Papers by Jun Huang

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

Challenge:	Existing methods for few-shot Named Entity Recognition ignore entity boundaries and are time-consuming . a seminal span-based prototypical network solves the problem using two stages: span extraction and mention classification.
Approach:	They propose a seminal span-based prototypical network that tackles few-shot NER . they transform sequential tags into a global boundary matrix and use prototypical learning .
Outcome:	The proposed model outperforms strong baselines over multiple benchmarks.

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Yuanhao Yue, Chengyu Wang, Jun Huang, Peng Wang

Thinking with DistilQwen: A Tale of Four Distilled Reasoning and Reward Model Series (2025.emnlp-industry)

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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Wenrui Cai, Chengyu Wang, Junbing Yan, Jun Huang, Xiangzhong Fang

Green & Sustainable NLP

Challenge:	In the rapidly evolving landscape of large language models, the need for efficient reasoning models has become increasingly urgent.
Approach:	They extend the Qwen model family by introducing four model series specifically designed for industrial applications.
Outcome:	The proposed models outperform previous models in multiple benchmarks and provide scalable training and inference functionality on the Alibaba Cloud PAI platform.

A Deep Learning-Based System for PharmaCoNER (D19-57)

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Ying Xiong, Yedan Shen, Yuanhang Huang, Shuai Chen, Buzhou Tang, Xiaolong Wang, Qingcai Chen, Jun Yan, Yi Zhou

Information Retrieval

Challenge:	Efficient access to mentions of clinical entities is very important for using clinical text.
Approach:	They developed a pipeline system based on deep learning methods for this shared task . it achieves a micro-average F1-score of 0.9105 on track 1 and a mini-average LSTM score of 0.8391 on track 2 .
Outcome:	The proposed system achieves a micro-average F1-score of 0.9105 on track 1 and a mini-average score of 0.8391 on track 2.

PAI-Diffusion: Constructing and Serving a Family of Open Chinese Diffusion Models for Text-to-image Synthesis on the Cloud (2024.acl-demos)

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Chengyu Wang, Zhongjie Duan, Bingyan Liu, Xinyi Zou, Cen Chen, Kui Jia, Jun Huang

Sharing, Teaching and Aligning: Knowledgeable Transfer Learning for Cross-Lingual Machine Reading Comprehension (2023.findings-emnlp)

Challenge:	Existing diffusion models fail to address the challenges of generating high-quality images from textual descriptions due to its large vocabulary size and complex character relationships.
Approach:	They propose a framework that integrates Chinese diffusion models with Alibaba Cloud's Platform for AI and enables the generation of contextually relevant images.
Outcome:	The proposed framework integrates with Alibaba Cloud’s Platform for AI, providing accessible and scalable solutions.

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Tingfeng Cao, Chengyu Wang, Chuanqi Tan, Jun Huang, Jinhui Zhu

Multilinguality

Challenge:	X-STA is a new approach for cross-lingual machine reading comprehension . the variation of answer span positions in different languages makes it difficult to transfer knowledge across languages.
Approach:	They propose a method that leverages an attentive teacher to subtly transfer the answer spans of the source language to the answer output space of the target.
Outcome:	The proposed method outperforms state-of-the-art approaches on three multi-lingual datasets.

DAFNet: Dynamic Auxiliary Fusion for Sequential Model Editing in Large Language Models (2024.findings-acl)

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Taolin Zhang, Qizhou Chen, Dongyang Li, Chengyu Wang, Xiaofeng He, Longtao Huang, Hui Xue’, Jun Huang

Experience-driven Multi-turn Reinforcement Learning for GUI Agents (2026.acl-long)

Challenge:	Large language models (LLMs) have shown impressive results, but still suffer from hallucination, i.e., the generation of false information.
Approach:	They propose a task of sequential model editing that aims to rectify mistakes continuously.
Outcome:	The proposed method significantly outperforms baselines in single-turn and sequential editing.

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Zhengxi Lu, Jiabo Ye, Fei Tang, Yongliang Shen, Haiyang Xu, Ziwei Zheng, Weiming Lu, Ming Yan, Fei Huang, Jun Xiao, Yueting Zhuang

Challenge:	GUI agents have demonstrated remarkable progress in automating complex user interface interactions . training such agents for long-horizon tasks remains challenging due to limited rewards and prohibitive costs.
Approach:	They propose a method that leverages expert trajectories as environment experiences for on-policy multi-turn training.
Outcome:	The proposed method achieves significant gains over the base model with 1K public trajectories as RL experiences . it achieves competitive performance against strong baselines such as UI-TARS-7B and GPT-4o .

Named Entity Recognition via Noise Aware Training Mechanism with Data Filter (2021.findings-acl)

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Xiusheng Huang, Yubo Chen, Shun Wu, Jun Zhao, Yuantao Xie, Weijian Sun

AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework (2024.lrec-main)

Challenge:	Existing methods for named entity recognition (NER) do not distinguish noisy from hard samples.
Approach:	They propose a noise-aware-with-filter method to help model identify noisy samples . they propose 'incomplete trust' loss function which boosts L CRF with a robust term .
Outcome:	The proposed method outperforms the existing methods on six real-world Chinese and English NER datasets.

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Xiang Li, Zhenyu Li, Chen Shi, Yong Xu, Qing Du, Mingkui Tan, Jun Huang

Information Retrieval

Challenge:	Currently, ML&DL methods fail to provide reasons for stock trend predictions, lacking interpretability and reasoning processes. large language models (LLMs) suffer from hallucinations and are unable to keep up with the latest information.
Approach:	They develop a method to train large language models to handle financial analysis tasks . they use AlphaFin datasets to compare performance with traditional methods .
Outcome:	The proposed method improves stock trend prediction and financial question answering tasks.

Spiral of Silence in Large Language Model Agents (2025.findings-emnlp)

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Mingze Zhong, Meng Fang, Zijing Shi, Yuxuan Huang, Shunfeng Zheng, Yali Du, Ling Chen, Jun Wang

Know-MRI: A Knowledge Mechanisms Revealer&Interpreter for Large Language Models (2025.acl-demo)

Challenge:	Existing theories of Spiral of Silence do not apply to large language models .
Approach:	They propose an evaluation framework for examining SoS in large language models . they consider four controlled conditions that vary the availability of "History" and "Persona" signals .
Outcome:	The proposed framework examines the SoS-like dynamics in large language models . it shows that history and persona together produce strong majority dominance .

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Jiaxiang Liu, Boxuan Xing, Chenhao Yuan, ChenxiangZhang ChenxiangZhang, Di Wu, Xiusheng Huang, Haida Yu, Chuhan Lang, Pengfei Cao, Jun Zhao, Kang Liu

Explainability & Interpretability in NLP

Challenge:	Existing interpretation methods only support tasks with specific inputs, limiting their practical applications.
Approach:	They propose an extensible module that matches different input data with interpretation methods and consolidates the interpreting outputs.
Outcome:	The proposed module can match different input data with interpretation methods and consolidate the interpreting outputs.

Revisiting and Advancing Chinese Natural Language Understanding with Accelerated Heterogeneous Knowledge Pre-training (2022.emnlp-industry)

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Taolin Zhang, Junwei Dong, Jianing Wang, Chengyu Wang, Ang Wang, Yinghui Liu, Jun Huang, Yong Li, Xiaofeng He

Knowledgeable In-Context Tuning: Exploring and Exploiting Factual Knowledge for In-Context Learning (2024.findings-naacl)

Challenge:	Existing knowledge-enhanced pre-trained language models (KEPLMs) can capture internal knowledge, but can't understand external background knowledge.
Approach:	They propose to use Chinese knowledge-enhanced pre-trained language models to improve context-aware representations via learning from structured relations in knowledge bases.
Outcome:	Experiments show that Chinese knowledge-enhanced pre-trained language models outperform strong baselines over various benchmark NLP tasks and in different model sizes.

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Jianing Wang, Chengyu Wang, Chuanqi Tan, Jun Huang, Ming Gao

Exploring the Compositional Deficiency of Large Language Models in Mathematical Reasoning Through Trap Problems (2024.emnlp-main)

Challenge:	Existing studies have explored multiple aspects that affect the performance of large language models (LLMs) such as input-output mapping, extensive data resources, and the ability to train on labeled examples.
Approach:	They propose a framework that injects knowledge into LLMs during continual self-supervised pre-training and judiciously selects examples with high knowledge relevance.
Outcome:	The proposed framework outperforms baseline models and improves by more than 13% and 7% on text classification and question-answering tasks.

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Jun Zhao, Jingqi Tong, Yurong Mou, Ming Zhang, Qi Zhang, Xuanjing Huang

Knowledge-Empowered Representation Learning for Chinese Medical Reading Comprehension: Task, Model and Resources (2021.findings-acl)

Challenge:	Current LLMs lack systematic compositionality, and therefore cannot serve as reliable cognitive models.
Approach:	They propose to introduce logical traps into the original problems of MATH and GSM8K to investigate the compositionality of large language models in mathematical reasoning.
Outcome:	The proposed model can generate infinite combinations from finite learned components.

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Taolin Zhang, Chengyu Wang, Minghui Qiu, Bite Yang, Zerui Cai, Xiaofeng He, Jun Huang

Layer-wise Fusion with Modality Independence Modeling for Multi-modal Emotion Recognition (2023.acl-long)

Challenge:	MRC is a popular task in NLP, aiming to understand a passage and answer the relevant questions.
Approach:	They propose a multi-target machine learning task for the medical domain that predicts answers to medical questions and corresponding support sentences from medical information sources simultaneously.
Outcome:	The proposed model outperforms baselines by fusing context-aware and knowledge-awful token representations.

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Jun Sun, Shoukang Han, Yu-Ping Ruan, Xiaoning Zhang, Shu-Kai Zheng, Yulong Liu, Yuxin Huang, Taihao Li

LaMP-Val: Large Language Models Empower Personalized Valuation in Auction (2025.findings-emnlp)

Challenge:	Existing studies focus on developing models that exploit the unification of multiple modalities.
Approach:	They propose to maintain modality independence by using a multi-modal transformer model that fuses all modalities.
Outcome:	The proposed model outperforms state-of-the-art models in multi-modal emotion recognition.

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Jie Sun, Tianyu Zhang, Houcheng Jiang, Kexin Huang, Xiang Shu, Zhibo Zhu, Lintao Ma, Xingyu Lu, Jun Zhou, Junkang Wu, Chi Luo, An Zhang, Jiancan Wu, Xiang Wang

R³A: Reinforced Reasoning for Relevance Assessment for RAG in User-Generated Content Platforms (2026.acl-industry)

Challenge:	Currently, most research focuses on the bidding algorithms used within auction mechanisms.
Approach:	They propose a personalized valuation framework that integrates Large Language Models to incorporate personalized semantic preference into users valuation process.
Outcome:	The proposed framework incorporates Large Language Models to incorporate personalized semantic preference into users valuation process.

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Xiaowei Yuan, Lei Jin, Haoxin Zhang, Ziyang Huang, Yan Gao, null Yiwu, Yao Hu, Jun Zhao, Kang Liu

Inductive Relation Inference of Knowledge Graph Enhanced by Ontology Information (2023.findings-emnlp)

Challenge:	Existing approaches to query–document relevance assessment are limited . ambiguous user intent and asymmetric relevance are challenges for RAG platforms .
Approach:	They propose a decomposed reasoning model for relevance assessment that decomposes query intent into intent inference and evidence grounding.
Outcome:	The proposed model outperforms strong baselines on offline benchmarks and achieves significant gains in large-scale online A/B testing.

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Wentao Zhou, Jun Zhao, Tao Gui, Qi Zhang, Xuanjing Huang

Knowledge Representation

Challenge:	Existing methods to inference knowledge graphs lack ontology information, which is often too sparse.
Approach:	They propose a knowledge graph inductive inference method that fuses ontology information to learn the semantic information of entities.
Outcome:	The proposed method outperforms large language models like ChatGPT on two benchmark datasets and improves the MRR metrics by 15.4% and 44.1%, respectively.

Enhancing Reasoning Abilities of Small LLMs with Cognitive Alignment (2025.emnlp-main)

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Wenrui Cai, Chengyu Wang, Junbing Yan, Jun Huang, Xiangzhong Fang

Locomo-Plus: Beyond-Factual Cognitive Memory Evaluation Framework for LLM Agents (2026.acl-long)

Challenge:	Existing methods to distill chain-of-thought (CoT) results from large language reasoning models (LRMs) to small models are ineffective and require substantial amount of annotated data.
Approach:	They propose a Critique-Rethink-Verify system for training small language reasoning models that can be critiquized according to the cognitive capabilities of smaller models.
Outcome:	The proposed system outperforms other methods on challenging reasoning benchmarks.

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Yifei Li, Weidong Guo, Lingling Zhang, Rongman Xu, Muye Huang, Hui Liu, Lijiao Xu, Yu Xu, Jun Liu

Challenge:	Existing benchmarks and evaluation protocols focus on surface-level factual recall.
Approach:	They propose a benchmark for assessing cognitive memory under cue–trigger semantic disconnect.
Outcome:	The proposed framework reveals failures not captured by existing benchmarks.

TransferTOD: A Generalizable Chinese Multi-Domain Task-Oriented Dialogue System with Transfer Capabilities (2024.emnlp-main)

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Ming Zhang, Caishuang Huang, Yilong Wu, Shichun Liu, Huiyuan Zheng, Yurui Dong, Yujiong Shen, Shihan Dou, Jun Zhao, Junjie Ye, Qi Zhang, Tao Gui, Xuanjing Huang

Dialogue Systems & Conversational Agents

Challenge:	Current datasets cater to user-led systems and are limited to predefined specific scenarios and slots.
Approach:	They propose to use a Chinese dialogue dataset to train a model that authentically simulates human-computer dialogues in 30 popular life service scenarios.
Outcome:	The proposed model achieves a joint accuracy of 75.09% in out-of-domain evaluations . it also achieves notable abilities in slot filling and questioning .

Improve Rule Retrieval and Reasoning with Self-Induction and Relevance ReEstimate (2025.findings-acl)

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Ziyang Huang, Wangtao Sun, Jun Zhao, Kang Liu

Information Retrieval

Challenge:	Existing rule retrieval methods suffer from low accuracy due to semantic gap between instantiated facts and abstract representations of rules.
Approach:	They propose a method that induces inferential rules that might offer benefits for reasoning by abstracting the underlying knowledge and logical structure in queries.
Outcome:	The proposed method improves retrieval effectiveness and accuracy across settings.

Infinity-Parser: Layout-Aware Reinforcement Learning with High-quality Document Parsing Dataset (2026.findings-acl)

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Baode Wang, Biao Wu, Weizhen Li, Meng Fang, Zuming Huang, Jun Huang, Yanjie Liang, Haozhe Wang, Ling Chen, Wei Chu, Yuan Qi

Adversary-Aware DPO: Enhancing Safety Alignment in Vision Language Models via Adversarial Training (2025.findings-emnlp)

Challenge:	Existing supervised fine-tuning methods struggle to generalize across document types, leading to poor performance.
Approach:	They propose layoutRL, a reinforcement learning framework that optimizes layout understanding through composite rewards integrating normalized edit distance, paragraph count accuracy, and reading order preservation.
Outcome:	The proposed model outperforms specialized document parsing systems and general-purpose vision-language models on a broad range of document types, languages, and structural complexities.

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Fenghua Weng, Jian Lou, Jun Feng, Minlie Huang, Wenjie Wang

Better Simultaneous Translation with Monotonic Knowledge Distillation (2023.acl-long)

Challenge:	Adversary-aware DPO (ADPO) is a training framework that explicitly considers adversary.
Approach:	a new framework integrates adversarial training into a pre-trained large language model to enhance safety alignment . adversary-aware DPO provides a framework that explicitly considers adversary .
Outcome:	a new training framework outperforms baselines in safety alignment and general utility of large language models.

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Shushu Wang, Jing Wu, Kai Fan, Wei Luo, Jun Xiao, Zhongqiang Huang

Break Through the Compression Bottleneck: From Theory to Practice (2026.findings-acl)

Challenge:	Existing methods to train offline MT models require generating target tokens before source sentence is fully consumed.
Approach:	They propose a method that leverages traditional translation models as teachers to generate monotonic yet accurate reference translations for sequence-level knowledge distillation.
Outcome:	The proposed approach improves on strong baselines and on a monotonic version of the WMT15 De-En test set.

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Xiusheng Huang, Lu Wang, Yequan Wang, Jun Zhao, Kang Liu

TRELM: Towards Robust and Efficient Pre-training for Knowledge-Enhanced Language Models (2024.lrec-main)

Challenge:	Existing compression methods suffer from bottleneck issues when compression ratio is increased.
Approach:	They propose a novel approach to combine low-rank decomposition and quantization methods to reduce the compression bottleneck.
Outcome:	The proposed method reduces the computational and memory overhead of existing methods while maintaining model accuracy.

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Junbing Yan, Chengyu Wang, Taolin Zhang, Xiaofeng He, Jun Huang, Wei Zhang, Longtao Huang, Hui Xue

Document-Level Relation Extraction via Pair-Aware and Entity-Enhanced Representation Learning (2022.coling-1)

Challenge:	Existing methods for incorporating external knowledge into language models do not prioritize learning embeddings for entity-related tokens.
Approach:	They propose a framework for incorporating external knowledge into pre-training models that utilize entity-related tokens.
Outcome:	The proposed framework reduces pre-training time by 50% and outperforms other KEPLMs in knowledge probing tasks and multiple knowledge-aware language understanding tasks.

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Xiusheng Huang, Hang Yang, Yubo Chen, Jun Zhao, Kang Liu, Weijian Sun, Zuyu Zhao

Efficient Prior-Guided Reasoning for Robust Retrieval-Augmented Generation under Conflicts (2026.acl-long)

Challenge:	Existing document-level relation extraction methods are sparse in relational entity pairs and the representation of entity pairs is insufficient.
Approach:	They propose a Pair-Aware and Entity-Enhanced(PAEE) model to solve two challenges . they propose predicting potential relational entity pairs and assembling directional entity pairs .
Outcome:	The proposed model can obtain state-of-the-art performance on four benchmark datasets . it can predict potential relational entity pairs and assemble directional entity pairs .

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Xiaowei Yuan, Ziyang Huang, Zhao Yang, Yequan Wang, Jun Zhao, Kang Liu

PhysReason: A Comprehensive Benchmark towards Physics-Based Reasoning (2025.acl-long)

Challenge:	Retrieval-Augmented Generation (RAG) has become a standard paradigm for grounding Large Language Models (LLMs) however, performance degrades substantially when faced with noisy, outdated, or conflicting retrieved information.
Approach:	They propose a framework that explicitly elicits the model’s parametric knowledge as prior information to guide reasoning on retrieved documents.
Outcome:	The proposed framework achieves robust performance across varying degrees of external inconsistency and noise.

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Xinyu Zhang, Yuxuan Dong, Yanrui Wu, Jiaxing Huang, Chengyou Jia, Basura Fernando, Mike Zheng Shou, Lingling Zhang, Jun Liu

Towards Adaptive Mechanism Activation in Language Agent (2025.coling-main)

Challenge:	Large language models demonstrate remarkable capabilities across various domains, including mathematics and logic reasoning.
Approach:	They propose a physics-based reasoning benchmark that includes physics theorems and constraints and a Physics Solution Auto Scoring Framework to evaluate physics based reasoning in large language models.
Outcome:	The proposed framework enables models to achieve less than 60% on answer-level evaluation, with performance dropping from knowledge questions (75.11%) to hard problems (31.99%).

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Ziyang Huang, Jun Zhao, Kang Liu

Challenge:	Existing Language Agents rely on a fixed mechanism or a set of mechanisms activated in a predefined order, limiting their adaptation to varied potential task solution structures.
Approach:	They propose to use language agents to learn to activate different mechanisms without relying on expert models to optimize their adaptation to different task solutions.
Outcome:	The proposed approach improves agent performance by enabling it to activate the appropriate mechanisms according to the potential characteristics of the task.

S3Eval: A Synthetic, Scalable, Systematic Evaluation Suite for Large Language Model (2024.naacl-long)

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Fangyu Lei, Qian Liu, Yiming Huang, Shizhu He, Jun Zhao, Kang Liu

Meta Fine-Tuning Neural Language Models for Multi-Domain Text Mining (2020.emnlp-main)

Challenge:	Existing benchmarks fail to evaluate extremely long-context LLMs or analyze their limitations.
Approach:	They propose a Synthetic, Scalable, Systematic evaluation suite for LLMs using SQL execution.
Outcome:	The proposed evaluation suite is able to scale text length and difficulty across scenarios and provides strong correlations with real-world benchmarks.

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Chengyu Wang, Minghui Qiu, Jun Huang, Xiaofeng He

Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce (P18-2)

Challenge:	Pre-trained neural language models improve learning for various NLP tasks by fine-tuning them on task-specific training sets.
Approach:	They propose a meta-learning procedure to fine-tune neural language models on task-specific training sets.
Outcome:	The proposed procedure solves a group of similar NLP tasks on a text mining dataset.

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Minghui Qiu, Liu Yang, Feng Ji, Wei Zhou, Jun Huang, Haiqing Chen, Bruce Croft, Wei Lin

Natural Language Interfaces

Challenge:	Recent researches focus on deep learning and reinforcement learning for multi-turn information seeking conversation systems.
Approach:	They propose an efficient and effective multi-turn conversation model based on convolutional neural networks and extend it to adapt the knowledge learned from a resource-rich domain to enhance the performance.
Outcome:	The proposed model performs better than the existing model on an industrial chatbot called AliMe Assist.

DiffusionSL: Sequence Labeling via Tag Diffusion Process (2023.findings-emnlp)

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Ziyang Huang, Pengfei Cao, Jun Zhao, Kang Liu

Test-Time Code-Switching for Cross-lingual Aspect Sentiment Triplet Extraction (2025.naacl-long)

Challenge:	Sequence Labeling (SL) is a long-standing field of natural language processing.
Approach:	They propose a framework that utilizes a conditional discrete diffusion model for generating discrete tag data.
Outcome:	The proposed framework outperforms gpt-3.5-turbo on multiple benchmark datasets and tasks.

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Dongming Sheng, Kexin Han, Hao Li, Yan Zhang, Yucheng Huang, Jun Lang, Wenqiang Liu

Multilinguality

Challenge:	Aspect Sentiment Triplet Extraction (ASTE) is a thriving research area . current code-switching methods suffer from term boundary detection issues and out-of-dictionary problems.
Approach:	They propose a test-time code-switching framework which bridges the gap between bilingual training and monolingual test- time prediction.
Outcome:	The proposed framework achieves an average improvement of 3.7% on four cross-lingual datasets.

PRAM: An End-to-end Prototype-based Representation Alignment Model for Zero-resource Cross-lingual Named Entity Recognition (2023.findings-acl)

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Yucheng Huang, Wenqiang Liu, Xianli Zhang, Jun Lang, Tieliang Gong, Chen Li

Representation Learning

Challenge:	Existing methods to address the named entity recognition problem are limited and lack explicit optimization specific to the task.
Approach:	They propose a prototype-based representation alignment model for a cross-lingual named entity recognition task using labeled source language data.
Outcome:	The proposed model outperforms existing state-of-the-art methods in some challenging scenarios.

LongHeads: Multi-Head Attention is Secretly a Long Context Processor (2024.findings-emnlp)

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Yi Lu, Xin Zhou, Wei He, Jun Zhao, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang

Green & Sustainable NLP

Challenge:	Large language models struggle to process lengthy inputs due to limited length generalization and attention’s quadratic computational demands.
Approach:	They propose a training-free framework that allows each head to attend to important context chunks instead of allowing each head a full sentence .
Outcome:	The proposed framework unlocks multi-head attention's untapped potential by allowing each head to attend to important context chunks instead of the full sentence.

TransPrompt: Towards an Automatic Transferable Prompting Framework for Few-shot Text Classification (2021.emnlp-main)

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Chengyu Wang, Jianing Wang, Minghui Qiu, Jun Huang, Ming Gao

LONGAGENT: Achieving Question Answering for 128k-Token-Long Documents through Multi-Agent Collaboration (2024.emnlp-main)

Challenge:	Recent studies show that prompts improve performance of large pre-trained language models for few-shot text classification.
Approach:	They propose a prompt-based framework for few-shot learning that captures cross-task transferable knowledge and uses two de-biasing techniques to make it more task-agnostic and unbiased .
Outcome:	The proposed framework outperforms strong baselines over multiple NLP tasks and datasets.

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Jun Zhao, Can Zu, Xu Hao, Yi Lu, Wei He, Yiwen Ding, Tao Gui, Qi Zhang, Xuanjing Huang

Question Answering

Challenge:	Large language models (LLMs) have been successful in understanding language and processing text, but their cost prohibits their practical applications.
Approach:	They propose a multi-agent collaboration method that breaks down lengthy documents into smaller, more manageable chunks and organizes the member agents to read their assigned chunks.
Outcome:	The proposed method achieves 16.42% and 1.63% accuracy gains over existing models on single-hop and multi-hop QA settings.

Training-Free Adaptive Speculative Decoding via Linguistic Priors (2026.findings-acl)

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Jingyi Wang, Jiaqi Huang, Zunnan Xu, Jun Zhou, Kehong Yuan, Xiang Qian

Syntactic Text Processing

Challenge:	Speculative decoding (SPD) is a promising technique to accelerate Large Language Models (LLMs). current approaches neglect the inherent heterogeneity of natural language and fail to distinguish between semantically-rich content and structurally-predictable syntax.
Approach:	They propose a training-free framework that leverages linguistic priors to enable adaptive drafting and verification.
Outcome:	The proposed framework significantly accelerates inference without additional training.

A Reinforced Generation of Adversarial Examples for Neural Machine Translation (2020.acl-main)

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Wei Zou, Shujian Huang, Jun Xie, Xinyu Dai, Jiajun Chen

VideoCLIP-XL: Advancing Long Description Understanding for Video CLIP Models (2024.emnlp-main)

Challenge:	Neural machine translation systems fail on less decent inputs, which may harm the credibility of these systems.
Approach:	They propose a paradigm that generates adversarial examples using reinforcement learning to expose pitfalls for a given performance metric.
Outcome:	The proposed paradigm produces stable attacks with meaning-preserving adversarial examples.

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Jiapeng Wang, Chengyu Wang, Kunzhe Huang, Jun Huang, Lianwen Jin

Diagram-Driven Course Questions Generation (2025.emnlp-main)

Challenge:	Existing studies have shown that CLIP models with short summary texts cannot process extensive textual descriptions due to its text encoder's reliance on positional embeddings with length 77.
Approach:	They propose a Contrastive Language-Image Pre-training (CLIP) model which aims to unleash the long-description understanding capability of video CLIP models.
Outcome:	The proposed model can learn the distribution of feature space while expanding the long description capability.

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Xinyu Zhang, Lingling Zhang, Yanrui Wu, Muye Huang, Wenjun Wu, Bo Li, Shaowei Wang, Basura Fernando, Jun Liu

Visual Data in NLP

Challenge:	Visual Question Generation (VQG) research focuses on natural images while neglecting diagrams, a critical component of educational materials.
Approach:	They propose a diagram-driven course questions generation task to generate diagram-relevant questions for specific courses.
Outcome:	The proposed framework outperforms existing models on DiagramQG while maintaining strong generalizability across natural image datasets.

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

Refining Sentence Embedding Model through Ranking Sentences Generation with Large Language Models (2025.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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Liyang He, Chenglong Liu, Rui Li, Zhenya Huang, Shulan Ruan, Jun Zhou, Enhong Chen

Unveiling Linguistic Regions in Large Language Models (2024.acl-long)

Challenge:	Sentence embedding is essential for many NLP tasks, but reliance on manual labels limits scalability.
Approach:	They propose a method for controlling the generation direction of large language models in the latent space by integrating ranking information and semantic information.
Outcome:	The proposed method achieves new SOTA performance with a modest cost in ranking sentence synthesis.

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Zhihao Zhang, Jun Zhao, Qi Zhang, Tao Gui, Xuanjing Huang

Why Not Act on What You Know? Unleashing Safety Potential of LLMs via Self-Aware Guard Enhancement (2025.findings-acl)

Challenge:	Existing studies on how LLMs achieve cross-lingual alignment and generalization have not explored the intrinsic mechanisms of how they achieve crosslingual alignment.
Approach:	They propose to remove a core region that corresponds to linguistic competence and set parameters to zero to reduce performance across 30 different languages.
Outcome:	The proposed model can be used to perform tasks requiring abstract knowledge and reasoning in complex languages.

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Peng Ding, Jun Kuang, ZongYu Wang, Xuezhi Cao, Xunliang Cai, Jiajun Chen, Shujian Huang

Challenge:	Large Language Models (LLMs) have demonstrated impressive capabilities across various tasks but are vulnerable to meticulously crafted jailbreak attacks.
Approach:	They propose a training-free defense strategy to align LLMs’ strong safety discrimination performance with their relatively weaker safety generation ability.
Outcome:	The proposed strategy achieves an average 99% success rate against numerous complex and covert jailbreak methods while maintaining helpfulness on general benchmarks.

ARTIST: A Transformer-based Chinese Text-to-Image Synthesizer Digesting Linguistic and World Knowledge (2022.findings-emnlp)

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Tingting Liu, Chengyu Wang, Xiangru Zhu, Lei Li, Minghui Qiu, Jun Huang, Ming Gao, Yanghua Xiao

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

Challenge:	Text-to-Image Synthesis (TIS) is a popular task to convert natural language texts into realistic images.
Approach:	They propose a transformer-based Chinese text-to-image synthesizer for high-resolution image generation that incorporates linguistic and relational knowledge facts into the model to ensure better performance without the usage of ultra-large models.
Outcome:	The proposed model outperforms existing models in Chinese with linguistic and relational knowledge facts.

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

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

Commonsense Knowledge Editing Based on Free-Text in LLMs (2024.emnlp-main)

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Xiusheng Huang, Yequan Wang, Jun Zhao, Kang Liu

PRISM: A Framework for Producing Interpretable Political Bias Embeddings with Political-Aware Cross-Encoder (2025.acl-long)

Challenge:	Existing knowledge editing methods focus on editing triple-based facts such as entity-relation pairs and events (multiple triplets).
Approach:	They propose a Knowledge Localization for Free-Text method which uses a Dynamics-aware Module to locate the parameter positions corresponding to commonsense knowledge and a knowledge editing module to update knowledge.
Outcome:	The proposed method exploits the potential of the MLP and Attention layers and edits commonsense knowledge based on free-text.

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Yiqun Sun, Qiang Huang, Anthony Kum Hoe Tung, Jun Yu

Fine-Mem: Fine-Grained Feedback Alignment for Long-Horizon Memory Management (2026.acl-long)

Challenge:	Existing embedding models excel at capturing general meaning, but overlook ideological nuances, limiting their effectiveness in political bias tasks.
Approach:	They propose a framework to Produce inteRpretable polItical biaS eMbeddings.
Outcome:	The proposed framework outperforms state-of-the-art embedding models in political bias classification . the proposed framework offers highly interpretable representations for political analysis .

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Weitao Ma, Xiaocheng Feng, Lei Huang, Xiachong Feng, Zhanyu Ma, Jun Xu, Jiuchong Gao, Jinghua Hao, Renqing He, Bing Qin

From Complex to Simple: Unraveling the Cognitive Tree for Reasoning with Small Language Models (2023.findings-emnlp)

Challenge:	Existing approaches to memory management rely on final task performance as the primary reward, resulting in severe reward sparsity and ineffective credit assignment.
Approach:	They propose a framework for fine-grained feedback alignment using a Chunk-level step reward and Evidence-Anchored Reward Attribution to redistribute global rewards based on memory items utilized as evidence in reasoning.
Outcome:	The proposed framework outperforms baselines and supports generalization across different model configurations and backbones.

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Yan Junbing, Chengyu Wang, Taolin Zhang, Xiaofeng He, Jun Huang, Wei Zhang

Open Set Relation Extraction via Unknown-Aware Training (2023.acl-long)

Challenge:	Existing methods to solve complex logical reasoning problems are cumbersome for language models.
Approach:	They propose to use iterative methodology to construct a cognitive tree using language models . they propose to generate multiple responses by utilizing in-context examples .
Outcome:	The proposed model achieves a performance level comparable to that of GPT-3.5 . the proposed model contains fewer parameters than 5% of the model with 175B parameters .

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Jun Zhao, Xin Zhao, WenYu Zhan, Qi Zhang, Tao Gui, Zhongyu Wei, Yun Wen Chen, Xiang Gao, Xuanjing Huang

On the Role of Long-tail Knowledge in Retrieval Augmented Large Language Models (2024.acl-short)

Challenge:	Existing supervised relation extraction methods can still misclassify unknown relations into known relations due to the lack of supervision signals.
Approach:	They propose a method that regularizes the model by dynamically synthesizing negative instances that can provide the missing supervision signals.
Outcome:	The proposed method achieves SOTA unknown relation detection without compromising the classification of known relations.

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Dongyang Li, Junbing Yan, Taolin Zhang, Chengyu Wang, Xiaofeng He, Longtao Huang, Hui Xue’, Jun Huang

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

Challenge:	Existing RAG methods focus on improving the task performance, without fine-grained process of knowledge.
Approach:	They propose a method that detects long-tail knowledge in large language models by analyzing retrieved documents and enhancing queries indiscriminately with retrieved information.
Outcome:	The proposed method achieves over 4x speedup in average inference time and consistent performance improvement in downstream tasks compared to existing pipelines.

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

Measuring Social Bias in Vision-Language Models with Face-Only Counterfactuals from Real Photos (2026.acl-long)

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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Haodong Chen, Qiang Huang, Jiaqi Zhao, Qiuping Jiang, Xiaojun Chang, Jun Yu

Visual Data in NLP

Challenge:	Vision-Language Models (VLMs) are increasingly deployed in socially consequential settings . attribution under visual confounding is a central challenge in measuring social bias .
Approach:	They propose a face-only counterfactual evaluation paradigm that isolates demographic effects while preserving real-image realism.
Outcome:	The proposed paradigm isolates demographic effects while preserving real-image realism.

EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing (2022.emnlp-demos)

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Chengyu Wang, Minghui Qiu, Taolin Zhang, Tingting Liu, Lei Li, Jianing Wang, Ming Wang, Jun Huang, Wei Lin

Uncertainty-aware Parameter-Efficient Self-training for Semi-supervised Language Understanding (2023.findings-emnlp)

Challenge:	Pre-Trained Models (PTMs) have reshaped the development of natural language processing (NLP) but it is not easy to obtain high-performing PTMs without a large amount of labeled training data and deploy them online with fast inference speed.
Approach:	They propose to make it easy to build NLP applications with knowledge-enhanced pre-training and knowledge distillation.
Outcome:	EasyNLP supports a comprehensive suite of NLP algorithms and features knowledge-enhanced pre-training, knowledge distillation and few-shot learning functionalities.

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Jianing Wang, Qiushi Sun, Nuo Chen, Chengyu Wang, Jun Huang, Ming Gao, Xiang Li

M-TRACE: Detecting and Mitigating Time-Anchor Drift via Step-wise Conflict Checking in Temporal Reasoning (2026.findings-acl)

Challenge:	Existing methods for pre-trained language models rely on noisy data, which can be expensive if all parameters are updated.
Approach:	They propose a self-training framework that incorporates Monte Carlo dropouts into the model and judiciously selects reliable pseudo-labeled examples based on confidence and certainty.
Outcome:	The proposed framework improves performance and efficiency over multiple tasks over multiple datasets.

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Danyu Huang, Jiayuan Jiang, Yao Zhang, Jun Wang, Huijia Li, Zhenglu Yang

Natural Language Interfaces

Challenge:	Experimental results show that M-TRACE effectively reduces time-anchor drift . external knowledge may be inaccurate while internal knowledge can become outdated .
Approach:	They propose a multi-agent reasoning framework for temporal knowledge conflicts . they propose 'TimeConfQA' which guides conflict-aware final reasoning .
Outcome:	Experimental results show that M-TRACE reduces time-anchor drift and improves performance on complex temporal question answering tasks.

Towards Adaptive Prefix Tuning for Parameter-Efficient Language Model Fine-tuning (2023.acl-short)

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Zhen-Ru Zhang, Chuanqi Tan, Haiyang Xu, Chengyu Wang, Jun Huang, Songfang Huang

Reconstructing Event Regions for Event Extraction via Graph Attention Networks (2020.aacl-main)

Challenge:	Parameter-efficient fine-tuning only optimizes a few task-specific parameters with frozen pre-trained model.
Approach:	They propose to optimize a prefix vector inserted into Transformer layers to optimize the prefix . they propose to use a gate mechanism to adjust the prefixed to each layer .
Outcome:	The proposed approach improves on the SuperGLUE and NER datasets.

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Pei Chen, Hang Yang, Kang Liu, Ruihong Huang, Yubo Chen, Taifeng Wang, Jun Zhao

Towards Effective and Efficient Continual Pre-training of Large Language Models (2025.acl-long)

Challenge:	Existing approaches for event extraction focus on sentence-level event extraction, but they lack a broader view of the document context.
Approach:	They build graphs with candidate event filler extractions enriched by sentential embeddings as nodes and use graph attention networks to identify event regions in a document and aggregate event information.
Outcome:	The proposed method performs well on two languages and shows that it is faster than previous methods.

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Jie Chen, Zhipeng Chen, Jiapeng Wang, Kun Zhou, Yutao Zhu, Jinhao Jiang, Yingqian Min, Xin Zhao, Zhicheng Dou, Jiaxin Mao, Yankai Lin, Ruihua Song, Jun Xu, Xu Chen, Rui Yan, Zhewei Wei, Di Hu, Wenbing Huang, Ji-Rong Wen

Rapid Diffusion: Building Domain-Specific Text-to-Image Synthesizers with Fast Inference Speed (2023.acl-industry)

Challenge:	Continual pre-training (CPT) has been an important approach for adapting language models to specific domains or tasks.
Approach:	They propose a Continual pre-training method that can greatly improve Chinese language ability and scientific reasoning ability of LLMs.
Outcome:	The proposed method can greatly improve Chinese language ability and scientific reasoning ability of LLMs.

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Bingyan Liu, Weifeng Lin, Zhongjie Duan, Chengyu Wang, Wu Ziheng, Zhang Zipeng, Kui Jia, Lianwen Jin, Cen Chen, Jun Huang

Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment (2026.acl-long)

Challenge:	Text-to-Image Synthesis (TIS) aims to generate images based on textual inputs . but, current diffusion-based models lack entity knowledge and low inference speed .
Approach:	They propose a framework for training and deploying latent diffusion models with rich entity knowledge injected and optimized networks.
Outcome:	The proposed framework improves image quality and inference speed and can be used in industrial applications.

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Yuming Yang, Mingyoung Lai, Wanxu Zhao, Xiaoran Fan, Zhiheng Xi, Mingqi Wu, Chiyue Huang, Jun Zhao, Haijun Lv, Jian Tong, Yunhua Zhou, Yicheng Zou, Qipeng Guo, Tao Gui, Qi Zhang, Xuanjing Huang

DiffChat: Learning to Chat with Text-to-Image Synthesis Models for Interactive Image Creation (2024.findings-acl)

Challenge:	Existing methods assess suitability primarily through student likelihood, favoring trajectories that align closely with the student model’s current behavior but overlooking more informative ones.
Approach:	They propose a Rank–Surprisal Ratio metric that captures both alignment and informativeness to assess the suitability of a reasoning trajectory.
Outcome:	The proposed metric captures both alignment and informativeness to assess the suitability of a reasoning trajectory.

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Jiapeng Wang, Chengyu Wang, Tingfeng Cao, Jun Huang, Lianwen Jin

KMatrix-2: A Comprehensive Heterogeneous Knowledge Collaborative Enhancement Toolkit for Large Language Model (2025.emnlp-demos)

Challenge:	a novel method to align Large Language Models to "chat" with prompt-as-input Text-to-Image Synthesis models is proposed . a user-specified instruction can be used to create a high quality image .
Approach:	They propose a method to align Large Language Models to "chat" with prompt-as-input Text-to-Image Synthesis models for interactive image creation.
Outcome:	The proposed method can exhibit superior performance than baseline models and strong competitors based on automatic and human evaluations.

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Shun Wu, Di Wu, Wangtao Sun, Ziyang Huang, Xiaowei Yuan, Kun Luo, XueYou Zhang, Shizhu He, Jun Zhao, Kang Liu

GATE: Graph-based Adaptive Tool Evolution Across Diverse Tasks (2026.acl-long)

Challenge:	Existing studies on K-LLMs systems focus on declarative knowledge and procedural knowledge (rules) .
Approach:	They propose to build a toolkit that supports comprehensive heterogeneous knowledge collaborative enhancement for Large Language Models (LLMs).
Outcome:	The proposed toolkit provides unified knowledge integration and joint knowledge retrieval methods to achieve more comprehensive heterogeneous knowledge collaborative enhancement.

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Jianwen Luo, Yiming Huang, Jinxiang Meng, Fangyu Lei, Shizhu He, Xiao Liu, Shanshan Jiang, Bin Dong, Jun Zhao, Kang Liu

Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains (2021.acl-long)

Challenge:	Existing toolsets that use large language models are limited to single-task settings.
Approach:	They propose a framework that dynamically constructs and evolves a hierarchical graph of reusable tools across multiple scenarios.
Outcome:	The proposed framework achieves up to 4.3 faster milestone completion in Minecraft compared to the previous state-of-the-art method and provides an average improvement of 9.23% over existing tool-making methods in code generation tasks and 10.03% in agent tasks.

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Haojie Pan, Chengyu Wang, Minghui Qiu, Yichang Zhang, Yaliang Li, Jun Huang

LogiGraph: Logical Reasoning with Contrastive Learning and Lightweight Graph Networks (2025.coling-main)

Challenge:	Pre-trained language models have been successful in NLP tasks, but their large size and long inference time limit their deployment in real-time applications.
Approach:	They propose a meta-teacher model that captures transferable knowledge across domains and passes it to students.
Outcome:	The proposed model can distill large teacher models into small student models with guidance from the meta-teacher.

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Xiang Li, Chen Shi, Yong Xu, Jun Huang

Mock Worlds, Real Skills: Building Small Agentic Language Models with Synthetic Tasks, Simulated Environments, and Rubric-Based Rewards (2026.acl-long)

Challenge:	Existing methods emphasize contextual semantics while others pay more attention to explicit logical features. Existing models utilize graph convolutional networks (GCN) for node updates, still exhibiting some shortcomings.
Approach:	They propose a logical reasoning method with contrastive learning and lightweight graph networks (LogiGraph) they employ conjunction and punctuation marks as two types of edges to construct a dual graph.
Outcome:	The proposed method improves the GCN and employs conjunction and punctuation marks as two types of edges to construct a dual graph.

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Yuanjie Lyu, Chengyu Wang, Lei Shen, Jun Huang, Tong Xu

S3HQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid Question Answering (2023.acl-short)

Challenge:	Existing agentic training data are narrow in task variety and easily solved . real-world APIs lack diversity and are unstable for large-scale reinforcement learning rollout processes.
Approach:	They propose a framework that synthesizes diverse tool-use training data and simulates complete environments.
Outcome:	The proposed framework synthesizes diverse tool-use training data and simulates complete environments.

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Fangyu Lei, Xiang Li, Yifan Wei, Shizhu He, Yiming Huang, Jun Zhao, Kang Liu

KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering (2022.emnlp-main)

Challenge:	Existing question answering systems use a retriever-reader framework to answer multi-hop questions . existing models lack retrieval, selector, and reasoner capabilities .
Approach:	They propose a three-stage text tableQA framework which comprises of retriever, selector, and reasoner.
Outcome:	The proposed framework outperforms baseline methods in the few-shot setting and ranks first on the HybridQA leaderboard.

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Jianing Wang, Chengyu Wang, Minghui Qiu, Qiuhui Shi, Hongbin Wang, Jun Huang, Ming Gao

Making Every Step Effective: Jailbreaking Large Vision-Language Models Through Hierarchical KV Equalization (2025.findings-emnlp)

Challenge:	Extractive Question Answering (EQA) is one of the most essential tasks in Machine Reading Comprehension (MRC).
Approach:	They propose a framework that transforms extractive question answering into a non-autoregressive Masked Language Modeling (MLM) generation problem.
Outcome:	The proposed framework outperforms state-of-the-art approaches in few-shot learning scenarios by a large margin.

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Shuyang Hao, Yiwei Wang, Bryan Hooi, Jun Liu, Muhao Chen, Zi Huang, Yujun Cai

Reasoning with OmniThought: A Large CoT Dataset with Verbosity and Cognitive Difficulty Annotations (2026.acl-long)

Challenge:	HKVE selectively accepts gradient optimization results based on the distribution of attention scores across different layers, ensuring that every optimization step positively contributes to the attack.
Approach:	They propose a framework that selectively accepts gradient optimization results based on the distribution of attention scores across different layers and selectively takes them into account when calculating the attack success rate.
Outcome:	The proposed framework outperforms existing methods by achieving success rates of 75.08% on MiniGPT4, 85.84% on LLaVA and 81.00% on Qwen-VL.

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Wenrui Cai, Chengyu Wang, Junbing Yan, Jun Huang, Xiangzhong Fang

A Wolf in Sheep’s Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easily (2024.naacl-long)

Challenge:	Existing resources often fail to provide extensive reasoning problems with coherent CoT processes distilled from multiple teacher models.
Approach:	They propose a large-scale dataset featuring 2 million CoT processes generated by multiple powerful LRMs.
Outcome:	The proposed dataset features 2 million CoT processes and is validated by multiple powerful LRMs.

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Peng Ding, Jun Kuang, Dan Ma, Xuezhi Cao, Yunsen Xian, Jiajun Chen, Shujian Huang

FashionKLIP: Enhancing E-Commerce Image-Text Retrieval with Fashion Multi-Modal Conceptual Knowledge Graph (2023.acl-industry)

Challenge:	Existing methods for generating 'jailbreaks' suffer from manual design or require optimization on other white-box models, which compromises either generalization or efficiency.
Approach:	They propose a framework that leverages LLMs to generate effective jailbreak prompts and a generalized framework that can be used to generate prompts.
Outcome:	The proposed framework improves the attack success rate while reducing the time cost compared to baselines.

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Xiaodan Wang, Chengyu Wang, Lei Li, Zhixu Li, Ben Chen, Linbo Jin, Jun Huang, Yanghua Xiao, Ming Gao

Towards Interpretable Clinical Diagnosis with Bayesian Network Ensembles Stacked on Entity-Aware CNNs (2020.acl-main)

Challenge:	Recent advances in visual-language pre-trained (VLP) models have greatly improved cross-modal retrieval performance . however, the fine-grained interactions between objects from different modalities are far from well-established . e-commerce domain lacks sufficient training data and fine-granular cross-modulal knowledge .
Approach:	They propose a visual-language pre-trained (VLP) image-text retrieval model that integrates cross-modal knowledge into the model to improve performance.
Outcome:	The proposed model improves performance on e-commerce image-text retrieval task by a large margin.

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Jun Chen, Xiaoya Dai, Quan Yuan, Chao Lu, Haifeng Huang

Explainability & Interpretability in NLP

Challenge:	a novel framework for text-based diagnosis of diseases requires appropriate balance between accuracy and interpretability.
Approach:	They propose a framework that stacks Bayesian Network Ensembles on top of CNN to build an accurate yet interpretable diagnosis system.
Outcome:	The proposed framework outperforms the previous automatic diagnosis methods in accuracy performance and the diagnosis explanation of the framework is reasonable.

CocaCLIP: Exploring Distillation of Fully-Connected Knowledge Interaction Graph for Lightweight Text-Image Retrieval (2023.acl-industry)

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Jiapeng Wang, Chengyu Wang, Xiaodan Wang, Jun Huang, Lianwen Jin

Self-Demos: Eliciting Out-of-Demonstration Generalizability in Large Language Models (2024.findings-naacl)

Challenge:	Existing methods for text-image retrieval are limited to edge devices and real-time situations due to the substantial indexing and inference time.
Approach:	They propose a fully-Connected knowledge interaction graph technique for cross-modal pre-training distillation.
Outcome:	The proposed method achieves SOTA performances on the widely-used Flickr30K and MSCOCO benchmarks under the lightweight setting.

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Wei He, Shichun Liu, Jun Zhao, Yiwen Ding, Yi Lu, Zhiheng Xi, Tao Gui, Qi Zhang, Xuanjing Huang

RTCFake: Speech Deepfake Detection in Real-Time Communication (2026.findings-acl)

Challenge:	Existing methods that rely on limited demos and out-of-demonstration (OOD) queries fail when faced with out- of-demotion queries.
Approach:	They propose a query-aware prompting method that elicits the inherent generalizability of large language models by query-based demo generation.
Outcome:	The proposed method outperforms state-of-the-art methods in the OOD setting and two public math benchmarks.

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Jun Xue, Zhuolin Yi, Yihuan Huang, Yanzhen Ren, Yujie Chen, Cunhang Fan, Zicheng Su, Yongcheng Zhang, Bo Cai

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

Challenge:	Existing detection studies focus on offline simulations and struggle to cope with complex distortions introduced during RTC transmission.
Approach:	They propose a large-scale speech deepfake dataset tailored for RTC scenarios . the dataset is constructed by transmitting speech through multiple social media and conferencing platforms .
Outcome:	The proposed dataset is constructed by transmitting speech through multiple mainstream social media and conferencing platforms, enabling precise pairing between offline and online speech.

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

Focus-dLLM: Accelerating Long-Context Diffusion LLM Inference via Confidence-Guided Context Focusing (2026.acl-long)

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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Lingkun Long, Yushi Huang, Shihao Bai, Ruihao Gong, Jun Zhang, Ao Zhou, Jianlei Yang

Green & Sustainable NLP

Challenge:	Existing methods for estimating attention importance for tokens are ineffective . dLLMs require bidirectional attention, which limits inference efficiency .
Approach:	They propose a training-free attention sparsification framework for efficient long-context inference . they propose 'sink-aware pruning strategy' to accurately estimate and remove redundant computation .
Outcome:	The proposed approach offers 29 lossless speedup under 32K context length.

Towards Explainable Diagnosis: A Self-learned Explanatory Knowledge Base Approach (2026.acl-long)

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Dongqi Huang, Tong Zhou, Zhuoran Jin, Shenghui Shi, null Maoyujiao, Kang Liu, Jun Zhao, Yubo Chen

Explainability & Interpretability in NLP

Challenge:	Large language models (LLMs) have great potential to facilitate explainable diagnosis, but their effectiveness is often constrained by insufficient diagnostic expertise.
Approach:	They propose a unified LLM-based framework for faithful and explainable diagnosis that builds a high-quality diagnostic knowledge base through a record-driven explanation learning paradigm.
Outcome:	The proposed framework outperforms baselines on the DiReCT and JAMA benchmarks and improves the explanation completeness metric from 64.5% to 76.9% over the best existing methods.

DA-Code: Agent Data Science Code Generation Benchmark for Large Language Models (2024.emnlp-main)

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Yiming Huang, Jianwen Luo, Yan Yu, Yitong Zhang, Fangyu Lei, Yifan Wei, Shizhu He, Lifu Huang, Xiao Liu, Jun Zhao, Kang Liu

Programming Languages in NLP

Challenge:	DA-Code is a code generation benchmark designed to assess LLMs on agent-based data science tasks.
Approach:	They propose a code generation benchmark specifically designed for LLMs on agent-based data science tasks.
Outcome:	The benchmark performs better than existing frameworks, but lacks accuracy . it is based on real-world data, and includes examples that cover a wide range of tasks .

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

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Chengyu Wang, Junbing Yan, Yuanhao Yue, Jun Huang

OAgents: An Empirical Study of Building Effective Agents (2025.findings-emnlp)

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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He Zhu, Tianrui Qin, King Zhu, Heyuan Huang, Yeyi Guan, Jinxiang Xia, Hanhao Li, Yi Yao, Ningning Wang, Pai Liu, Tianhao Peng, Xin Gui, Li Xiaowan, Yuhui Liu, Xiangru Tang, Jian Yang, Ge Zhang, Xitong Gao, Yuchen Eleanor Jiang, Changwang Zhang, Jun Wang, Jiaheng Liu, Wangchunshu Zhou

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

Challenge:	a recent study shows that agent research practices are far from standard, rigorous . lack of a standard evaluation protocol makes previous works not reproducible, authors say .
Approach:	They conduct an empirical study on the GAIA benchmark to investigate agent design choices . they find that lack of a standard evaluation protocol makes previous works not reproducible .
Outcome:	The proposed framework achieves state-of-the-art performance among open-source projects.

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

Meta Distant Transfer Learning for Pre-trained Language Models (2021.emnlp-main)

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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Chengyu Wang, Haojie Pan, Minghui Qiu, Jun Huang, Fei Yang, Yin Zhang

Loose lips sink ships: Mitigating Length Bias in Reinforcement Learning from Human Feedback (2023.findings-emnlp)

Challenge:	Notable PLMs are available for text classification tasks, but performance of PLM on downstream tasks may be limited by the availability of training set.
Approach:	They propose a meta-learning framework to learn the transferable knowledge across tasks using PLMs.
Outcome:	The proposed framework outperforms baselines on seven datasets and is task-agnostic and unbiased.

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Wei Shen, Rui Zheng, Wenyu Zhan, Jun Zhao, Shihan Dou, Tao Gui, Qi Zhang, Xuanjing Huang

Explicitly Capturing Relations between Entity Mentions via Graph Neural Networks for Domain-specific Named Entity Recognition (2021.acl-short)

Challenge:	Experimental results prove that language models can learn from human feedback better, irrespective of sequence length . emergence of length bias often induces the model to favor longer outputs .
Approach:	They propose to separate reward modeling from the influence of sequence length by using the Product-of-Experts technique.
Outcome:	The proposed approach shows that language models perform better regardless of sequence length . the main expert is focused on understanding human intents, while the biased expert targets the identification and capture of length bias.

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Pei Chen, Haibo Ding, Jun Araki, Ruihong Huang

CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark (2022.acl-long)

Challenge:	Named entity recognition (NER) is well studied for the general domain, but the performance is still moderate for specialized domains.
Approach:	They propose to explicitly connect entity mentions based on global coreference relations and local dependency relations to build better entity mention representations.
Outcome:	The proposed system improves the NER performance even with a tiny amount of labeled data.

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Ningyu Zhang, Mosha Chen, Zhen Bi, Xiaozhuan Liang, Lei Li, Xin Shang, Kangping Yin, Chuanqi Tan, Jian Xu, Fei Huang, Luo Si, Yuan Ni, Guotong Xie, Zhifang Sui, Baobao Chang, Hui Zong, Zheng Yuan, Linfeng Li, Jun Yan, Hongying Zan, Kunli Zhang, Buzhou Tang, Qingcai Chen

Challenge:	a new benchmark for biomedical language understanding is being developed in Chinese . most benchmarks are limited to English, which makes it difficult to replicate success in other languages.
Approach:	They propose to use Chinese biomedical language understanding evaluation benchmarks to evaluate Chinese models.
Outcome:	The proposed benchmarks show that the current models perform worse than the human ceiling.

From Recognition to Reasoning: Benchmarking and Enhancing MLLMs on Real-World Receipt Document Understanding (2026.acl-long)

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Yandi Wang, Libin Zhan, Ziwei Huang, Tiancheng Luo, Yuxuan Jiang, Wang Dong, Leilei Gan, Jun Chen

An Effective Incorporating Heterogeneous Knowledge Curriculum Learning for Sequence Labeling (2025.acl-short)

Challenge:	Existing models for visual information extraction suffer from limitations in scale and realism . ReceiptBench is a large-scale, human-annotated benchmark for receipts .
Approach:	They propose a large-scale, human-annotated benchmark for visual information extraction . the method organizes information extraction into four hierarchical sub-tasks .
Outcome:	The proposed method surpasses proprietary models on complex reasoning tasks.

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Xuemei Tang, Jun Wang, Qi Su, Chu-Ren Huang, Jinghang Gu

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

Challenge:	Existing approaches to enhance sequence labeling models require data heterogeneity and additional modules.
Approach:	They propose a dual-stage curriculum learning framework specifically designed for sequence labeling tasks.
Outcome:	The proposed model improves training and accelerates training, mitigating the slow training issue of complex models.

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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

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

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.

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

HumanLLM: Benchmarking and Improving LLM Anthropomorphism via Human Cognitive Patterns (2026.acl-long)

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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Xintao Wang, Jian Yang, Weiyuan Li, Rui Xie, Jen-tse Huang, Jun Gao, Shuai Huang, Yueping Kang, Yuanli Guo, Hongwei Feng, Yanghua Xiao

Linguistics & Cognitive NLP

Challenge:	Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning and generation, serving as the foundation for advanced persona simulation and Role-Playing Language Agents (RPLAs).
Approach:	They propose a framework that treats psychological patterns as interacting causal forces and synthesizes 113 scenarios where 2-5 patterns reinforce, conflict, or modulate each other.
Outcome:	The proposed framework outperforms Qwen3-32B on multi-pattern dynamics despite 4 fewer parameters.

BeautifulPrompt: Towards Automatic Prompt Engineering for Text-to-Image Synthesis (2023.emnlp-industry)

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Tingfeng Cao, Chengyu Wang, Bingyan Liu, Ziheng Wu, Jinhui Zhu, Jun Huang

MathFlow: Enhancing the Perceptual Flow of MLLMs for Visual Mathematical Problems (2026.acl-long)

Challenge:	Recent text-to-image models require multiple passes of prompt engineering by humans to produce satisfactory results for real-world applications.
Approach:	They propose a deep generative model to generate high-quality prompts from raw descriptions using visual feedback.
Outcome:	The proposed model produces high-quality prompts from simple raw descriptions . it can be integrated to a cloud-native AI platform to provide better image generation service in the cloud.

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Shuhang Chen, Hangjie Yuan, Yunqiu Xu, Pengwei Liu, Tao Feng, Jun Cen, Zeying Huang, Yi Yang