on Knowledge Graph (2022.coling-1)

Challenge:	Large Language Models (LLMs) have been used to remove harmful knowledge and undesirable capabilities.
Approach:	They propose a framework that leverages Cognitive Diagnosis Modeling to evaluate LLM unlearning.
Outcome:	The proposed framework enhances evaluation and facilitates removal of harmful abilities.

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Zhitong Yang, Bo Wang, Jinfeng Zhou, Yue Tan, Dongming Zhao, Kun Huang, Ruifang He, Yuexian Hou

Should I Believe in What Medical AI Says? A Chinese Benchmark for Medication Based on Knowledge and Reasoning (2025.acl-short)

Challenge:	Existing target-oriented dialogs take a local and greedy strategy for response generation, where global planning is absent.
Approach:	They propose a global planning method for target-oriented dialog on a commonsense knowledge graph to adjust local response generation towards the global target.
Outcome:	The proposed method can reach the target with a higher success rate, fewer turns, and more coherent responses.

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Yue Wu, Yangmin Huang, Qianyun Du, Lixian Lai, Zhiyang He, Jiaxue Hu, Xiaodong Tao

PDF-to-Tree: Parsing PDF Text Blocks into a Tree (2024.findings-emnlp)

Challenge:	Large language models (LLMs) generate hallucinations when handling unfamiliar information.
Approach:	They propose a Chinese benchmark to evaluate large language models' knowledge and reasoning capabilities in medication tasks.
Outcome:	The proposed benchmark evaluates models in indication, dosage and administration, contraindicated population, mechanisms of action, drug recommendation, and drug interaction across six datasets.

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Yue Zhang, Zhihao Zhang, Wenbin Lai, Chong Zhang, Tao Gui, Qi Zhang, Xuanjing Huang

Challenge:	Existing studies try to extract one universal reading order for PDF files, however, some applications, like Retrieval Augmented Generation, require breaking long articles into sections and subsections for better indexing.
Approach:	They propose a new task and dataset, PDF-to-Tree, which organizes the text blocks of a PDF into a tree structure.
Outcome:	The proposed parser achieves 93.93% accuracy, surpassing baseline methods by 6.72%.

Building a Family of Data Augmentation Models for Low-cost LLM Fine-tuning on the Cloud (2025.coling-industry)

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Chengyu Wang, Yuanhao Yue, Jun Huang, Peng Wang

Guardian-as-an-Advisor: Advancing Next-Generation Guardian Models for Trustworthy LLMs (2026.findings-acl)

Challenge:	Existing models for learning large language models are expensive and difficult to build and fine-tune.
Approach:	They propose a family of data augmentation models to improve model fine-tuning efficiency . they leverage powerful LLMs to expand, refine and re-write instructions and responses .
Outcome:	The proposed models improve the efficiency of model fine-tuning by leveraging small datasets and quality assessment techniques.

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Yue Huang, Haomin Zhuang, Jiayi Ye, Han Bao, Yanbo Wang, Hang Hua, Siyuan Wu, Pin-Yu Chen, Xiangliang Zhang

Ryze: Evidence-Enriched Data Synthesis from Biomedical Papers (2026.acl-demo)

Challenge:	prevailing taxonomies neglect robustness and honesty, yielding safer-on-paper but less useful systems.
Approach:	They propose a soft-gating pipeline where a guardian predicts a binary risk label plus a concise explanation and prepends this advice to the original query for re-inference.
Outcome:	The proposed model maintains safety while reducing over-refusal.

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Yeqi Huang, Yue Chen, Yanwei Ye, Guanhao Su, Luo Mai

Knowledge Extraction on Semi-Structured Content: Does It Remain Relevant for Question Answering in the Era of LLMs? (2026.eacl-long)

Challenge:	Existing post-training pipelines that generate QA pairs require costly expert annotation and synthetic data that drops evidence structure.
Approach:	They propose a system that converts raw biomedical papers into evidence-enriched training sets and a domain-specialized VLM.
Outcome:	Ryze synthesizes QA pairs with complete supporting evidence, reduces layout and OCR errors . the system outperforms the base model on LAB-Bench and surpasses GPT-5.2 by +3.8%.

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Kai Sun, Yin Huang, Srishti Mehra, Mohammad Kachuee, Xilun Chen, Renjie Tao, Zhaojiang Lin, Andrea Jessee, Nirav Shah, Alex L Betty, Yue Liu, Anuj Kumar, Wen-tau Yih, Xin Luna Dong

MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction (2022.naacl-main)

Challenge:	Existing literature on knowledge extraction for question answering questions whether it is still relevant for question answerrs.
Approach:	They extend an existing benchmark with knowledge extraction annotations and evaluate commercial and open-source LLMs of varying sizes.
Outcome:	The proposed model can achieve high QA accuracy, but can still benefit from knowledge extraction through augmentation with extracted triples and multi-task learning.

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Yue Zhang, Zhenghua Li, Zuyi Bao, Jiacheng Li, Bo Zhang, Chen Li, Fei Huang, Min Zhang

Syntactic Text Processing

Challenge:	Using a multi-reference multi-source evaluation dataset, Chinese grammatical error correction (CGEC) is relatively scarce.
Approach:	They propose a multi-reference multi-source evaluation dataset for Chinese grammar error correction . the dataset contains 7,063 sentences written by Chinese-as-a-Second-Language learners .
Outcome:	The proposed dataset can be used to evaluate Chinese grammar errors in Chinese.

A Comparison between Pre-training and Large-scale Back-translation for Neural Machine Translation (2021.findings-acl)

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Dandan Huang, Kun Wang, Yue Zhang

Multilinguality

Challenge:	BERT is a promising technique to improve NMT, but how it outperforms standard NMT is understudied.
Approach:	We compare MT engines trained with pre-trained BERT and back-translation with incrementally larger amounts of data.
Outcome:	The proposed technique outperforms standard NMT models on morphology and syntax.

EasyDistill: A Comprehensive Toolkit for Effective Knowledge Distillation of Large Language Models (2025.emnlp-demos)

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Chengyu Wang, Junbing Yan, Wenrui Cai, Yuanhao Yue, Jun Huang

NL2Lean: Translating Natural Language into Lean 4 through Multi-Aspect Reinforcement Learning (2025.emnlp-main)

Challenge:	In this paper, we present EasyDistill, a comprehensive toolkit designed for effective black-box and white-box knowledge distillation (KD) of large language models.
Approach:	They propose a toolkit for effective black-box and white-box knowledge distillation (KD) of large language models (LLMs).
Outcome:	The framework offers data synthesis, supervised fine-tuning, ranking optimization, and reinforcement learning techniques specifically tailored for KD scenarios.

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Yue Fang, Shaohan Huang, Xin Yu, Haizhen Huang, Zihan Zhang, Weiwei Deng, Furu Wei, Feng Sun, Qi Zhang, Zhi Jin

Machine Translation

Challenge:	Existing formal proof assistants rely on instruction tuning and lack fine-grained structural and semantic alignment.
Approach:	They propose a reinforcement learning framework that enables LLMs to translate natural language into formal language such as Lean 4 . they use a model with basic translation ability to refine the model's reinforcement learning .
Outcome:	The proposed method outperforms baseline models on NL-to-Lean 4 tasks.

MTGP: Multi-turn Target-oriented Dialogue Guided by Generative Global Path with Flexible Turns (2023.findings-acl)

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Anqi Liu, Bo Wang, Yue Tan, Dongming Zhao, Kun Huang, Ruifang He, Yuexian Hou

MLeVLM: Improve Multi-level Progressive Capabilities based on Multimodal Large Language Model for Medical Visual Question Answering (2024.findings-acl)

Challenge:	Existing approaches focus on global planning, which plans toward the target before the conversation.
Approach:	They propose to generate a global path as a natural language sentence instead of a sequence of nodes.
Outcome:	The proposed method has fewer turns, more coherent semantics, and higher success rate than baselines.

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Dexuan Xu, Yanyuan Chen, Jieyi Wang, Yue Huang, Hanpin Wang, Zhi Jin, Hongxing Wang, Weihua Yue, Jing He, Hang Li, Yu Huang

SAM3-I: Segment Anything with Instructions (2026.acl-long)

Challenge:	Existing MVQA models ignore multi-level progressive capabilities due to unspecific data and plain architecture.
Approach:	They propose a multi-level visual language model for medical visual question answering (MVQA) which covers multi- level questions and answers as well as reasoning processes from visual clues to semantic cognition.
Outcome:	The proposed model outperforms existing medical multimodal large language models on a multi-level instruction dataset and a feature alignment module.

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Jingjing Li, Yue Feng, Yuchen Guo, Jincai Huang, Wei Ji, Qi Bi, Yongri Piao, Miao Zhang, Xiaoqi Zhao, Qiang Chen, Shihao Zou, Huchuan Lu, Li Cheng

Low-Resource NLP

Challenge:	Existing methods for concept-level grounding and instruction-level reasoning use coarse representations and iterative mask filtering.
Approach:	They propose an instruction-following extension of the Segment Anything Model 3 family that unifies concept-level grounding and instruction-level reasoning within a single segmentation framework.
Outcome:	Experiments show that SAM3-I achieves appealing performance across referring and reasoning-based segmentation while maintaining its strong concept recall ability.

PrismRAG: Boosting RAG Factuality with Distractor Resilience and Strategized Reasoning (2025.emnlp-industry)

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Mohammad Kachuee, Teja Gollapudi, Minseok Kim, Yin Huang, Kai Sun, Xiao Yang, Jiaqi Wang, Nirav Shah, Yue Liu, Aaron Colak, Anuj Kumar, Wen-tau Yih, Xin Luna Dong

PolicyLLM: Towards Excellent Comprehension of Public Policy for Large Language Models (2026.findings-acl)

Challenge:	Existing methods to improve factuality of large language models (LLMs) rely on human-engineered instructions.
Approach:	They propose a retrieval-augmented generation framework that trains the model with distractor-aware QA pairs mixing gold evidence with subtle distractor passages and instills reasoning-centric habits that make the LLM plan, rationalize, and synthesize without extensive human engineered instructions.
Outcome:	The proposed framework outperforms state-of-the-art solutions across 12 open-book RAG QA benchmarks and is being deployed in production.

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Han Bao, Penghao Zhang, Yue Huang, Zhengqing Yuan, Yanchi Ru, SU Rui, Yujun Zhou, Xiangqi Wang, Kehan Guo, Nitesh V Chawla, Yanfang Ye, Xiangliang Zhang

Boosting Data Utilization for Multilingual Dense Retrieval (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) are increasingly integrated into real-world decision-making, but their ability to comprehend and reason about policy-related content remains underexplored.
Approach:	They propose a bilingual benchmark evaluating policy comprehension comprising 21K cases across a broad spectrum of policy areas.
Outcome:	The proposed model shows stronger performance on application-oriented policy tasks than on memorization or conceptual understanding, and yields the highest accuracy on structured reasoning tasks.

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Chao Huang, Fengran Mo, Yufeng Chen, Changhao Guan, Zhenrui Yue, Xinyu Wang, Jinan Xu, Kaiyu Huang

Information Retrieval

Challenge:	Existing studies focus on fine-tuning multilingual dense retrieval models, but data scarcity for low-resource languages makes it difficult to align representations in a shared vector space.
Approach:	They propose to obtain high-quality hard negative samples and effective mini-batch data to boost data utilization for multilingual dense retrieval by obtaining high-quality negative samples.
Outcome:	The proposed method outperforms existing baselines on a multilingual retrieval benchmark, MIRACL, with 16 languages.

Clues Before Answers: Generation-Enhanced Multiple-Choice QA (2022.naacl-main)

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Zixian Huang, Ao Wu, Jiaying Zhou, Yu Gu, Yue Zhao, Gong Cheng

RiskLab: A Controlled Toolkit for Probing Emergent Risks in LLM-Based Multi-Agent Systems (2026.acl-demo)

Challenge:	Multiple-choice question answering (MCQA) uses text-to-text framework . but, there is an under-utilization of the decoder and knowledge that can be decoded .
Approach:	They propose a generative multiple-choice question answering model which generates a clue from the question and leverages it to enhance a reader for MCQA.
Outcome:	The proposed model outperforms text-to-text models on multiple MCQA datasets.

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Yu Jiang, Wenjie Wang, Yue Huang, Yanbo Wang, Zhenhong Zhou, Xiuying Chen, Yang Liu, Pin-Yu Chen, Wei Wang, Xiangliang Zhang

Interventional Rationalization (2023.emnlp-main)

Challenge:	Recent advances in large language model (LLM) agents have accelerated deployment of multi-agent systems for complex tasks.
Approach:	They propose an open-source toolkit for instantiating, probing, and measuring emergent risks in LLM-based multi-agent systems under controlled conditions.
Outcome:	The proposed toolkit is based on a structured topology–environment–protocol–agent–task quintuple enabling reproducible studies of how communication structure, coordination mechanisms, and incentives shape system-level risks.

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Linan Yue, Qi Liu, Li Wang, Yanqing An, Yichao Du, Zhenya Huang

LLM-as-a-Coauthor: Can Mixed Human-Written and Machine-Generated Text Be Detected? (2024.findings-naacl)

Challenge:	Existing methods for rationalization use spurious correlations in data to compose rationales and make predictions.
Approach:	They propose a method to discover the causal rationales by using a structural causal model.
Outcome:	The proposed method is based on the causal theory and validates on three real-world datasets.

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Qihui Zhang, Chujie Gao, Dongping Chen, Yue Huang, Yixin Huang, Zhenyang Sun, Shilin Zhang, Weiye Li, Zhengyan Fu, Yao Wan, Lichao Sun

Text Generation

Challenge:	Current research focuses on purely MGT detection without adequately addressing mixed scenarios including AI-revised Human-Written Text (HWT) and human-revealed MGT.
Approach:	They define mixtext, a form of mixed text involving both AI and human-generated content, and then use a MixSet dataset to assess their effectiveness.
Outcome:	The proposed detectors struggle to identify mixtext, particularly in dealing with subtle modifications and style adaptability.

Can GRPO Boost Complex Multimodal Table Understanding? (2025.emnlp-main)

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Xiaoqiang Kang, Shengen Wu, Zimu Wang, Yilin Liu, Xiaobo Jin, Kaizhu Huang, Wei Wang, Yutao Yue, Xiaowei Huang, Qiufeng Wang

SHARP: Steering Hallucination in LVLMs via Representation Engineering (2025.emnlp-main)

Challenge:	Existing table understanding methods struggle with low initialization accuracy and coarse rewards in tabular contexts.
Approach:	They propose a three-stage RL framework that enhances multimodal table understanding through: (1) Warm-up that prompts initial perception and reasoning capabilities; (2) Perception Alignment GRPO (PA-GRPO); (3) Hint-Completion GR PO (HC-GRP);
Outcome:	The proposed framework outperforms existing models on held-in and held-out datasets, outperforming SFT and GRPO largely.

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Junfei Wu, Yue Ding, Guofan Liu, Tianze Xia, Ziyue Huang, Dianbo Sui, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan

AlphaContext: An Evolutionary Tree-based Psychometric Context Generator for Creativity Assessment (2026.acl-long)

Challenge:	Large Vision-Language Models (LVLMs) generate responses that are plausible but incorrect or unsupported—commonly referred to as hallucinations.
Approach:	They propose a representation-level intervention framework that modulates hallucination-related features during inference by probing their encoded features.
Outcome:	The proposed framework reduces hallucinations while maintaining the performance and generalization capabilities of Large Vision-Language Models (LVLMs).

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Yixuan Wang, Yue Huang, Hong Qian, Yunzhao Wei, Yifei Ding, Wenkai Wang, Zhi Liu, Zhongjing Huang, Aimin Zhou, Jiajun Guo

Challenge:	Existing LLM-based tools struggle with insufficient assessment cues, weak narrative coherence, limited stylistic diversity, and poor support for creative thinking.
Approach:	They propose an evolutionary tree-based psychometric context generator that integrates rule-guided outline planning, sentence-level MCTS generation, MAP-Elites quality-diversity optimization and assessment-guide refiner simulation.
Outcome:	The proposed tool outperforms strong LLMs and structured frameworks on 7 evaluation dimensions and shows higher alignment with expert-designed contexts.

Training Medical QA Models Based on Mixed Rewards from Multiple-Choice and Open-Ended Questions (2025.findings-emnlp)

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Yue Qiu, Yujan Ting, Pei Dong, Terrence Chen, Weijing Huang

Question Answering

Challenge:	Reinforcement learning (RL) for large language models typically requires clear reward signals, which are often unavailable for open-ended (OE) questions where answer evaluation is ambiguous without scalable expert labeling.
Approach:	They propose a mixed-data approach to training large language models with varying reward clarity . they combine Multiple-choice questions (MCQs) with OE questions for which they use simpler, potentially noisy rewards such as Jaccard similarity or LLM-based evaluators.
Outcome:	The mixed-data approach improves medical question-answering performance across model scales.

Cross-Lingual Pitfalls: Automatic Probing Cross-Lingual Weakness of Multilingual Large Language Models (2025.acl-long)

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Zixiang Xu, Yanbo Wang, Yue Huang, Xiuying Chen, Jieyu Zhao, Meng Jiang, Xiangliang Zhang

SR-LLM: Rethinking the Structured Representation in Large Language Model (2025.acl-long)

Challenge:	Large language models have achieved remarkable success in Natural Language Processing, yet their cross-lingual consistency remains a significant challenge.
Approach:	They propose a method to identify cross-lingual weaknesses in Large Language Models . they construct bilingual question pairs that expose performance discrepancies between English and target languages .
Outcome:	The proposed method uncovers over 50% accuracy drops in target languages across models.

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Jiahuan Zhang, Tianheng Wang, Ziyi Huang, Yulong Wu, Hanqing Wu, DongbaiChen DongbaiChen, Linfeng Song, Yue Zhang, Guozheng Rao, Kaicheng Yu

Targeted Exploration via Unified Entropy Control for Reinforcement Learning (2026.findings-acl)

Challenge:	Structured representations have long been pivotal in computational linguistics, but their role remains ambiguous in the Large Language Models (LLMs) era.
Approach:	They propose a framework that integrates structured representations into LLMs from training-free and training-dependent perspectives.
Outcome:	The proposed framework integrates structured representations through natural language descriptions in LLM prompts while augmenting the model’s inference capability through fine-tuning on linguistically described structured representation.

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Chen Wang, Lai Wei, Yanzhi Zhang, Chenyang Shao, Zedong Dan, Weiran Huang, Ge Lan, Yue Wang

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning (2025.acl-long)

Challenge:	Existing methods for group relative policy optimization suffer from entropy collapse . Existing exploration methods introduce additional bias or variance during exploration, making it difficult to maintain stability.
Approach:	They propose a framework that provides targeted mechanisms for exploration and stabilization.
Outcome:	The proposed framework expands search space on difficult prompts while preventing entropy growth uncontrollably.

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Chenxi Huang, Shaotian Yan, Liang Xie, Binbin Lin, Sinan Fan, Yue Xin, Deng Cai, Chen Shen, Jieping Ye

Dissecting Logical Reasoning in LLMs: A Fine-Grained Evaluation and Supervision Study (2025.findings-emnlp)

Challenge:	Representation Fine-tuning (ReFT) is a proposed method for improving parameter efficiency . however, it yields suboptimal performance, as fixed-position representations have uncertain impact on outputs .
Approach:	They propose a method that fine-tunes critical representations in a low-rank linear subspace while freezing the base model.
Outcome:	The proposed method improves accuracy of LLaMA-2-7B and ReFT by 18.2 and 3.8 on GSM8K.

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Yujun Zhou, Jiayi Ye, Zipeng Ling, Yufei Han, Yue Huang, Haomin Zhuang, Zhenwen Liang, Kehan Guo, Taicheng Guo, Xiangqi Wang, Xiangliang Zhang

CFlowPsyD: An Analysis-Enhanced Dataset for Asynchronous Psychological Counseling through Self-Optimizing Multi-Agent Framework (2026.findings-acl)

Challenge:	Existing benchmarks that rely on final-answer accuracy fail to capture the quality of the reasoning process.
Approach:	They propose a fine-grained evaluation framework that assesses logical reasoning across three dimensions: overall accuracy, stepwise soundness, and representation-level probing.
Outcome:	The proposed framework assesses logical reasoning across three dimensions: overall accuracy, stepwise soundness, and representation-level probing.

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Donghao Li, Yifan Deng, Jinta Weng, Xingsheng Zhang, Chenxu Niu, Jingyuan Tian, Heyan Huang, Yue Hu

A Survey on Open Information Extraction from Rule-based Model to Large Language Model (2024.findings-emnlp)

Challenge:	Asynchronous psychological counseling (APC) is a crucial mental health service modality that transcends temporal and spatial constraints.
Approach:	They propose a self-optimizing multi-agent framework for counseling dialogue generation, CFlowPsy, which utilizes real anonymized counseling cases as seed data to synthesize diverse problem-solving-oriented APC conversations through large language models.
Outcome:	The proposed framework synthesizes diverse problem-solving-oriented APC conversations through large language models.

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Liu Pai, Wenyang Gao, Wenjie Dong, Lin Ai, Ziwei Gong, Songfang Huang, Li Zongsheng, Ehsan Hoque, Julia Hirschberg, Yue Zhang

What Have We Achieved on Text Summarization? (2020.emnlp-main)

Challenge:	Open Information Extraction (OpenIE) is a key NLP task aimed at extracting structured information from unstructured text sources.
Approach:	They propose to categorize OpenIE into rule-based, neural, and pre-trained large language models and discuss each within a chronological framework.
Outcome:	The paper categorizes OpenIE approaches into rule-based, neural, and pre-trained large language models, discussing each within a chronological framework.

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Dandan Huang, Leyang Cui, Sen Yang, Guangsheng Bao, Kun Wang, Jun Xie, Yue Zhang

DistilQwen2.5: Industrial Practices of Training Distilled Open Lightweight Language Models (2025.acl-industry)

Challenge:	Existing methods for text summarization have been investigated, but there are still gaps between them and human professionals.
Approach:	They analyze 8 major sources of errors on 10 representative summarization models manually.
Outcome:	Aiming to gain more understanding of summarization systems with respect to their strengths and limitations on a fine-grained syntactic and semantic level, we use 8 major sources of errors on 10 representative summarizing models.

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Chengyu Wang, Junbing Yan, Yuanhao Yue, Jun Huang

Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings (2022.acl-long)

Challenge:	Existing studies on distilled lightweight LLMs have focused on transferring knowledge from a larger model (the teacher) to a smaller model (sector).
Approach:	They propose a family of distilled, lightweight LLMs derived from Qwen2.5 models.
Outcome:	Experimental results show that the distilled models have significantly stronger instruction-following capabilities than the original models.

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Jiangbin Zheng, Yile Wang, Ge Wang, Jun Xia, Yufei Huang, Guojiang Zhao, Yue Zhang, Stan Li

SimulBench: Evaluating Language Models with Creative Simulation Tasks (2025.findings-naacl)

Challenge:	Contextualized embeddings are expensive and resource-demanding, hence environmentally unfriendly.
Approach:	They propose a method to convert contextualized embeddings from pre-trained models into static embeddables using synonym knowledge and weighted vector distribution.
Outcome:	The proposed method outperforms baseline embeddings by a large margin through extrinsic and intrinsic tasks.

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Qi Jia, Xiang Yue, Tuney Zheng, Jie Huang, Bill Yuchen Lin

Guiding Dialogue Agents to Complex Semantic Targets by Dynamically Completing Knowledge Graph (2023.findings-acl)

Challenge:	Existing benchmarks for large language models do not fully evaluate their potential for broad implementation.
Approach:	They propose to use a fixed LLM as a user agent to engage with an LLM to collect dialogues first under different tasks.
Outcome:	The proposed framework outperforms LLaMA-3-70b-Chat on 18.55% more cases.

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Yue Tan, Bo Wang, Anqi Liu, Dongming Zhao, Kun Huang, Ruifang He, Yuexian Hou

InterIDEAS: Philosophical Intertextuality via LLMs (2025.emnlp-main)

Challenge:	Existing knowledge graphs are incomplete in tracking complex semantic relations of the target-oriented dialogue.
Approach:	They combine methods of knowledge retrieval and relationship prediction to construct a context-related dynamic KG and a metric to evaluate the tracked path automatically.
Outcome:	The proposed method can control the agent more logically and smoothly toward the complex target.

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Yue Yang, Yinzhi Xu, Chenghao Huang, JohnMichael Jurgensen, Han Hu, Hao Wang

Challenge:	a new dataset aims to bridge philosophy, literary studies, and natural language processing (NLP) by integrating theories of intertextuality with bibliometric techniques.
Approach:	They propose a dataset that bridges philosophy, literary studies, and natural language processing (NLP) it combines theories of intertextuality from literary studies with bibliometric techniques and recent LLMs .
Outcome:	a new dataset bridges philosophy, literary studies, and natural language processing (NLP) to analyze intertextuality . the proposed method helps scholars understand the intellectual, social, and historical relations embedded in texts . it also contributes to the development of language models, authors say .

LIME: Less Is More for MLLM Evaluation (2025.findings-acl)

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King Zhu, Qianbo Zang, Shian Jia, Siwei Wu, Feiteng Fang, Yizhi Li, Shuyue Guo, Tianyu Zheng, Jiawei Guo, Bo Li, Haoning Wu, Xingwei Qu, Jian Yang, Ruibo Liu, Xiang Yue, Jiaheng Liu, Chenghua Lin, Hamid Alinejad-Rokny, Min Yang, Shiwen Ni, Wenhao Huang, Ge Zhang

ProMed: Shapley Information Gain Guided Reinforcement Learning for Proactive Medical LLMs (2026.acl-long)

Challenge:	Existing MLLM benchmarks and unified evaluation frameworks cannot accurately and efficiently reflect the ability of MLMLs.
Approach:	They propose a semi-automated benchmark curated using a pipeline that filters out uninformative samples and eliminates answer leakage by focusing on tasks that require image-based understanding.
Outcome:	The proposed benchmark reduces the number of samples by 76% and evaluation time by 77% while it can more effectively distinguish different models’ abilities.

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Hongxin Ding, Baixiang Huang, Yue Fang, Weibin Liao, Xinke Jiang, Jinyang Zhang, Yinghao Zhu, Zheng Li, Liantao Ma, Junfeng Zhao, Yasha Wang

Challenge:	Existing medical Large Language Models (LLMs) follow a reactive paradigm, risking diagnostic errors by answering before seeking sufficient details.
Approach:	They propose a reinforcement learning framework that transitions LLMs toward a proactive paradigm, enabling them to ask clinically valuable questions before decision-making.
Outcome:	Experiments on partial-information medical benchmarks show that ProMed outperforms state-of-the-art methods by 6.29% on average and delivers a 54.45% gain over the reactive paradigm.

TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing (2021.acl-demo)

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Xiao Wang, Qin Liu, Tao Gui, Qi Zhang, Yicheng Zou, Xin Zhou, Jiacheng Ye, Yongxin Zhang, Rui Zheng, Zexiong Pang, Qinzhuo Wu, Zhengyan Li, Chong Zhang, Ruotian Ma, Zichu Fei, Ruijian Cai, Jun Zhao, Xingwu Hu, Zhiheng Yan, Yiding Tan, Yuan Hu, Qiyuan Bian, Zhihua Liu, Shan Qin, Bolin Zhu, Xiaoyu Xing, Jinlan Fu, Yue Zhang, Minlong Peng, Xiaoqing Zheng, Yaqian Zhou, Zhongyu Wei, Xipeng Qiu, Xuanjing Huang

Robustness in NLP

Challenge:	Existing approaches to textual robustness evaluation focus on slightly modifying the input data, which maintains the original meaning and results in a different prediction.
Approach:	They propose a multilingual robustness evaluation toolkit for NLP that integrates universal text transformations, task-specific transformations and adversarial attack.
Outcome:	The toolkit includes universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses.

BatonVoice: An Operationalist Framework for Enhancing Controllable Speech Synthesis with Linguistic Intelligence from LLMs (2026.acl-long)

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Yue Wang, Ruotian Ma, Xingyu Chen, Zhengliang Shi, Morunliu Yang, Wanshun Chen, Huang Liu, Jiadi Yao, Xin He, Qu Yang, Qingxuan Jiang, Fanghua Ye, Juntao Li, Zhaopeng Tu, Xiaolong Li, Liefeng Bo, Min Zhang

Speech & Audio in NLP

Challenge:	Existing approaches often fail to leverage the linguistic intelligence of Large Language Models (LLMs) Existing models lack the ability to follow text instructions for controllable Text-to-Speech (TTS).
Approach:	They propose a framework where an LLM acts as a conductor, understanding user instructions and generating a textual plan - explicit vocal features.
Outcome:	The proposed model outperforms open- and closed-source models in speech synthesis and achieves zero-shot cross-lingual generalization.

AgenticQwen: Training Small Agentic Language Models with Dual Data Flywheels for Industrial-Scale Tool Use (2026.acl-industry)

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Yuanjie Lyu, Chengyu Wang, Haonan Zheng, Yuanhao Yue, Junbing Yan, Ming Wang, Jun Huang

Aria-UI: Visual Grounding for GUI Instructions (2025.findings-acl)

Challenge:	Modern industrial applications increasingly demand language models capable of multi-step reasoning and tool use in real-world settings.
Approach:	They propose a model family that trains via multi-round reinforcement learning on synthetic data and open-source data.
Outcome:	The proposed model train on synthetic and open-source data achieves strong performance on multiple agentic benchmarks and in an industrial agent system.

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Yuhao Yang, Yue Wang, Dongxu Li, Ziyang Luo, Bei Chen, Chao Huang, Junnan Li