Papers by Yan Huang

Towards Explainable Computerized Adaptive Testing with Large Language Model (2024.findings-emnlp)

Challenge:	Existing benchmarks and MLLMs focus on single-image input scenarios, leaving performance of ML models when handling multiple images underexplored.
Approach:	They propose a benchmark to evaluate fine-grained abilities of multimodal large language models in multi-image scenarios.
Outcome:	The proposed benchmark categorizes the multi-image abilities into three scenarios: MII, MKS and MIC.

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Cheng Cheng, GuanHao Zhao, Zhenya Huang, Yan Zhuang, Zhaoyuan Pan, Qi Liu, Xin Li, Enhong Chen

Explainability & Interpretability in NLP

Challenge:	Existing methods focus on minimizing the number of questions required to assess ability, lacking clear and reliable explanations for the question selection process.
Approach:	They propose to use large language models to enhance computer adaptive testing (CAT) by providing human-like interpretability and explanations.
Outcome:	The proposed agent-based CAT performs comparably or superior to traditional CAT methods in accuracy and significantly improves student trust and satisfaction.

Evolving Beyond Snapshots: Harmonizing Structure and Sequence via Entity State Tuning for Temporal Knowledge Graph Forecasting (2026.acl-long)

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Siyuan Li, Yunjia Wu, Yiyong Xiao, Pingyang Huang, Peize Li, Ruitong Liu, Yan Wen, Te Sun

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

Challenge:	Temporal knowledge graphs (TKGs) require predicting future facts by modeling structural dependencies within each snapshot and temporal evolution across snapshots.
Approach:	They propose an encoder-agnostic framework that provides persistent entity states . EST maintains a global state buffer and aligns structural evidence with sequential signals .
Outcome:	Experiments show that EST improves diverse backbones and achieves state-of-the-art performance.

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

CART: A Generative Cross-Modal Retrieval Framework With Coarse-To-Fine Semantic Modeling (2025.acl-long)

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.

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Minghui Fang, Shengpeng Ji, Jialong Zuo, Hai Huang, Yan Xia, Jieming Zhu, Xize Cheng, Xiaoda Yang, Wenrui Liu, Gang Wang, Zhenhua Dong, Zhou Zhao

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

Challenge:	Cross-modal retrieval tasks are used to retrieve data from one modality or another based on a query from another modality.
Approach:	They propose a generative cross-modal retrieval framework based on coarse-to-fine semantic modeling . they propose combining K-Means and RQ-VAE to discretize multimodal data into token sequences that support autoregressive generation.
Outcome:	The proposed framework achieves excellent performance and efficiency in multimodal retrieval tasks.

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

Model Composition for Multimodal Large Language Models (2024.acl-long)

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.

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Chi Chen, Yiyang Du, Zheng Fang, Ziyue Wang, Fuwen Luo, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Maosong Sun, Yang Liu

Pierce the Mists, Greet the Sky: Decipher Knowledge Overshadowing via Knowledge Circuit Analysis (2025.emnlp-main)

Challenge:	Existing methods for creating versatile MLLMs rely on joint training with paired instruction data, which is resource-intensive and challenging to extend to new modalities.
Approach:	They propose a new paradigm for multimodal large language models by reusing modality encoders and merging LLM parameters.
Outcome:	The proposed model retains the modal understanding capabilities of each original model.

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Haoming Huang, Yibo Yan, Jiahao Huo, Xin Zou, Xinfeng Li, Kun Wang, Xuming Hu

Flames: Benchmarking Value Alignment of LLMs in Chinese (2024.naacl-long)

Challenge:	Large Language Models (LLMs) are hampered by hallucinations, a particularly challenging variant, knowledge overshadowing, which can lead to erroneous outputs even with high-quality training data.
Approach:	They propose a framework to analyze and detect knowledge overshadowing by using knowledge circuit analysis to dissect the function of key components in the circuit and how attention pattern dynamics contribute to the phenomenon.
Outcome:	Extensive experiments show that the framework can detect and analyze knowledge overshadowing and improves on existing models.

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Kexin Huang, Xiangyang Liu, Qianyu Guo, Tianxiang Sun, Jiawei Sun, Yaru Wang, Zeyang Zhou, Yixu Wang, Yan Teng, Xipeng Qiu, Yingchun Wang, Dahua Lin

A Graph-based Model for Joint Chinese Word Segmentation and Dependency Parsing (2020.tacl-1)

Challenge:	Existing benchmarks for large language models (LLMs) do not accurately uncover safety vulnerabilities in LLMs.
Approach:	They propose a value alignment benchmark called Flames that encompasses both harmlessness principles and a unique morality dimension that integrates specific Chinese values such as harmony.
Outcome:	The proposed model performs poorly on Flames, particularly in safety and fairness dimensions.

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Hang Yan, Xipeng Qiu, Xuanjing Huang

Syntactic Text Processing

Challenge:	Chinese word segmentation and dependency parsing suffer from error propagation . a graph-based model can integrate both tasks, but it suffers from performance limitations .
Approach:	They propose a graph-based model to integrate Chinese word segmentation and dependency parsing . their model achieves better performance than previous joint models .
Outcome:	The proposed model achieves better performance than previous joint models and state-of-the-art results in both Chinese word segmentation and dependency parsing.

Cross-Lingual Phrase Retrieval (2022.acl-long)

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Heqi Zheng, Xiao Zhang, Zewen Chi, Heyan Huang, Yan Tan, Tian Lan, Wei Wei, Xian-Ling Mao

Cross-Lingual Transfer

Challenge:	Existing approaches to cross-lingual phrase retrieval learn word or sentence representations in word or sentences.
Approach:	They propose a cross-lingual phrase retrieval model that extracts phrase representations from unlabeled example sentences.
Outcome:	The proposed model outperforms state-of-the-art methods on a large-scale cross-lingual phrase retrieval dataset, showing it can perform in an unseen language pair during training.

TrInk: Ink Generation with Transformer Network (2025.emnlp-main)

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Zezhong Jin, Shubhang Desai, Xu Chen, Biyi Fang, Zhuoyi Huang, Zhe Li, Chong-Xin Gan, Xiao Tu, Man-Wai Mak, Yan Lu, Shujie Liu

Where Fact Ends and Fairness Begins: Redefining AI Bias Evaluation through Cognitive Biases (2025.findings-emnlp)

Challenge:	Existing methods for handwriting generation capture global dependencies and can generate high-quality handwritten samples.
Approach:	They propose a Transformer-based model for ink generation, TrInk, which captures global dependencies.
Outcome:	The proposed model reduces character error rate and word error rate by 35.56% on the IAM-OnDB dataset compared to previous models.

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Jen-tse Huang, Yuhang Yan, Linqi Liu, Yixin Wan, Wenxuan Wang, Kai-Wei Chang, Michael R. Lyu

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

Challenge:	Existing benchmarks conflate factual correctness and normative fairness . a model may generate responses that are factually accurate but socially unfair .
Approach:	They propose a benchmark to examine the boundary between fact and fair . they draw on representativeness bias, attribution bias and ingroup–outgroup bias to explain why models often misalign fact and faireness.
Outcome:	The proposed model is based on ten frontier models and is available on github . it is compared with a standard model that generates people of color in Nazi-era uniforms .

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

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

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

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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 .

Budget-Constrained Tool Learning with Planning (2024.findings-acl)

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Yuanhang Zheng, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Yang Liu

To Answer or Not to Answer (TAONA): A Robust Textual Graph Understanding and Question Answering Approach (2025.findings-emnlp)

Challenge:	Existing methods for budget-constrained tool learning have been overlooked . et al., 2023b) compared tool learning with other methods to improve performance .
Approach:	They propose a method for budget-constrained tool learning by creating a preferable plan under the budget constraint before utilizing the tools.
Outcome:	The proposed method reduces the cost of tool learning and reaches competitive Pass Rate.

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Yuchen Yan, Aakash Kolekar, Sahika Genc, Wenju Xu, Edward W Huang, Anirudh Srinivasan, Mukesh Jain, Qi He, Hanghang Tong

Length Generalization of Causal Transformers without Position Encoding (2024.findings-acl)

Challenge:	Existing studies assume that generated answers integrate all relevant information from the textual graph.
Approach:	They propose a novel GraphRAG model that integrates all relevant information from the textual graph into the generated answer.
Outcome:	Extensive experiments validate TAONA’s superior performance for both A-side and B-side tasks.

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Jie Wang, Tao Ji, Yuanbin Wu, Hang Yan, Tao Gui, Qi Zhang, Xuanjing Huang, Xiaoling Wang

Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning (2026.acl-long)

Challenge:	Besides Transformers without position encodings, the success of NoPE provides a new way to overcome the challenge of generalizing to longer sentences.
Approach:	They propose a parameter-efficient tuning for searching attention heads’ best temperature hyper-parameters, which substantially expands NoPE’s context size.
Outcome:	The proposed tuning significantly expands NoPE's context size, allowing it to generalize to longer sentences with state-of-the-art generalization algorithms.

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Sikuan Yan, Xiufeng Yang, Zuchao Huang, Ercong Nie, Zifeng Ding, Zonggen Li, Xiaowen Ma, Jinhe Bi, Kristian Kersting, Jeff Z. Pan, Hinrich Schuetze, Volker Tresp, Yunpu Ma

The Illusion of Specialization: Unveiling the Domain-Invariant "Standing Committee" in Mixture-of-Experts Models (2026.acl-long)

Challenge:	Large Language Models (LLMs) are stateless and limited by a finite context window, preventing them from maintaining knowledge across long conversations or evolving tasks.
Approach:	They propose a reinforcement learning framework that empowers LLMs to actively manage external memory through two specialized agents.
Outcome:	The proposed framework outperforms baselines and benchmarks across diverse question types, three benchmarks, and multiple model scales.

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Yan Wang, Yitao Xu, Nanhan Shen, Jinyan Su, Jimin Huang, Zining Zhu

Challenge:	Mixture of Experts models are widely assumed to achieve domain specialization through sparse routing.
Approach:	They propose a framework that analyzes routing behavior at the level of expert groups rather than individual experts.
Outcome:	The proposed framework analyzes routing behavior at the level of expert groups rather than individual experts.

SocialBench: Sociality Evaluation of Role-Playing Conversational Agents (2024.findings-acl)

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Hongzhan Chen, Hehong Chen, Ming Yan, Wenshen Xu, Gao Xing, Weizhou Shen, Xiaojun Quan, Chenliang Li, Ji Zhang, Fei Huang

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

Challenge:	Existing studies on role-playing agents have focused on enhancing their conversational capability, role-specific knowledge and style, but there has been a gap in assessing their social intelligence.
Approach:	They propose a benchmark to evaluate the sociality of role-playing agents using LLMs.
Outcome:	The proposed benchmark is constructed from various sources and covers a wide range of 500 characters and over 6,000 question prompts and 30,800 multi-turn role-playing utterances.

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

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

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

GMFL: Efficient Global Masking for Federated LLM Fine-tuning (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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Xin Huang, Yan Hu, Yue-Jiao Gong, Xinglin Zhang

Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting (2023.findings-emnlp)

Challenge:	Low-Rank Adaptation (LoRA) has emerged as a prominent solution to mitigate the communication and computation costs in federated fine-tuning of Large Language Models (LLMs).
Approach:	They propose a plug-and-play layer freezing mechanism to integrate with existing federated fine-tuning frameworks.
Outcome:	The proposed solution reduces communication overhead and lowers computational costs while preserving the performance of the underlying federated fine-tuning methods.

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Haoyang Huang, Tianyi Tang, Dongdong Zhang, Xin Zhao, Ting Song, Yan Xia, Furu Wei

Low-Resource NLP

Challenge:	Large language models (LLMs) demonstrate impressive multilingual capability, but their performance varies substantially across different languages.
Approach:	They propose a generic template prompt that stimulates cross-lingual and logical reasoning skills to enhance task performance across languages.
Outcome:	The proposed method improves multilingual capability across languages and covers high-resource and low-resourced languages.

ManuSearch: Democratizing Deep Search in Large Language Models with a Transparent and Open Multi-Agent Framework (2025.findings-emnlp)

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Lisheng Huang, Yichen Liu, Jinhao Jiang, Rongxiang Zhang, Jiahao Yan, Junyi Li, Xin Zhao

Writing-RL: Advancing Long-form Writing via Adaptive Curriculum Reinforcement Learning (2026.acl-long)

Challenge:	Existing systems with opaque architectures are limiting deep search capabilities for web-augmented large language models.
Approach:	They propose a transparent and modular multi-agent framework to democratize deep search for LLMs.
Outcome:	The proposed framework outperforms open-source systems in deep reasoning tasks.

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Xuanyu Lei, Chenliang Li, Yuning Wu, Kaiming Liu, Weizhou Shen, Peng Li, Ming Yan, Fei Huang, Ya-Qin Zhang, Yang Liu

Challenge:	Recent advances in Large Language Models (LLMs) have enabled strong performance in long-form writing, but current training paradigms remain limited.
Approach:	They propose an Adaptive Curriculum Reinforcement Learning framework to advance long-form writing capabilities beyond SFT.
Outcome:	Experiments on 7B-scale writer models show that Writing-RL improves long-form writing performance over strong SFT baselines.

DecoupleSearch: Decouple Planning and Search via Hierarchical Reward Modeling (2025.emnlp-main)

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Hao Sun, Zile Qiao, Bo Wang, Guoxin Chen, Yingyan Hou, Yong Jiang, Pengjun Xie, Fei Huang, Yan Zhang

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

Challenge:	Retrieval-Augmented Generation (RAG) systems have emerged as a pivotal methodology for enhancing Large Language Models (LLMs).
Approach:	They propose a framework that decouples planning and search processes using dual value models, enabling independent optimization of plan reasoning and search grounding.
Outcome:	The proposed framework decouples planning and search processes using dual value models, enabling independent optimization of plan reasoning and search grounding.

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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

mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understanding (2024.findings-emnlp)

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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Anwen Hu, Haiyang Xu, Jiabo Ye, Ming Yan, Liang Zhang, Bo Zhang, Ji Zhang, Qin Jin, Fei Huang, Jingren Zhou

DeepGuard: Secure Code Generation via Multi-Layer Semantic Aggregation (2026.acl-long)

Challenge:	Existing Multimodal Large Language Models lack general structure understanding abilities for text-rich document images.
Approach:	They propose to use unified structure learning to boost the performance of MLLMs by encoding structure information into text-rich images.
Outcome:	The proposed model achieves state-of-the-art on 10 visual document understanding benchmarks.

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Li Huang, Zhongxin Liu, Yifan Wu, Tao Yin, Dong li, Jichao Bi, Nankun Mu, Hongyu Zhang, Meng Yan

Mutual-Taught for Co-adapting Policy and Reward Models (2025.acl-long)

Challenge:	Large Language Models (LLMs) can replicate insecure patterns from training data.
Approach:	They propose a framework that leverages distributed security-relevant cues by aggregating representations from multiple upper layers via an attention-based module.
Outcome:	Experiments show that the framework improves the secure-and-correct generation rate by 11.9% over baselines.

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Tianyuan Shi, Canbin Huang, Fanqi Wan, Longguang Zhong, Ziyi Yang, Weizhou Shen, Xiaojun Quan, Ming Yan

Machine Translation

Challenge:	Experimental results show that this iterative approach leads to consistent improvements in both the policy model and reward model.
Approach:	They propose a method that iteratively improves both the policy model and reward model without requiring additional human annotation.
Outcome:	The proposed method improves both the policy model and reward model without human annotation.

mPLUG-DocOwl2: High-resolution Compressing for OCR-free Multi-page Document Understanding (2025.acl-long)

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Anwen Hu, Haiyang Xu, Liang Zhang, Jiabo Ye, Ming Yan, Ji Zhang, Qin Jin, Fei Huang, Jingren Zhou

Browse and Concentrate: Comprehending Multimodal Content via Prior-LLM Context Fusion (2024.acl-long)

Challenge:	Multimodal Large Language Models (MLLMs) have improved document understanding performance but generate thousands of visual tokens for a single document image, leading to excessive GPU memory and slower inference times.
Approach:	They propose a high-resolution document compression module to generate 324 tokens for a single document image.
Outcome:	The proposed module reduces first token latency by more than 50% and improves document comprehension performance.

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Ziyue Wang, Chi Chen, Yiqi Zhu, Fuwen Luo, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Maosong Sun, Yang Liu

Revealing the Attention Floating Mechanism in Masked Diffusion Models (2026.findings-acl)

Challenge:	Multimodal Large Language Models (MLLMs) lack understanding of multi-image and interleaved inputs due to the visual features encoded by frozen encoders before being fed into the LLM backbone.
Approach:	They propose a two phase paradigm to enable in-depth multimodal context fusion prior to feeding the features into LLMs.
Outcome:	The proposed paradigm boosts the performance on 7 multi-image scenarios, contributing to increments on average accuracy by 2.13% and 7.60% against strong MLLMs baselines with 3B and 11B LLMs, respectively.

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Xin Dai, Pengcheng Huang, Zhenghao Liu, Shuo Wang, Yukun Yan, Chaojun Xiao, Yu Gu, Ge Yu, Maosong Sun

“I’ve Decided to Leak”: Probing Internals Behind Prompt Leakage Intents (2025.emnlp-main)

Challenge:	Masked diffusion models (MDMs) leverage bidirectional attention and a denoising process.
Approach:	They investigate the attention behaviors of Masked diffusion models by revealing the phenomenon of Attention Floating.
Outcome:	The proposed model doubles the performance of autoregressive models in knowledge-intensive tasks.

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Jianshuo Dong, Yutong Zhang, Liu Yan, Zhenyu Zhong, Tao Wei, Ke Xu, Minlie Huang, Chao Zhang, Han Qiu

Concept2Box: Joint Geometric Embeddings for Learning Two-View Knowledge Graphs (2023.findings-acl)

Challenge:	Large language models (LLMs) exhibit prompt leakage vulnerabilities, raising intellectual property and confidentiality concerns.
Approach:	They use probing techniques to capture LLMs’ intent-related internal representations and show that they internalize prompt leakage intents in their hidden states before generating tokens.
Outcome:	The proposed probes achieve 90%+ AUROC across all tested models, even when applied to new system prompts and attacks.

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Zijie Huang, Daheng Wang, Binxuan Huang, Chenwei Zhang, Jingbo Shang, Yan Liang, Zhengyang Wang, Xian Li, Christos Faloutsos, Yizhou Sun, Wei Wang

PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation (2020.emnlp-main)

Challenge:	Existing methods to embed knowledge graphs have ignored the fact that they contain two fundamentally different views: high-level ontology-view concepts and fine-grained instance-view entities.
Approach:	They propose a novel geometric representation that jointly embeds the two views of a KG using dual geometric representations.
Outcome:	Experiments on the public DBpedia KG and a newly-created industrial KG show the proposed method works well.

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Bin Bi, Chenliang Li, Chen Wu, Ming Yan, Wei Wang, Songfang Huang, Fei Huang, Luo Si

LLMs for Mathematical Modeling: Towards Bridging the Gap between Natural and Mathematical Languages (2025.findings-naacl)

Challenge:	Existing techniques for natural language understanding and generation use autoencoding and/or autoregressive objectives to train models.
Approach:	They propose a self-supervised pre-training scheme that pre-trains an autoencoding and autoregressive language model on a large unlabeled corpus for generating new text conditioned on context.
Outcome:	The proposed scheme achieves state-of-the-art results on a variety of language generation benchmarks covering generative question answering, abstractive summarization and conversational response generation.

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Xuhan Huang, Qingning Shen, Yan Hu, Anningzhe Gao, Benyou Wang

Mixture-of-Modules: Reinventing Transformers as Dynamic Assemblies of Modules (2024.emnlp-main)

Challenge:	Large Language Models (LLMs) have demonstrated strong performance across various natural language processing tasks, but their proficiency in mathematical reasoning remains a key challenge.
Approach:	They propose a process-oriented framework to evaluate LLMs' ability to construct mathematical models, using solvers to compare outputs with ground truth.
Outcome:	The proposed framework evaluates LLMs' ability to construct mathematical models, using solvers to compare outputs with ground truth.

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Zhuocheng Gong, Ang Lv, Jian Guan, Wei Wu, Huishuai Zhang, Minlie Huang, Dongyan Zhao, Rui Yan

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

Challenge:	Empirical results show that MoMs consistently outperform vanilla transformers .
Approach:	They propose an architecture that allows for a mixture-of-modules computation that uses a finite set of modules defined by multi-head attention and feed-forward networks.
Outcome:	The proposed architecture outperforms vanilla Transformers and their variants in multiple ways.

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

Adversarial Yet Cooperative: Multi-Perspective Reasoning in Retrieved-Augmented Language Models (2026.findings-acl)

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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Can Xu, Lingyong Yan, Jiayi Wu, Haosen Wang, Shuaiqiang Wang, Yuchen Li, Jizhou Huang, Dawei Yin, Xiang Li

E2E-VLP: End-to-End Vision-Language Pre-training Enhanced by Visual Learning (2021.acl-long)

Challenge:	Existing training paradigms rely on outcome-oriented rewards, which provide insufficient signal for shaping the complex, multi-step reasoning process.
Approach:	They propose a framework that integrates large reasoning models with retrieval-augmented generation to improve reasoning fidelity and verification rigor.
Outcome:	Experiments on multiple benchmarks demonstrate the effectiveness of the proposed framework.

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Haiyang Xu, Ming Yan, Chenliang Li, Bin Bi, Songfang Huang, Wenming Xiao, Fei Huang

Fake Alignment: Are LLMs Really Aligned Well? (2024.naacl-long)

Challenge:	Existing vision-language pre-training methods use a two-step training procedure to learn visual features from image-text pairs.
Approach:	They propose a vision-language pre-trained model for V+L understanding and generation using a unified Transformer framework.
Outcome:	The proposed model can learn visual representation and semantic alignments between image and text on visual-text pairs and on visual processing tasks.

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Yixu Wang, Yan Teng, Kexin Huang, Chengqi Lyu, Songyang Zhang, Wenwei Zhang, Xingjun Ma, Yu-Gang Jiang, Yu Qiao, Yingchun Wang

Overcoming both Domain Shift and Label Shift for Referring Video Segmentation (2025.findings-naacl)

Challenge:	Existing studies on large language models have shown that they are poorly aligned in practice.
Approach:	They propose a framework to evaluate safety in large language models . they propose two new metrics to quantify fake alignment and obtain corrected performance estimation.
Outcome:	The proposed framework and two metrics show that some models with purported safety are poorly aligned in practice.

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Hai Huang, Sashuai Zhou, Yan Xia

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

Challenge:	Existing methods to improve the robustness of open-set domain generalization can only recognize seen objects and mark all unseen objects as “unknown” categories .
Approach:	They propose a method to make the model maintain good segmentation ability for unknown objects . they propose CLIP-based Reasoning Prompt which can combine text and visual prompts .
Outcome:	The proposed method can bridge the gap caused by label shift by combining text and visual prompts to improve text-object matching ability.

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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.

TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities (2023.acl-demo)

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Zhe Zhao, Yudong Li, Cheng Hou, Jing Zhao, Rong Tian, Weijie Liu, Yiren Chen, Ningyuan Sun, Haoyan Liu, Weiquan Mao, Han Guo, Weigang Gou, Taiqiang Wu, Tao Zhu, Wenhang Shi, Chen Chen, Shan Huang, Sihong Chen, Liqun Liu, Feifei Li, Xiaoshuai Chen, Xingwu Sun, Zhanhui Kang, Xiaoyong Du, Linlin Shen, Kimmo Yan

Small LLMs Are Weak Tool Learners: A Multi-LLM Agent (2024.emnlp-main)

Challenge:	Several pre-training models of different modalities are showing a rising trend of homogeneity in their model structures.
Approach:	They propose a toolkit that supports pre-training models of different modalities.
Outcome:	The proposed toolkit can match the performance of the original implementations on text, vision, and audio benchmarks.

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Weizhou Shen, Chenliang Li, Hongzhan Chen, Ming Yan, Xiaojun Quan, Hehong Chen, Ji Zhang, Fei Huang

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

Challenge:	Large Language Models (LLMs) have revolutionized natural language processing with impressive capabilities, but they lack domain specificity, real-time information and face challenges in solving specialized problems.
Approach:	They propose a multi-LLM approach that decomposes the aforementioned capabilities into a planner, caller, and summarizer.
Outcome:	The proposed model outperforms existing models by demonstrating its effectiveness and advantages in tool learning.

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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

STORM-BORN: A Challenging Mathematical Derivations Dataset Curated via a Human-in-the-Loop Multi-Agent Framework (2025.findings-acl)

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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Wenhao Liu, Zhenyi Lu, Xinyu Hu, Jerry Zhang, Dailin Li, Jiacheng Cen, Huilin Cao, Haiteng Wang, Yuhan Li, Xie Kun, Dandan Li, Pei Zhang, Chengbo Zhang, Yuxiang Ren, Xiaohong Huang, Yan Ma

Multi-level Association Refinement Network for Dialogue Aspect-based Sentiment Quadruple Analysis (2025.acl-long)

Challenge:	Existing datasets suffer from outdated and insufficient challenging content, neglecting human-like reasoning, and limited reliability due to single-LLM generation.
Approach:	They propose a human-in-the-loop, multi-agent data generation framework that integrates reasoning-dense filters, multiagent collaboration, and human mathematicians’ evaluations to ensure the reliability and quality of the dataset.
Outcome:	The proposed framework improves accuracy and quality of the 2,000-synthesized datasets by integrating reasoning-dense filters, multi-agent collaboration, and human mathematicians’ evaluations.

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Zeliang Tong, Wei Wei, Xiaoye Qu, Rikui Huang, Zhixin Chen, Xingyu Yan

Sentiment Analysis

Challenge:	Existing methods for identifying quadruples rely on predefined dialogue structure and word semantics to achieve accurate and comprehensive sentiment associations between utterances and words.
Approach:	They propose a multi-level association refinement network to achieve more accurate sentiment associations between utterances and words.
Outcome:	The proposed framework achieves state-of-the-art performance under low-resource conditions.

Same Claim, Different Judgment: Benchmarking Scenario-Induced Bias in Multilingual Financial Misinformation Detection (2026.findings-acl)

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Zhiwei Liu, Yupeng Cao, Yuechen Jiang, Mohsinul Kabir, Polydoros Giannouris, Chen Xu, Ziyang Xu, Tianlei Zhu, Md. Tariquzzaman, Triantafillos Papadopoulos, Yan Wang, Lingfei Qian, Xueqing Peng, Zhuohan Xie, Ye Yuan, Saeed Almheiri, Abdulrazzaq Alnajjar, Ming-Bin Chen, Harry Stuart, Paul Thompson, Prayag Tiwari, Alejandro Lopez-Lira, Xue Liu, Jimin Huang, Sophia Ananiadou

A Concise Model for Multi-Criteria Chinese Word Segmentation with Transformer Encoder (2020.findings-emnlp)

Challenge:	Existing research on LLM biases has focused on direct questioning or general-purpose settings . pronounced behavioral biase despite their growing deployment in financial analysis, forecasting, and decision support.
Approach:	They propose a benchmark to evaluate behavioral biases of large language models in MFMD . they use a multilingual financial misinformation dataset to integrate these with misinformation claims .
Outcome:	The proposed benchmark evaluates behavioral biases of large language models across economic scenarios.

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Xipeng Qiu, Hengzhi Pei, Hang Yan, Xuanjing Huang

GovScape: A Public Multimodal Search System for 70 Million Pages of Government PDFs (2026.acl-demo)

Challenge:	Existing work on multicriteria Chinese word segmentation focuses on combining multiple heterogeneous segmentation criteria into a single task.
Approach:	They propose a unified model for multi-criteria Chinese word segmentation which is fully-shared for all criteria.
Outcome:	The proposed model outperforms existing models on eight datasets with different criteria.

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Ying-Hsiang Huang, Claire Gong, Shreya Shaji, Alison R Yan, Leslie Harka, Albert Du, Anjali Shubha Gopal, Samuel J Klein, Shannon Zejiang Shen, Mark E. Phillips, Trevor Owens, Kyle Deeds, Benjamin Charles Germain Lee

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

Challenge:	Efforts over the past three decades have produced web archives containing billions of webpage snapshots and petabytes of data.
Approach:	They propose a public search system that supports multimodal searches across 10,015,993 federal government PDFs from the 2020 End of Term crawl.
Outcome:	The proposed system supports multimodal searches across 10,015,993 federal government PDFs from the 2020 End of Term crawl (70,958,487 total PDF pages) significant compute cost for GovScape’s pre-processing pipeline for 10 million PDFs was approximately 1,500, equivalent to 47,000 PDF pages per dollar spent on compute.

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

SocialEval: Evaluating Social Intelligence of Large Language Models (2025.acl-long)

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.

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Jinfeng Zhou, Yuxuan Chen, Yihan Shi, Xuanming Zhang, Leqi Lei, Yi Feng, Zexuan Xiong, Miao Yan, Xunzhi Wang, Yaru Cao, Jianing Yin, Shuai Wang, Quanyu Dai, Zhenhua Dong, Hongning Wang, Minlie Huang

Unifying Latent and Lexicon Representations for Effective Video-Text Retrieval (2024.lrec-main)

Challenge:	Existing work on LLMs does not address their social intelligence (SI) and their discrepancy with humans.
Approach:	They propose a script-based bilingual SI benchmark that integrates outcome-oriented goal achievement evaluation and process-oriented interpersonal ability evaluation by manually crafting narrative scripts.
Outcome:	The proposed model is based on a script-based bilingual evaluation paradigm that integrates outcome- and process-oriented evaluation by manually crafting narrative scripts.

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Haowei Liu, Yaya Shi, Haiyang Xu, Chunfeng Yuan, Qinghao Ye, Chenliang Li, Ming Yan, Ji Zhang, Fei Huang, Bing Li, Weiming Hu

TestAgent: An Adaptive and Intelligent Expert for Human Assessment (2025.findings-acl)

Challenge:	Existing methods for video-text retrieval capture fine-grained semantic concepts . however, they lack the ability to capture finer-grain concepts such as objects and actions.
Approach:	They propose a dual-encoder architecture for fast video-text retrieval that learns lexicon representations to capture fine-grained semantics.
Outcome:	The proposed framework outperforms existing methods with 4.8% and 8.2% improvement on MSR-VTT and DiDeMo respectively.

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Junhao Yu, Yan Zhuang, Yuxuan Sun, Weibo Gao, Qi Liu, Mingyue Cheng, Zhenya Huang, Enhong Chen

UReader: Universal OCR-free Visually-situated Language Understanding with Multimodal Large Language Model (2023.findings-emnlp)

Challenge:	Existing adaptive testing methods face several challenges due to mechanized nature of most algorithms and noisy response data.
Approach:	They propose to use large language models to enhance adaptive testing through interactive engagement to capture test-takers’ responses and anomalies.
Outcome:	The proposed agent achieves more accurate results with 20% fewer questions than state-of-the-art baselines and testers preferred it in speed, smoothness, and other dimensions.

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Jiabo Ye, Anwen Hu, Haiyang Xu, Qinghao Ye, Ming Yan, Guohai Xu, Chenliang Li, Junfeng Tian, Qi Qian, Ji Zhang, Qin Jin, Liang He, Xin Lin, Fei Huang

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

Challenge:	Existing studies for visually-situated language understanding have shown shallow zero-shot visual text recognition ability when fed a low-resolution image with salient text information.
Approach:	They propose a model for universal OCR-free visually-situated language understanding based on the Multimodal Large Language Model (MLLM) their model is jointly finetuned on a wide range of visually situated language understanding tasks via a unified instruction format.
Outcome:	The proposed model achieves state-of-the-art ocr-free performance in 8 out of 10 visually-situated language understanding tasks across 5 domains: documents, tables, charts, natural images, and webpage screenshots.

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

SelfAug: Mitigating Catastrophic Forgetting in Retrieval-Augmented Generation via Distribution Self-Alignment (2025.findings-emnlp)

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

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Yuqing Huang, Rongyang Zhang, Qimeng Wang, Chengqiang Lu, Yan Gao, null Yiwu, Yao Hu, Xuyang Zhi, Guiquan Liu, Xin Li, Hao Wang, Enhong Chen

Speculating LLMs’ Chinese Training Data Pollution from Their Tokens (2025.emnlp-main)

Challenge:	Existing solutions for supervised fine-tuning often lead to catastrophic forgetting, where models lose their previously acquired knowledge and general capabilities.
Approach:	They propose a self-distribution alignment method that aligns input sequence logits to preserve the model’s semantic distribution, thereby mitigating catastrophic forgetting and improving downstream performance.
Outcome:	The proposed method achieves a superior balance between downstream learning and general capability retention.

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Qingjie Zhang, Di Wang, Haoting Qian, Liu Yan, Tianwei Zhang, Ke Xu, Qi Li, Minlie Huang, Hewu Li, Han Qiu

Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding (P18-1)

Challenge:	Experiments on GPT and other 23 LLMs indicate that tokens widely exist while GPT’s vocabulary behaves the worst: more than 23% long Chinese tokens (i.e., a token with more than two Chinese characters) are either porn or online gambling.
Approach:	They propose to locate Polluted Chinese (PoC) tokens in LLMs and build a PoC token detector to label them in vocabularies by considering each token’s semantics and related contents from the search engines.
Outcome:	The proposed method predicts that the ratio of “*” related webpages in GPT-4o's training data is around 0.5%.

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Bingfeng Luo, Yansong Feng, Zheng Wang, Songfang Huang, Rui Yan, Dongyan Zhao

Challenge:	Experimental results show that the combination of regular expressions and NNs improves learning effectiveness when a small number of training examples are available.
Approach:	They propose to combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP by exploiting the rich expressiveness of REs at different levels within a NN.
Outcome:	The proposed approach significantly improves learning effectiveness when a small number of training examples are available.

Probing the Safety Robustness of LLMs in Latent Space (2026.acl-long)

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Tianle Gu, Kexin Huang, Zongqi Wang, Yixu Wang, Jie Li, Xin Wang, Yang Yao, Yujiu Yang, Yan Teng, Yingchun Wang

EcoLANG: Efficient and Effective Agent Communication Language Induction for Social Simulation (2025.findings-emnlp)

Challenge:	Despite substantial progress in safety alignment techniques, aligned large language models can still produce unsafe responses under minor internal perturbations.
Approach:	They introduce Activation Steering Attack (ASA) and leverage the Negative Log-Likelihood (NLL) as a diagnostic signal to probe the local sensitivity of safety behaviors in latent space.
Outcome:	The proposed method is model-agnostic and supervision-free, enabling a general and reproducible diagnostic metric for analyzing safety robustness.

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Xinyi Mou, Chen Qian, Wei Liu, Ling Yan, Yao Hu, Xuanjing Huang, Zhongyu Wei

Multilingual Generative Retrieval via Cross-lingual Semantic Compression (2025.findings-emnlp)

Challenge:	Large language models (LLMs) have demonstrated impressive ability to role-play humans and replicate complex social dynamics.
Approach:	They propose an efficient agent communication language induction for social simulations that reduces token consumption by over 20%.
Outcome:	The proposed model reduces token consumption by over 20% while preserving human language.

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Yuxin Huang, Simeng Wu, Ran Song, Yan Xiang, Yantuan Xian, Shengxiang Gao, Zhengtao Yu

DetectBench: Can Large Language Model Detect and Piece Together Implicit Evidence? (2024.findings-emnlp)

Challenge:	Existing methods for multilingual retrieval still face cross-lingual identifier misalignment and identifiere inflation.
Approach:	They propose a framework that unifies semantically equivalent multilingual keywords into shared atoms to align semantics and compresses the identifier space.
Outcome:	The proposed framework improves cross-lingual alignment and reduces redundancy.

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Zhouhong Gu, Lin Zhang, Xiaoxuan Zhu, Jiangjie Chen, Wenhao Huang, Yikai Zhang, Shusen Wang, Zheyu Ye, Yan Gao, Hongwei Feng, Yanghua Xiao

Thinking Economically: A Hierarchical Framework for Adaptive-Complexity Reasoning in LLMs (2026.findings-acl)

Challenge:	Existing LLMs' abilities to detect evidence in long contexts are far inferior to humans.
Approach:	They propose a benchmark to assess LLMs' abilities in evidence and multi-step commonsense reasoning within a long context.
Outcome:	The proposed method improves the performance of LLMs in evidence detection and commonsense reasoning.

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Yubo Gao, Haotian Wu, Hong Chen, Junquan Huang, Yibo Yan, Jungang Li, Zihao Dongfang, Sicheng Tao, PS Tan, Jie Zhang, Xuming Hu

Semantics-enhanced Cross-modal Masked Image Modeling for Vision-Language Pre-training (2024.lrec-main)

Challenge:	Existing efficiency methods for Chain-of-Thought (CoT) generate excessively long rationales without commensurate accuracy gains.
Approach:	They propose a training framework that operationalizes this principle through coarse-to-fine budgeting.
Outcome:	Experiments on GSM8K and MATH500 show that HAB surpasses standard CoT in accuracy and reduces token usage, achieving stronger performance-efficiency trade-off than baselines.

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Haowei Liu, Yaya Shi, Haiyang Xu, Chunfeng Yuan, Qinghao Ye, Chenliang Li, Ming Yan, Ji Zhang, Fei Huang, Bing Li, Weiming Hu

Enhancing Marker Scoring Accuracy through Ordinal Confidence Modelling in Educational Assessments (2025.acl-industry)

Challenge:	Existing methods for vision-language pre-training lack high-level semantics and text is not sufficiently involved in masked modeling.
Approach:	They propose a semantics-enhanced cross-modal MIM framework for vision-language representation learning that harvests high-level semantics from global image features via self-supervised agreement learning and transfers them to local patch encodings by sharing the encode space.
Outcome:	The proposed model achieves state-of-the-art or competitive performance on multiple vision-language tasks.

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Abhirup Chakravarty, Mark Brenchley, Trevor Breakspear, Ian Lewin, Yan Huang

Text Classification

Challenge:	Automated Essay Scoring (AES) systems aim to evaluate the quality of candidate writing using computational methods.
Approach:	They propose a model that assigns a confidence score to each automated score to ensure it meets high reliability standards.
Outcome:	The proposed model achieves an F1 score of 0.97 and releases 47% of predicted scores with 100% CEFR agreement and 99% with at least 95% CEFR agreeance compared to the standalone model where all predicted scores are released.

Addressing Semantic Drift in Generative Question Answering with Auxiliary Extraction (2021.acl-short)

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Chenliang Li, Bin Bi, Ming Yan, Wei Wang, Songfang Huang

Specialization through Collaboration: Understanding Expert Interaction in Mixture-of-Expert Large Language Models (2026.eacl-long)

Challenge:	Recent work focuses on question answering based on machine reading comprehension . current approaches treat QA as extracting a consecutive piece of text to a given question.
Approach:	They propose a generative QA model that incorporates an extractive mechanism into a model.
Outcome:	The proposed model improves quality and semantic accuracy over baseline models.

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Yuanbo Tang, Naifan Zhang, Yan Tang, Meixuan Chen, Shuhan Huang, Tingyu Cao, Yang Li

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

Challenge:	Mixture-of-Experts (MoE) based large language models are popular for multitasking . however, whether each expert can specialize to a task remains unclear .
Approach:	They propose to use a dictionary learning approach to analyze expert collaboration mechanisms in MoE LLMs.
Outcome:	The proposed model outperforms existing methods by 2.5% while enabling 50% expert reduction.

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

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

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

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

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

TinyChart: Efficient Chart Understanding with Program-of-Thoughts Learning and Visual Token Merging (2024.emnlp-main)

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Liang Zhang, Anwen Hu, Haiyang Xu, Ming Yan, Yichen Xu, Qin Jin, Ji Zhang, Fei Huang

ClueAnchor: Clue-Anchored Knowledge Reasoning Exploration and Optimization for Retrieval-Augmented Generation (2025.findings-emnlp)

Challenge:	Recent studies have shown that multimodal large language models can be useful for chart understanding, but their size limits their use in resource-constrained environments.
Approach:	They propose an efficient multimodal large language model with only 3B parameters for chart understanding.
Outcome:	The proposed model outperforms several chart-understanding MLLMs with up to 13B parameters on ChartQA, Chart-to-Text, Chart to Table, OpenCQA, and ChartX.

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Hao Chen, Yukun Yan, Sen Mei, Wanxiang Che, Zhenghao Liu, Qi Shi, Xinze Li, Yuchun Fan, Pengcheng Huang, Qiushi Xiong, Zhiyuan Liu, Maosong Sun

Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models (2024.acl-long)

Challenge:	Existing RAG systems often underutilize the retrieved documents, authors say . they fail to extract and integrate key clues needed to support faithful and interpretable reasoning .
Approach:	a new framework extracts key clues from retrieved content and generates multiple reasoning paths . the framework optimizes the model by selecting the most appropriate reasoning path .
Outcome:	Experiments show that ClueAnchor outperforms baseline RAG frameworks in completeness and robustness.

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Xudong Lu, Qi Liu, Yuhui Xu, Aojun Zhou, Siyuan Huang, Bo Zhang, Junchi Yan, Hongsheng Li

Fortify the Shortest Stave in Attention: Enhancing Context Awareness of Large Language Models for Effective Tool Use (2024.acl-long)

Challenge:	Mixture-of-Experts (MoE) LLMs achieve higher performance with fewer active parameters, but are still difficult to deploy due to their immense parameter sizes.
Approach:	They propose expert-level sparsification techniques to enhance the deployment efficiency of large language models by introducing plug-and-play expert pruning and skipping techniques.
Outcome:	The proposed methods reduce model sizes and increase inference speed while maintaining satisfactory performance across a wide range of tasks.

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Yuhan Chen, Ang Lv, Ting-En Lin, Changyu Chen, Yuchuan Wu, Fei Huang, Yongbin Li, Rui Yan

Distinguish Before Answer: Generating Contrastive Explanation as Knowledge for Commonsense Question Answering (2023.findings-acl)

Challenge:	In this paper, we demonstrate that an inherent waveform pattern in the attention allocation of large language models significantly affects their performance in tasks demanding a high degree of context awareness.
Approach:	They propose a method that compensates an attention trough with an attention peak by a process to enhance the model's awareness to various contextual positions.
Outcome:	The proposed method improves the performance of a 7B model on the largest tool-use benchmark, comparable to that of GPT-4.

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Qianglong Chen, Guohai Xu, Ming Yan, Ji Zhang, Fei Huang, Luo Si, Yin Zhang

Scaling External Knowledge Input Beyond Context Windows of LLMs via Multi-Agent Collaboration (2026.acl-long)

Challenge:	Existing knowledge-enhanced methods have trouble obtaining knowledge from different knowledge bases . a concept-centric model can be used to generate a contrastive explanation for QA tasks .
Approach:	They propose a Concept-centric Prompt-bAsed Contrastive Explanation Generation model which converts obtained symbolic knowledge into the contrastive explanation for better distinguishing the differences among given candidates.
Outcome:	The proposed model achieves new SOTA on CSQA, QASC, and OBQA.

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Zijun Liu, Zhennan Wan, Peng Li, Ming Yan, Fei Huang, Yang Liu

Pointing to Select: A Fast Pointer-LSTM for Long Text Classification (2020.coling-main)

Challenge:	Existing context window extension methods obstruct scaling external knowledge input.
Approach:	They develop a multi-agent framework to overcome two core bottlenecks in existing agent orchestration designs.
Outcome:	The proposed framework overcomes two core bottlenecks and improves inference-time knowledge integration without longer-context training.

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Jinhua Du, Yan Huang, Karo Moilanen

From Evasion to Concealment: Stealthy Knowledge Unlearning for LLMs (2025.findings-acl)

Challenge:	Existing methods to skip irrelevant words in text processing are slow and vanishing gradients can cause slow inference and a loss of coherence.
Approach:	They propose a pointer network-based LSTM framework which can change skip rates during inference.
Outcome:	The proposed model is 1.1x3.5x faster than the standard LSTM framework and more accurate than Leap-LSTM at high skip rates.

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Tianle Gu, Kexin Huang, Ruilin Luo, Yuanqi Yao, Xiuying Chen, Yujiu Yang, Yan Teng, Yingchun Wang

Can LLM Agents Simulate Multi-Turn Human Behavior? Evidence from Real Online Customer Behavior Data (2026.acl-long)

Challenge:	Existing approaches to unlearning often treat nonsensical responses or template-based refusals as the unlearning target, making the process even more vulnerable to attacks and jailbreaks.
Approach:	They propose a method that uses inverted facts to remove the need for auxiliary models or retaining data while avoiding leakage.
Outcome:	Evaluated on the ToFU Knowledge Unlearning dataset using Llama2-7B-Chat and Phi-1.5, MEOW outperforms baselines in forgetting quality while preserving model utility.

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Yuxuan Lu, Jing Huang, Yan Han, Bingsheng Yao, Sisong Bei, Yaochen Xie, Yisi Sang, Qi He, Dakuo Wang

Re-TASK: Revisiting LLM Tasks from Capability, Skill, and Knowledge Perspectives (2025.findings-acl)

Challenge:	Recent research shows that LLM Agents can generate “believable” human behaviors via prompt-only methods, leaving open questions of whether they can accurately generate step-by-step actions in multi-turn interaction tasks.
Approach:	They propose to use shopping data to evaluate LLMs' ability to accurately generate step-by-step actions in a multi-turn interaction task.
Outcome:	The proposed model achieves 17.26% action generation accuracy and 33.86% F1 score on final purchase prediction, representing improvements of 5.4% and 13.85% over baselines.

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Zhihu Wang, Shiwan Zhao, Yu Wang, Heyuan Huang, Sitao Xie, Yubo Zhang, Jiaxin Shi, Zhixing Wang, Hongyan Li, Junchi Yan

Challenge:	Existing approaches to solving complex tasks with large language models (LLMs) fail to decompose tasks accurately or execute subtasks effectively.
Approach:	They propose a Chain-of-Learning (CoL) paradigm that highlights task dependencies on specific capability items, further broken down into their constituent knowledge and skill components.
Outcome:	The proposed model improves Yi-1.5-9B and Llama3-Chinese-8B for legal tasks by 45.00% and 24.50% on different domains.

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

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

Reward Modeling Requires Automatic Adjustment Based on Data Quality (2024.findings-emnlp)

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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Binghai Wang, Rui Zheng, Lu Chen, Zhiheng Xi, Wei Shen, Yuhao Zhou, Dong Yan, Tao Gui, Qi Zhang, Xuanjing Huang

Challenge:	Reinforcement Learning from Human Feedback (RLHF) is a method for aligning language models with human values.
Approach:	They propose a method that automatically adjusts reward modeling based on data quality . they use preference data to train a reward model that is more aligned with human values .
Outcome:	The proposed method stabilizes reward model training and significantly improves alignment performance on human preference datasets.

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

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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

Asymmetric Conflict and Synergy in Post-training for LLM-based Multilingual Machine Translation (2025.findings-acl)

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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Tong Zheng, Yan Wen, Huiwen Bao, Junfeng Guo, Heng Huang

ModelScope-Agent: Building Your Customizable Agent System with Open-source Large Language Models (2023.emnlp-demo)

Challenge:	Existing work in LLM-based MMT typically mitigates the Curse of Multilinguality . asymmetric phenomenon in linguistic conflicts and synergy varies in different translation directions .
Approach:	They propose a direction-aware training approach to address asymmetry in linguistic conflicts and synergy . they propose X-ALMA-13B-Pretrain with multilingual pre-training to achieve comparable performance .
Outcome:	The proposed method achieves comparable performance to X-ALMA-13B-Pretrain (only SFT) with fewer pretraining tokens and 17B parameters.

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Chenliang Li, He Chen, Ming Yan, Weizhou Shen, Haiyang Xu, Zhikai Wu, Zhicheng Zhang, Wenmeng Zhou, Yingda Chen, Chen Cheng, Hongzhu Shi, Ji Zhang, Fei Huang, Jingren Zhou

DatawiseAgent: A Notebook-Centric LLM Agent Framework for Adaptive and Robust Data Science Automation (2025.emnlp-main)

Challenge:	Large language models (LLMs) have demonstrated remarkable capabilities to comprehend human intentions, engage in reasoning, and design planning-like behavior.
Approach:	They propose a framework that equips large language models with tool-use capabilities . they propose LLaMA and Chat-GLM as controllers, and a model-based agent framework .
Outcome:	The proposed framework equips open-source LLMs with tool-use capabilities . it provides a user-friendly system library with a customizable engine design .

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Ziming You, Yumiao Zhang, Dexuan Xu, Yiwei Lou, Yandong Yan, Wei Wang, Huamin Zhang, Yu Huang

Understanding the Dark Side of LLMs’ Intrinsic Self-Correction (2025.acl-long)

Challenge:	Existing large language model (LLM) agents for data science automation are limited by narrow task scopes, limited generalization across tasks and models, and over-reliance on state-of-the-art (SOTA) LLMs.
Approach:	They propose a notebook-centric LLM agent framework for adaptive and robust data science automation.
Outcome:	The proposed framework surpasses baselines such as AutoGen and TaskWeaver in performance tests across diverse data science scenarios and models.

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Qingjie Zhang, Di Wang, Haoting Qian, Yiming Li, Tianwei Zhang, Minlie Huang, Ke Xu, Hewu Li, Liu Yan, Han Qiu

Challenge:	Recent studies show that LLMs’ intrinsic self-correction fails without oracle labels as feedback.
Approach:	They propose to use one simple task and three complex tasks with state-of-the-art LLMs like ChatGPT, Llama, and DeepSeek to interpret LLM's intrinsic self-correction.
Outcome:	The proposed methods reveal the dark side of LLMs’ intrinsic self-correction for different tasks, especially for those failure cases.

SeqAR: Jailbreak LLMs with Sequential Auto-Generated Characters (2025.naacl-long)

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Yan Yang, Zeguan Xiao, Xin Lu, Hongru Wang, Xuetao Wei, Hailiang Huang, Guanhua Chen, Yun Chen

Accelerating BERT Inference for Sequence Labeling via Early-Exit (2021.acl-long)

Challenge:	Existing studies have focused on the potential misuse of large language models (LLMs) however, the ability to align LLMs with human values is still vulnerable to malicious attacks.
Approach:	They propose a red-teaming strategy to enhance LLM safety by using a framework to design jailbreak prompts automatically.
Outcome:	The proposed framework achieves attack success rates of 88% and 60% in cold-start scenarios.

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Xiaonan Li, Yunfan Shao, Tianxiang Sun, Hang Yan, Xipeng Qiu, Xuanjing Huang

MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)

Challenge:	Existing early-exit mechanisms are designed for sequence-level tasks, rather than sequence labeling.
Approach:	They propose to extend sentence-level early-exit to accelerate inference of PTMs . they propose a token-level mechanism that allows partial tokens to exit early at different layers .
Outcome:	The proposed approach can save up to 66%75% inference cost with minimal performance degradation.

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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie

Improving Factual Consistency of News Summarization by Contrastive Preference Optimization (2024.findings-emnlp)

Challenge:	Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models.
Approach:	They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts.
Outcome:	The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings .

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Huawen Feng, Yan Fan, Xiong Liu, Ting-En Lin, Zekun Yao, Yuchuan Wu, Fei Huang, Yongbin Li, Qianli Ma

Text Generation

Challenge:	Recent advances in news summarization have created problems with “hallucinations” that are factually inconsistent with the source text.
Approach:	They propose to disentangle LLMs’ propensities to generate faithful and fake content by adopting a probing-based specific training method to improve their capacity of distinguishing two types of propensity.
Outcome:	The proposed method disentangles LLMs’ propensities to generate faithful and fake content and improves their ability to distinguish between two types of propensity.

Learning Goal-oriented Dialogue Policy with opposite Agent Awareness (2020.aacl-main)

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Zheng Zhang, Lizi Liao, Xiaoyan Zhu, Tat-Seng Chua, Zitao Liu, Yan Huang, Minlie Huang

Chunks as Arms: Multi-Armed Bandit-Guided Sampling for Long-Context LLM Preference Optimization (2026.acl-long)

Challenge:	Existing approaches for goal-oriented dialogue policy learning focus on the target agent policy and treat the opposite agent policy as part of the environment.
Approach:	They propose a framework for policy learning in goal-oriented dialogues that uses the opposite agent's policy estimation to improve the target agent by regarding it as part of the target policy.
Outcome:	The proposed framework shows superior performance over state-of-the-art models on cooperative and competitive dialogue tasks.

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Shaohua Duan, Pengcheng Huang, Xinze Li, Zhenghao Liu, Xiaoyuan Yi, Yukun Yan, Shuo Wang, Yu Gu, Ge Yu, Maosong Sun

Benchmarking Chinese Commonsense Reasoning of LLMs: From Chinese-Specifics to Reasoning-Memorization Correlations (2024.acl-long)

Challenge:	Recent studies have explored fine-tuning Large Language Models with synthetic data to enhance their long-context capabilities.
Approach:	They propose a framework that leverages a Multi-Armed Bandit rollout strategy to identify the most informative chunks from the given long context for sampling high-quality and diverse responses.
Outcome:	The proposed framework achieves 4% improvement on long-context reasoning benchmarks on Llama and Qwen.

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Jiaxing Sun, Weiquan Huang, Jiang Wu, Chenya Gu, Wei Li, Songyang Zhang, Hang Yan, Conghui He

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

Challenge:	Currently, many benchmarks evaluate the commonsense reasoning of large language models (LLMs), but most are English-based, limiting non-English evaluations.
Approach:	They propose to use Chinese commonsense reasoning to evaluate LLMs' commonsensing ability.
Outcome:	The proposed benchmark covers both globally known and Chinese-specific commonsense reasoning abilities and can be used as a reference for future research.

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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

Reasoning While Asking: Transforming Reasoning Large Language Models from Passive Solvers to Proactive Inquirers (2026.acl-long)

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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Xin Chen, Feng Jiang, Yiqian Zhang, Hardy Chen, Shuo Yan, Wenya Xie, Min Yang, Shujian Huang

Wenqiang Wang, Peng Chen, Yan Xiao, Yangshijie Zhang, Xiaoyue Lu, Jianjie Huang, Xiaochun Cao

Challenge:	Existing reasoning-oriented LLMs lack a blind self-thinking paradigm . current models fail to recognize when their reasoning is underinformed or based on ambiguous user instructions .
Approach:	They propose a new reasoning paradigm that transforms LLMs into proactive inquirers that interleave reasoning with clarification.
Outcome:	The proposed model outperforms baseline models on mathematical reasoning, code generation, and document editing.

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F-Eval: Asssessing Fundamental Abilities with Refined Evaluation Methods (2024.acl-long)

Challenge:	Standard in-context learning assumes identical output spaces between test and retrieval datasets . however, in practice, these datasets can be fully aligned, partially alignes, or fully disjoint in label space .
Approach:	They propose a framework for in-context learning under output-space mismatch . they identify demonstrations relevant to the test label space via a Bayesian probabilistic criterion .
Outcome:	The proposed framework achieves state-of-the-art results across three LLMs, three task types, and four datasets.

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Yu Sun, Keyuchen Keyuchen, Shujie Wang, Peiji Li, Qipeng Guo, Hang Yan, Xipeng Qiu, Xuanjing Huang, Dahua Lin

Challenge:	Large language models (LLMs) have been evaluated for their instruction-following capabilities but lack references to their fundamental abilities.
Approach:	They propose a bilingual evaluation benchmark to evaluate the fundamental abilities of large language models including expression, commonsense and logic.
Outcome:	The proposed evaluation methods show higher correlation coefficients and larger distinction than other evaluators.

Gender-tuning: Empowering Fine-tuning for Debiasing Pre-trained Language Models (2023.findings-acl)

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Somayeh Ghanbarzadeh, Yan Huang, Hamid Palangi, Radames Cruz Moreno, Hamed Khanpour

MUSEG: Reinforcing Video Temporal Understanding via Timestamp-Aware Multi-Segment Grounding (2026.acl-long)

Challenge:	Existing methods for debiasing are resource-intensive and costly. Existing solutions for debiansing require fine-tuning on downstream tasks.
Approach:	They propose to integrate Masked Language Modeling (MLM) training objectives into fine-tuning’s training process to debiase the PLMs.
Outcome:	The proposed approach outperforms the state-of-the-art baselines in terms of gender bias scores while improving PLMs’ performance solely using the downstream tasks’ dataset.

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Fuwen Luo, Shengfeng Lou, Chi Chen, Ziyue Wang, Chenliang Li, Weizhou Shen, Jiyue Guo, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Yang Liu

UCFE: A User-Centric Financial Expertise Benchmark for Large Language Models (2025.findings-naacl)

Challenge:	Existing methods for MLLMs struggle with fine-grained temporal reasoning . despite advances in video understanding, current methods struggle with time-sensitive tasks .
Approach:	They propose a time-stamp-aware multi-segment grounding method that enhances temporal understanding by introducing timestamps.
Outcome:	The proposed method outperforms existing methods on time-sensitive tasks and generalizes well across diverse temporal understanding scenarios.

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Yuzhe Yang, Yifei Zhang, Yan Hu, Yilin Guo, Ruoli Gan, Yueru He, Mingcong Lei, Xiao Zhang, Haining Wang, Qianqian Xie, Jimin Huang, Honghai Yu, Benyou Wang

Enhancing Multimodal Unified Representations for Cross Modal Generalization (2025.findings-acl)

Challenge:	Recent advances in large language models (LLMs) have expanded their potential applications in finance.
Approach:	They propose a framework to evaluate the ability of large language models to handle financial tasks using human expert evaluations and task-specific interactions.
Outcome:	The proposed framework evaluates the ability of large language models to handle complex financial tasks and combines human expert evaluations with dynamic, task-specific interactions to simulate the complexities of evolving financial scenarios.

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Hai Huang, Yan Xia, Shengpeng Ji, Shulei Wang, Hanting Wang, Minghui Fang, Jieming Zhu, Zhenhua Dong, Sashuai Zhou, Zhou Zhao

Contrastive Instruction Tuning (2024.findings-acl)

Challenge:	Existing studies on discrete unified representations overlook important distinctions between different dimensions of features.
Approach:	They propose to use a codebook to optimize unified representations from pretraining and fine- and coarse-grained disentangling to optimize the representations.
Outcome:	The proposed methods improve the interpretability of multimodal unified representations . they use training-free optimization of codebook and fine and coarse cross-modal disentangling .

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Tianyi Yan, Fei Wang, James Y. Huang, Wenxuan Zhou, Fan Yin, Aram Galstyan, Wenpeng Yin, Muhao Chen

DAM: Dynamic Attention Mask for Long-Context Large Language Model Inference Acceleration (2025.findings-acl)

Challenge:	Current LLMs exhibit limited robustness to unseen instructions, generating inconsistent outputs when the same instruction is phrased with slightly varied forms or language styles.
Approach:	They propose a method which maximizes the similarity between the hidden representations of semantically equivalent instruction-instance pairs while minimizing the similarities between semantically different ones.
Outcome:	Experiments on the PromptBench benchmark show that Contrastive Instruction Tuning improves LLMs’ robustness to unseen instructions with variations across character, word, sentence, and semantic levels by +2.5% in accuracy.

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Hanzhi Zhang, Heng Fan, Kewei Sha, Yan Huang, Yunhe Feng

Context-aware Difference Distilling for Multi-change Captioning (2024.acl-long)

Challenge:	Long-context understanding is crucial for many NLP applications, but transformers struggle with efficiency due to quadratic complexity of self-attention.
Approach:	They propose a dynamic sparse attention mechanism that assigns adaptive masks at the attention-map level, preserving heterogeneous attention patterns.
Outcome:	The proposed method achieves high alignment with full-attention models while reducing memory and compute overhead.

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Yunbin Tu, Liang Li, Li Su, Zheng-Jun Zha, Chenggang Yan, Qingming Huang

BrainLoc: Brain Signal-Based Object Detection with Multi-modal Alignment (2025.findings-emnlp)

Challenge:	Existing methods for multi-change captioning are difficult because it requires a higher level of cognition to reason an arbitrary number of changes.
Approach:	They propose a context-aware difference distilling network to capture all genuine changes for yielding sentences.
Outcome:	The proposed network captures all genuine changes for yielding sentences on three public datasets.

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Jiaqi Duan, Xiaoda Yang, Kaixuan Luan, Hongshun Qiu, Weicai Yan, Xueyi Zhang, Youliang Zhang, Zhaoyang Li, Donglin Huang, JunYu Lu, Ziyue Jiang, Xifeng Yang

MM-Doc-R1: Training Agents for Long Document Visual Question Answering through Multi-turn Reinforcement Learning (2026.findings-acl)

Challenge:	BrainLoc is a lightweight object detection model guided by fMRI signals.
Approach:	They propose a brain-based object detection model guided by fMRI signals . they employ a multi-modal alignment strategy that enhances fmr feature extraction .
Outcome:	The proposed model improves fMRI-based object detection accuracy and convenience.

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Jiahang Lin, Kai Hu, Binghai Wang, Yuhao Zhou, Zhiheng Xi, Honglin Guo, Shichun Liu, Junzhe Wang, Shihan Dou, Enyu Zhou, Hang Yan, Zhenhua Han, Tao Gui, Qi Zhang, Xuanjing Huang

Attention Entropy is a Key Factor: An Analysis of Parallel Context Encoding with Full-attention-based Pre-trained Language Models (2025.acl-long)

Challenge:	Existing work on long document visual question answering is based on Retrieval-Augmented Generation (RAG) where textual or visual content is encoded into embeddings and relevance is determined by similarity scores with respect to the original query.
Approach:	They propose a framework that employs an agentic, vision-aware workflow to address long document visual question answering through iterative information discovery and synthesis.
Outcome:	The proposed framework outperforms existing RL systems by 10.4% on the MMLongbench-Doc benchmark and demonstrates superior training performance over GRPO.

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Zhisong Zhang, Yan Wang, Xinting Huang, Tianqing Fang, Hongming Zhang, Chenlong Deng, Shuaiyi Li, Dong Yu

ESC-Eval: Evaluating Emotion Support Conversations in Large Language Models (2024.emnlp-main)

Challenge:	Large language models have demonstrated remarkable performance across a wide range of language tasks due to their remarkable ability in context modeling.
Approach:	They propose to use parallel context encoding to reduce attention entropy by incorporating attention sinks and selective mechanisms to reduce irregular attention . they also propose to incorporate attention sink mechanisms into the parallel encoded context to reduce the irregular attention.
Outcome:	The proposed methods lower irregular attention entropy and narrow performance gaps.

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Haiquan Zhao, Lingyu Li, Shisong Chen, Shuqi Kong, Jiaan Wang, Kexin Huang, Tianle Gu, Yixu Wang, Jian Wang, Liang Dandan, Zhixu Li, Yan Teng, Yanghua Xiao, Yingchun Wang

Projecting Out the Malice: A Global Subspace Approach to LLM Detoxification (2026.acl-long)

Challenge:	Emotion Support Conversation (ESC) is a crucial application for reducing stress and providing emotional guidance.
Approach:	They re-organize 2,801 role-playing cards to define roles of role-players . they train a specific role- playing model called ESC-Role which behaves more like a confused person than GPT-4 .
Outcome:	The proposed model behaves more like a confused person than GPT-4, and the model performs better than GPLs.

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Zenghao Duan, Zhiyi Yin, Zhichao Shi, Liang Pang, Shaoling Jing, Zihe Huang, Jiayi Wu, Yu Yan, Jingcheng Deng, Huawei Shen, Xueqi Cheng

Challenge:	Large language models (LLMs) exhibit exceptional performance but pose inherent risks of generating toxic content.
Approach:	They propose a method that removes toxic subspaces from FFN parameters . they propose to use a lightweight method to eliminate toxic subespaces .
Outcome:	The proposed method achieves SOTA detoxification while preserving general capabilities without large-scale retraining.

GRPO-Guided Modality Selection Enhanced LoRA-Tuned LLMs for Multimodal Emotion Recognition (2025.findings-emnlp)

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Yang Chen, Shuwan Yang, Yan Xiang, Ran Song, Yuxin Huang, Zhengtao Yu

Sentiment Analysis

Challenge:	Multimodal emotion recognition in conversation (MERC) aims to identify speakers’ emotional states by utilizing text, audio, and visual modalities.
Approach:	They propose an adaptive modality selection framework for multimodal emotion recognition in conversation that integrates all available modalities into one .
Outcome:	The proposed framework outperforms existing methods on multimodal dialogue datasets and is available at https://github.com/youflyaway/Modality-Selection-Enhanced-LoRA-Tuned-LLMs.

Effidit: An Assistant for Improving Writing Efficiency (2023.acl-demo)

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Shuming Shi, Enbo Zhao, Wei Bi, Deng Cai, Leyang Cui, Xinting Huang, Haiyun Jiang, Duyu Tang, Kaiqiang Song, Longyue Wang, Chenyan Huang, Guoping Huang, Yan Wang, Piji Li

Retrieved In-Context Principles from Previous Mistakes (2024.emnlp-main)

Challenge:	Effidit is a digital writing assistant that provides three modules to help users write faster and more efficiently.
Approach:	They present Effidit, a digital writing assistant that provides three modules to help users write higher-quality text more efficiently.
Outcome:	Effidit expands the capabilities of a typical writing assistant by providing three modules . Effit can help users create their own text faster and more efficiently .

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Hao Sun, Yong Jiang, Bo Wang, Yingyan Hou, Yan Zhang, Pengjun Xie, Fei Huang

Low-Resource NLP

Challenge:	Recent advances in in-context learning (ICL) have limited customization and inadequate error coverage.
Approach:	They propose a method to retrieve in-context principles from mistakes to improve model performance.
Outcome:	The proposed framework enhances model performance when applied to various prompting strategies.

Mitigating Hallucinations in Large Vision-Language Models via Entity-Centric Multimodal Preference Optimization (2025.emnlp-main)

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Jiulong Wu, Zhengliang Shi, Shuaiqiang Wang, Jizhou Huang, Dawei Yin, Lingyong Yan, Min Cao, Min Zhang

ExpandR: Teaching Dense Retrievers Beyond Queries with LLM Guidance (2025.emnlp-main)

Challenge:	Existing preference alignment methods focus on aligning model responses with human preferences while neglecting image-text modality alignment.
Approach:	They propose Entity-centric Multimodal Preference Optimization to improve modality alignment . they use open-source instruction datasets to automatically construct high-quality preference data .
Outcome:	The proposed approach reduces hallucination rates by 80.4% on Object HalBench and 52.6% on MM HalBech.

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Sijia Yao, Pengcheng Huang, Zhenghao Liu, Yu Gu, Yukun Yan, Shi Yu, Ge Yu

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

Challenge:	Existing methods for enhancing dense retrieval with query augmentation ignore the alignment between generation and ranking objectives.
Approach:	They propose a unified LLM-augmented dense retrieval framework that jointly optimizes both the LLM and the retriever.
Outcome:	Experimental results show that ExpandR outperforms strong baselines, achieving more than 5% improvement in retrieval performance.

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

Empirical Analysis of Decoding Biases in Masked Diffusion Models (2026.acl-long)

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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Pengcheng Huang, Tianming Liu, Zhenghao Liu, Yukun Yan, Shuo Wang, Tong Xiao, Zulong Chen, Maosong Sun

DAST: Difficulty-Adaptive Slow-Thinking for Large Reasoning Models (2025.emnlp-industry)

Challenge:	Existing MDMs employ uncertainty-based decoding strategies that limit their reasoning ability and ultimately degrade generation quality.
Approach:	They propose a framework that regularizes uncertainty-based decoding by incorporating two complementary priors to shape global decoding trajectories and promote content informativeness.
Outcome:	The proposed framework outperforms existing decoding strategies by more than 7% while achieving comparable performance to autoregressive models of similar parameter scales.

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Yi Shen, Jian Zhang, Jieyun Huang, Shuming Shi, Wenjing Zhang, Jiangze Yan, Ning Wang, Kai Wang, Zhaoxiang Liu, Shiguo Lian

Semantic matching for text classification with complex class descriptions (2023.emnlp-main)

Challenge:	Recent advances in slow-thinking reasoning models have shown exceptional performance in complex reasoning tasks.
Approach:	They propose a framework that enables models to automatically adjust Chain-of-Thought (CoT) length based on problem difficulty.
Outcome:	The proposed framework penalizes inefficiency on simple problems while incentivizing deep reasoning for complex ones.

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Brian De Silva, Kuan-Wen Huang, Gwang Lee, Karen Hovsepian, Yan Xu, Mingwei Shen

Analyzing Large Language Models’ Capability in Location Prediction (2024.lrec-main)

Challenge:	Existing methods for text classification support zero-shot learning but not both . Existing approaches do not support zero or few-shot, and are insufficient for complex classes .
Approach:	They propose a method which rapidly adapts from seen classes to new/unseen ones . they use labels and complex class descriptions to perform zero- and few-shot learning .
Outcome:	The proposed method beats baselines on complex class descriptions by 22.48% . it also improves zero-shot learning by 4.29% .

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Zhaomin Xiao, Eduardo Blanco, Yan Huang

Lost in the Context: Insufficient and Distracted Attention to Contexts in Preference Modeling (2025.acl-long)

Challenge:	Large language models (LLMs) are underutilized in the field of location prediction due to the sparsity of geotagged tweets.
Approach:	They present experimental results with four large language models in various instruction finetuning and exemplar settings and analyze whether taking into account the context is beneficial.
Outcome:	The proposed model is able to predict location in a variety of settings, including fine tuning and exemplar settings, and it is compared with the best model in the literature.

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Shihan Dou, Jiayi Chen, Chenhao Huang, Feng Chen, Wei Chengzhi, Huiyuan Zheng, Shichun Liu, Yan Liu, Chenxiao Liu, Chao Xin, Lin Yan, Zongzhang Zhang, Tao Gui, Qi Zhang, Xuanjing Huang

Challenge:	Existing reward models concatenate contexts and responses, but they often ignore crucial segments of the context that are important for evaluating the response quality.
Approach:	They propose a reward model that evaluates the response quality based on a given context and assigns a rewards reward.
Outcome:	The proposed framework significantly improves preference modeling by increasing attention to relevant information within the context and achieves better generalizability.

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

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

Calibrating the Confidence of Large Language Models by Eliciting Fidelity (2024.emnlp-main)

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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Mozhi Zhang, Mianqiu Huang, Rundong Shi, Linsen Guo, Chong Peng, Peng Yan, Yaqian Zhou, Xipeng Qiu

RecBase: Generative Foundation Model Pretraining for Zero-Shot Recommendation (2025.emnlp-main)

Challenge:	Large language models with RLHF and RLAIF have good alignment but exhibit overconfidence post-alignment.
Approach:	They propose a plug-and-play method to estimate the confidence of large language models.
Outcome:	The proposed method has shown good calibration performance on 6 RLHF-LMs on four MCQA datasets.

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Sashuai Zhou, Weinan Gan, Qijiong Liu, Ke Lei, Jieming Zhu, Hai Huang, Yan Xia, Ruiming Tang, Zhenhua Dong, Zhou Zhao

Robust Natural Language Understanding with Residual Attention Debiasing (2023.findings-acl)

Challenge:	Existing methods for addressing item-level user interests are lacking in cross-domain generalization . RecBase model is domain-agnostic and can be used to enhance recommender systems' effectiveness .
Approach:	They propose a domain-agnostic foundational model pretrained with a recommendation-oriented objective that leverages a large-scale, heterogeneous, cross-domain corpus with unified textual representations and feature mappings to enhance cross- domain generalization.
Outcome:	The proposed model matches or surpasses baselines in zero-shot and cross-domain recommendation tasks on eight real-world datasets.

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Fei Wang, James Y. Huang, Tianyi Yan, Wenxuan Zhou, Muhao Chen

StructuralLM: Structural Pre-training for Form Understanding (2021.acl-long)

Challenge:	Existing ensemble-based debiasing methods do not address unintended dataset biases . attention plays a crucial role in providing robust prediction in NLU models .
Approach:	They propose an end-to-end debiasing method that mitigates unintended biases from attention.
Outcome:	The proposed method improves the OOD performance of BERT-based models on three benchmarks.

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Chenliang Li, Bin Bi, Ming Yan, Wei Wang, Songfang Huang, Fei Huang, Luo Si

SportQA: A Benchmark for Sports Understanding in Large Language Models (2024.naacl-long)

Challenge:	Existing pre-trained language models focus on text-only representation, neglecting cell-level layout information.
Approach:	They propose a pre-training approach to leverage cell and layout information from scanned documents.
Outcome:	The proposed model achieves state-of-the-art in various downstream tasks . it uses 2Dposition embeddings to model word-level layout information .

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Haotian Xia, Zhengbang Yang, Yuqing Wang, Rhys Tracy, Yun Zhao, Dongdong Huang, Zezhi Chen, Yan Zhu, Yuan-fang Wang, Weining Shen

ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems (2020.acl-demos)

Challenge:	SportQA is a benchmark specifically designed for evaluating Large Language Models (LLMs) sports knowledge is characterized by its fast pace, variety of types, abundance of strategies, and rich player narratives .
Approach:	They propose a benchmark specifically designed for evaluating Large Language Models in the context of sports understanding.
Outcome:	The proposed benchmark aims to bridge the gap between existing and specialized benchmarks in sports understanding.

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Qi Zhu, Zheng Zhang, Yan Fang, Xiang Li, Ryuichi Takanobu, Jinchao Li, Baolin Peng, Jianfeng Gao, Xiaoyan Zhu, Minlie Huang

Deep-Reporter: Deep Research for Grounded Multimodal Long-Form Generation (2026.acl-long)

Challenge:	ConvLab-2 inherits Convlab's framework but integrates more powerful dialogue models and supports more datasets.
Approach:	They present ConvLab-2, an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models and perform an end-to-end evaluation.
Outcome:	The new tool inherits ConvLab's framework and extends it by integrating many recently proposed state-of-the-art dialogue models.

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Fangda Ye, Kuicai Dong, Xie Zhifei, Yuxin Hu, Yihang Yin, Shurui Huang, Shikai Dong, Chen Zhang, Jianzhu Bao, Shuicheng Yan

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

Challenge:	Recent agentic search frameworks are text-centric, overlooking multimodal evidence . a pressing task is multimodal long-form generation, a new paper argues .
Approach:	They propose a unified agentic framework for grounded multimodal long-form generation.
Outcome:	The proposed framework is based on a unified agentic framework for grounded multimodal long-form generation.

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

Scene-robust Natural Language Video Localization via Learning Domain-invariant Representations (2023.findings-acl)

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

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Zehan Wang, Yang Zhao, Haifeng Huang, Yan Xia, Zhou Zhao

StepCoder: Improving Code Generation with Reinforcement Learning from Compiler Feedback (2024.acl-long)

Challenge:	Recent studies have focused on improving performance with the assumption of independently identical data distribution while ignoring out-of-distribution data.
Approach:	They propose a scene-robust NLVL problem and a generalizable framework to learn a robust model.
Outcome:	The proposed model learns generalizable domain-invariant representations by alignment and decomposition.

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Shihan Dou, Yan Liu, Haoxiang Jia, Enyu Zhou, Limao Xiong, Junjie Shan, Caishuang Huang, Xiao Wang, Xiaoran Fan, Zhiheng Xi, Yuhao Zhou, Tao Ji, Rui Zheng, Qi Zhang, Tao Gui, Xuanjing Huang

MCP-Flow: Facilitating LLM Agents to Master Real-World, Diverse and Scaling MCP Tools (2026.acl-long)

Challenge:	Existing work integrates reinforcement learning with compiler feedback to enhance code generation quality but the long code generated by LLMs makes RL exploration ineffective.
Approach:	They propose a framework that integrates reinforcement learning and compiler feedback to enhance code generation quality.
Outcome:	The proposed framework outperforms state-of-the-art approaches in corresponding benchmarks and integrates reinforcement learning with compiler feedback to improve code generation quality.

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WenHao Wang, Peizhi Niu, Zhao Xu, Zhaoyu Chen, Jian Du, Yaxin Du, Xianghe Pang, Keduan Huang, Yanfeng Wang, Qiang Yan, Siheng Chen

EvoRoute: Experience-Driven Self-Routing LLM Agent Systems (2026.acl-long)

Challenge:	Existing research on Large Language Models (LLMs) relies on few servers and lacks training support.
Approach:	They propose a web-agent-driven pipeline for large-scale server discovery, data synthesis, and model training that collects and filters data from 1166 servers and 11536 tools.
Outcome:	Empirical evidence shows that MCP-Flow generates higher quality instruction-function call pairs and higher agentic task performance than previous work.

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Guibin Zhang, Haiyang Yu, Kaiming Yang, Bingli Wu, Fei Huang, Yongbin Li, Shuicheng Yan

Triad: A Framework Leveraging a Multi-Role LLM-based Agent to Solve Knowledge Base Question Answering (2024.emnlp-main)

Challenge:	EvoRoute is a self-evolving model routing paradigm that transcends static, pre-defined model assignments.
Approach:	They propose a model routing paradigm that transcends static, pre-defined model assignments.
Outcome:	Experiments on GAIA and BrowseComp+ show that EvoRoute reduces execution cost and latency by over 70%.

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Chang Zong, Yuchen Yan, Weiming Lu, Jian Shao, Yongfeng Huang, Heng Chang, Yueting Zhuang

fastHan: A BERT-based Multi-Task Toolkit for Chinese NLP (2021.acl-demo)

Challenge:	Recent advances with LLMs have shown promising results across various tasks, but their use in answering questions from knowledge bases remains largely unexplored.
Approach:	They propose a framework that utilizes an LLM-based agent with multiple roles for KBQA tasks.
Outcome:	The proposed framework outperforms state-of-the-art systems on the LC-QuAD and YAGO-QA benchmarks yielding F1 scores of 11.8% and 20.7%, respectively.

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Zhichao Geng, Hang Yan, Xipeng Qiu, Xuanjing Huang

UTC-IE: A Unified Token-pair Classification Architecture for Information Extraction (2023.acl-long)

Challenge:	Recently, the need for Chinese natural language processing (NLP) has a dramatic increase for many downstream applications.
Approach:	They propose to use Chinese word segmentation (CWS), Part-of-Speech (POS) tagging, named entity recognition (NER), and dependency parsing to train a multi-task model based on a pruned BERT.
Outcome:	The proposed model performs better than popular segmentation tools on a non-training corpus.

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Hang Yan, Yu Sun, Xiaonan Li, Yunhua Zhou, Xuanjing Huang, Xipeng Qiu

Information Extraction & Text Mining

Challenge:	Information Extraction (IE) tasks have been solved with different models because of their output structures.
Approach:	They propose a Unified Token-pair Classification architecture for Information Extraction that introduces Plusformer on top of the token-pear feature matrix.
Outcome:	The proposed approach outperforms task-specific and unified models on all tasks in 10 datasets and achieves better results on 2 joint IE datasets.

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

Learning Representation Mapping for Relation Detection in Knowledge Base Question Answering (P19-1)

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 .

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Peng Wu, Shujian Huang, Rongxiang Weng, Zaixiang Zheng, Jianbing Zhang, Xiaohui Yan, Jiajun Chen

CC-Tuning: A Cross-Lingual Connection Mechanism for Improving Joint Multilingual Supervised Fine-Tuning (2025.acl-long)

Challenge:	Existing approaches to detect relation detection only get high accuracy for questions whose relations have been seen in training data.
Approach:	They propose a method to learn representation mapping for both seen and unseen relations based on previously learned relation embedding.
Outcome:	The proposed method improves the performance of unseen relations while keeping the performance comparable to the state-of-the-art.

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Yangfan Ye, Xiaocheng Feng, Zekun Yuan, Xiachong Feng, Libo Qin, Lei Huang, Weitao Ma, Yichong Huang, Zhirui Zhang, Yunfei Lu, Xiaohui Yan, Duyu Tang, Dandan Tu, Bing Qin

Hallucination Detection for Generative Large Language Models by Bayesian Sequential Estimation (2023.emnlp-main)

Challenge:	Existing fine-tuning approaches that focus on English-centric training corpora often introduce implicit cross-lingual alignment, overlooking the potential for more profound, latent-level cross-linguistic interactions.
Approach:	They propose a multilingual fine-tuning paradigm that explicitly establishes a cross-lingual connection mechanism at the latent level.
Outcome:	The proposed model outperforms vanilla SFT and offers a strong latent-level alternative to data-level augmentation methods.

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Xiaohua Wang, Yuliang Yan, Longtao Huang, Xiaoqing Zheng, Xuanjing Huang

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

Challenge:	Existing methods for detecting hallucinations require large numbers of observations to be retrieved, increasing response times.
Approach:	They propose a framework that leverages Bayesian sequential analysis to optimize the trade-off between costs and benefits during the hallucination detection process.
Outcome:	The proposed framework surpasses existing methods in efficiency and precision of hallucination detection.

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Chengyu Wang, Junbing Yan, Yuanhao Yue, Jun Huang

Learning to Extract Rational Evidence via Reinforcement Learning for Retrieval-Augmented Generation (2026.findings-acl)

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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Xinping Zhao, Shouzheng Huang, Yan Zhong, Xinshuo Hu, Meishan Zhang, Baotian Hu, Min Zhang

Dynamic and Multi-Channel Graph Convolutional Networks for Aspect-Based Sentiment Analysis (2021.findings-acl)

Challenge:	Retrieval-Augmented Generation (RAG) is effective in Large Language Models (LLMs). However, retrieval noises undermine the quality of LLMs’ generation, necessitating the development of denoising mechanisms.
Approach:	They propose a model which integrates reasoning and extracting into one unified trajectory, followed by knowledge token masking to avoid information leakage.
Outcome:	Extensive experiments on five benchmark datasets show the superiority of EviOmni, which provides compact and high-quality evidence, enhances the accuracy of downstream tasks, and supports both traditional and agentic RAG systems.

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Shiguan Pang, Yun Xue, Zehao Yan, Weihao Huang, Jinhui Feng

Sentiment Analysis

Challenge:	. - (EN)
Approach:	. - (EN)
Outcome:	. - (EN)

StyleDubber: Towards Multi-Scale Style Learning for Movie Dubbing (2024.findings-acl)

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Gaoxiang Cong, Yuankai Qi, Liang Li, Amin Beheshti, Zhedong Zhang, Anton Hengel, Ming-Hsuan Yang, Chenggang Yan, Qingming Huang

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

Challenge:	Existing methods for movie dubbing break phonemes in scripts, resulting in incomplete phoneme pronunciation and poor identity stability.
Approach:	They propose a method that switches dubbing learning from frame level to phoneme level . it uses a multimodal style adaptor to learn pronunciation style from audio .
Outcome:	The proposed method improves on two benchmarks, V2C and Grid, and is available on github.

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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.

TRIPS: Efficient Vision-and-Language Pre-training with Text-Relevant Image Patch Selection (2022.emnlp-main)

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Chaoya Jiang, Haiyang Xu, Chenliang Li, Ming Yan, Wei Ye, Shikun Zhang, Bin Bi, Songfang Huang

BLEURT Has Universal Translations: An Analysis of Automatic Metrics by Minimum Risk Training (2023.acl-long)

Challenge:	Existing vision-and-language pre-training models suffer from long visual sequences . experimental results show that TRIPS gains a speedup of 40% over previous similar VLP models .
Approach:	They propose an efficient vision-and-language pre-training model with text-relevant image patch selection, TRIPS, which reduces the visual sequence progressively with a text-guided patch-selection layer in the visual backbone for efficient training and inference.
Outcome:	The proposed model can speed up training and inference by 40% over previous models.

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Yiming Yan, Tao Wang, Chengqi Zhao, Shujian Huang, Jiajun Chen, Mingxuan Wang

Text Generation

Challenge:	Automated metrics are used for machine translation, but they are often considered to be black boxes with potential biases that are difficult to detect.
Approach:	They analyze automatic metrics from the perspective of their guidance for machine translation training.
Outcome:	The proposed measures improve the performance of machine translation models.

Training a Better Chinese Spelling Correction Model via Prior-knowledge Guided Teacher (2024.findings-acl)

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Chi Wei, Shaobin Huang, Rongsheng Li, Naiyu Yan, Rui Wang

Measure Children’s Mindreading Ability with Machine Reading (2023.findings-emnlp)

Challenge:	Chinese Spelling Correction models are prone to over-correct and poor generalization for error patterns outside the standard distribution.
Approach:	They propose a teacher network guided by prior knowledge for distillation learning of CSC models.
Outcome:	The proposed method significantly enhances the CSC model’s language modeling capabilities, crucial for minimizing over-correction.

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Yuliang Yan, Xiaohua Wang, Xiang Zhou, Xiaoqing Zheng, Xuanjing Huang

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

Challenge:	Existing scoring models do not take the features of the stories and video clips into account when scoring, which will reduce the accuracy of the models.
Approach:	They propose to leverage the features extracted from stories and videos related to the questions being asked during the children’s mindreading evaluation.
Outcome:	The proposed framework agrees well with human experts on scores produced by the 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

Plutus: Benchmarking Large Language Models in Low-Resource Greek Finance (2025.emnlp-main)

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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Xueqing Peng, Triantafillos Papadopoulos, Efstathia Soufleri, Polydoros Giannouris, Ruoyu Xiang, Yan Wang, Lingfei Qian, Jimin Huang, Qianqian Xie, Sophia Ananiadou

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

Challenge:	Greek is the dominant language of the world's merchant navy and is a key language for international trade.
Approach:	They propose to develop a Greek financial evaluation benchmark and a financial LLM fine-tuned on Greek-specific financial data to bridge this gap.
Outcome:	The proposed benchmarks surpass GPT-4 by 8.33%, GPT- 4o by 26.83%, and Deepseek-V3 by 67.74%.

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

Relation Extraction with Temporal Reasoning Based on Memory Augmented Distant Supervision (N19-1)

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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Jianhao Yan, Lin He, Ruqin Huang, Jian Li, Ying Liu

Information Extraction & Text Mining

Challenge:	Distant supervision is an important paradigm for automatically extracting relations . but the examples collected can be noisy and pose significant challenge for labeling .
Approach:	They propose a method to predict whether two entities participate in a relation at a given time spot.
Outcome:	The proposed model performs better in WIKI-TIME and NYT-10 datasets compared with the best existing models . the proposed model is based on a dataset with a valid period of a certain relation of two entities in the knowledge base .

PANDA: Preference Adaptation for Enhancing Domain-Specific Abilities of LLMs (2024.findings-acl)

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An Liu, Zonghan Yang, Zhenhe Zhang, Qingyuan Hu, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Yang Liu

Challenge:	Large language models have demonstrated considerable capabilities across various tasks . however, they often fall short of the performance achieved by domain-specific state-of-the-art models .
Approach:	They propose a tuning-free method to augment domain-specific abilities of Large language models . they leverage insights from the response preference of expert models to augment LLMs .
Outcome:	The proposed method outperforms the expert model on 4 ScienceWorld tasks.

Case2Code: Scalable Synthetic Data for Code Generation (2025.coling-main)

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Yunfan Shao, Linyang Li, Yichuan Ma, Peiji Li, Demin Song, Qinyuan Cheng, Shimin Li, Xiaonan Li, Pengyu Wang, Qipeng Guo, Hang Yan, Xipeng Qiu, Xuanjing Huang, Dahua Lin

CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors (2023.acl-long)

Challenge:	Large Language Models (LLMs) have shown outstanding breakthroughs in code generation.
Approach:	They propose a case-to-code induction task that exploits the expressiveness and correctness of programs by incorporating LLMs into their training.
Outcome:	The proposed task improves distribution case-to-code induction and various coding generation tasks.

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Peng Li, Tianxiang Sun, Qiong Tang, Hang Yan, Yuanbin Wu, Xuanjing Huang, Xipeng Qiu

FLAT: Chinese NER Using Flat-Lattice Transformer (2020.acl-main)

Challenge:	Large language models pre-trained on massive corpora have shown impressive few-shot learning ability on many NLP tasks.
Approach:	They propose to recast structured output in the form of code instead of natural language and use generative LLMs of code to perform IE tasks.
Outcome:	The proposed method outperforms fine-tuning moderate-size pre-trained models and prompting NL-LLMs under few-shot settings.

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Xiaonan Li, Hang Yan, Xipeng Qiu, Xuanjing Huang

Discriminating between Similar Languages on Imbalanced Conversational Texts (L18-1)

Challenge:	Named entity recognition (NER) models are difficult to use because of the complex nature of the lattice structure and the low inference speed.
Approach:	They propose a character-word lattice structure that converts lattics into flat structures consisting of spans.
Outcome:	The proposed model outperforms other lexicon-based models on four datasets and is highly parallel.

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Junqing He, Xian Huang, Xuemin Zhao, Yan Zhang, Yonghong Yan