Papers by Fei Yang

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

Challenge:	Existing methods focus on replicating dialogues in textual form, neglecting the role’s voice traits as a crucial effect in interaction, which tends to be more immersive experiences in realistic scenarios.
Approach:	They propose a first seamless speech-language personality interaction model to achieve immersive RPAs with low latency.
Outcome:	The proposed model exhibits role-specific personality traits and vocal traits throughout the interaction, enabling a mixture of speech and language responses.

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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

Mixture-of-Supernets: Improving Weight-Sharing Supernet Training with Architecture-Routed Mixture-of-Experts (2024.findings-acl)

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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Ganesh Jawahar, Haichuan Yang, Yunyang Xiong, Zechun Liu, Dilin Wang, Fei Sun, Meng Li, Aasish Pappu, Barlas Oguz, Muhammad Abdul-Mageed, Laks Lakshmanan, Raghuraman Krishnamoorthi, Vikas Chandra

Enhancing Grammatical Error Correction Systems with Explanations (2023.acl-long)

Challenge:	Neural architecture search (NAS) uses weight-sharing supernets to generate diverse subnetworks without retraining.
Approach:	They propose a weight-sharing supernet that leverages mixture-of-experts to enhance supernet model expressiveness with minimal training overhead.
Outcome:	The proposed method achieves state-of-the-art (SoTA) performance in NAS for fast machine translation models, surpassing NAS-BERT and AutoDistil across various model sizes.

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Yuejiao Fei, Leyang Cui, Sen Yang, Wai Lam, Zhenzhong Lan, Shuming Shi

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs (2025.findings-emnlp)

Challenge:	To help language learners better understand why the GEC system makes a correction, the causes of errors and the corresponding error types are two key factors.
Approach:	They propose to annotate large dataset with evidence words and grammatical error types to help language learners better understand corrections.
Outcome:	The proposed model can be validated by human evaluation and can be used to help second-language learners decide whether to accept a correction suggestion and understand the associated grammar rule.

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Yixiao Zhou, Ziyu Zhao, Dongzhou Cheng, Zhiliang Wu, Jie Gui, Yi Yang, Fei Wu, Yu Cheng, Hehe Fan

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

Challenge:	Sparse Mixture-of-Experts (SMoE) architectures require loading all expert parameters . previous work focused on expert pruning and merging but focused on neuron-level structure .
Approach:	They propose a task-agnostic framework for expert pruning and reconstruction . it prunes redundant experts using router statistics, then decomposes them into neuron-level expert segments .
Outcome:	The proposed framework reduces the number of experts and memory usage, making it easier to deploy.

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

Are Emotion and Rhetoric Neurons in LLM? Neuron Recognition and Adaptive Masking for Emotion-Rhetoric Prediction Steering (2026.acl-long)

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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Li Zheng, Xin Zhang, Shuyi He, Fei Li, Chong Teng, Jiang-Ming Yang, Donghong Ji, Zhuang Li

OptiCo: Adaptive Distributed Training Optimization via Collaborative Agent Reasoning (2026.acl-long)

Challenge:	Existing studies on neurons focus on emotion and rhetoric, neglecting their intrinsic connections.
Approach:	They propose a framework for fine-grained steering of emotion and rhetoric in large language models . they propose 'neuro-based' masking method that integrates multi-dimensional screening .
Outcome:	The proposed method achieves directed induction of non-target sentences and enhancement of emotion tasks via rhetoric neurons.

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Sheng Chen, Tang Zhe, Weixing Zhang, Fei Yang, Yuanyuan. Wang, Tianlin Li, Yang Liu

DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories (2024.findings-acl)

Challenge:	Existing distributed training frameworks are plagued by over-reliance on prior profiling and poor generalization across models/hardware.
Approach:	They propose a model-driven multi-agent framework that leverages Large Language Models to enable automatic and explainable distributed training strategy configuration.
Outcome:	The proposed framework outperforms expert-designed training strategies within 20 iterations.

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Jia Li, Ge Li, Yunfei Zhao, Yongmin Li, Huanyu Liu, Hao Zhu, Lecheng Wang, Kaibo Liu, Zheng Fang, Lanshen Wang, Jiazheng Ding, Xuanming Zhang, Yuqi Zhu, Yihong Dong, Zhi Jin, Binhua Li, Fei Huang, Yongbin Li, Bin Gu, Mengfei Yang

mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval (2024.emnlp-industry)

Challenge:	Existing benchmarks are poorly aligned with real-world code repositories and are insufficient to evaluate the coding abilities of Large Language Models (LLMs).
Approach:	They propose a repository-level benchmark named DevEval to evaluate LLMs' coding abilities in real-world code repositories.
Outcome:	The proposed benchmarks show that the LLMs perform better in real-world code repositories than existing benchmarks.

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Xin Zhang, Yanzhao Zhang, Dingkun Long, Wen Xie, Ziqi Dai, Jialong Tang, Huan Lin, Baosong Yang, Pengjun Xie, Fei Huang, Meishan Zhang, Wenjie Li, Min Zhang

A Two-Agent Game for Zero-shot Relation Triplet Extraction (2024.findings-acl)

Challenge:	Existing models for text retrieval are based on a multi-stage process that involves retrieving documents from a large corpus.
Approach:	They propose to build a multilingual text representation model and a cross-encoder reranker from scratch for text retrieval.
Outcome:	The proposed models outperform the state-of-the-art models on long-context retrieval benchmarks.

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Ting Xu, Haiqin Yang, Fei Zhao, Zhen Wu, Xinyu Dai

Low-Resource NLP

Challenge:	Existing methods for relation triplet extraction rely on labeled data and are limited in their applicability.
Approach:	They propose a two-agent game approach to deliberate and debate unseen relations by two agents, a generator and an extractor.
Outcome:	The proposed method outperforms baseline methods by 6%-16% in F1 scores.

CMR Scaling Law: Predicting Critical Mixture Ratios for Continual Pre-training of Language Models (2024.emnlp-main)

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Jiawei Gu, Zacc Yang, Chuanghao Ding, Rui Zhao, Fei Tan

ToolRM: Towards Agentic Tool-Use Reward Modeling (2026.findings-acl)

Challenge:	Large Language Models (LLMs) excel in diverse tasks but often underperform in specialized fields due to limited domain-specific or proprietary corpus.
Approach:	They propose a power-law relationship between loss, mixture ratio, and training tokens scale and formalize the trade-off between general and domain-specific capabilities.
Outcome:	The proposed model achieves the desired domain transfer while maintaining general ability and highest utilization of available resources.

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Renhao Li, Jianhong Tu, Yang Su, Yantao Liu, Fei Huang, Hamid Alinejad-Rokny, Derek F. Wong, Junyang Lin, Min Yang

Challenge:	lack of reliable reward models for tool-use tasks has limited progress toward agentic AI . recent advances in agentic artificial intelligence are driven by tool-using capabilities of large language models.
Approach:	They propose a pipeline that constructs pairwise preference data using rule-based scoring and multidimensional sampling to build lightweight reward models.
Outcome:	The proposed model outperforms existing models on tool calling tasks with higher accuracy.

Data Interpreter: An LLM Agent for Data Science (2025.findings-acl)

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Sirui Hong, Yizhang Lin, Bang Liu, Bangbang Liu, Binhao Wu, Ceyao Zhang, Danyang Li, Jiaqi Chen, Jiayi Zhang, Jinlin Wang, Li Zhang, Lingyao Zhang, Min Yang, Mingchen Zhuge, Taicheng Guo, Tuo Zhou, Wei Tao, Robert Tang, Xiangtao Lu, Xiawu Zheng, Xinbing Liang, Yaying Fei, Yuheng Cheng, Yongxin Ni, Zhibin Gou, Zongze Xu, Yuyu Luo, Chenglin Wu

Towards Unified Prompt Tuning for Few-shot Text Classification (2022.findings-emnlp)

Challenge:	Large Language Models (LLMs) excel in various domains but face challenges when applied to data science workflows due to their complex, multi-stage nature.
Approach:	They propose a hierarchical graph-based agent that represents complexity and a progressive strategy for step-by-step verification, refinement, and consistent context management.
Outcome:	The proposed agent surpasses state-of-the-art baselines on the MATH dataset and performs better on InfiAgent-DABench.

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Jianing Wang, Chengyu Wang, Fuli Luo, Chuanqi Tan, Minghui Qiu, Fei Yang, Qiuhui Shi, Songfang Huang, Ming Gao

NOVA-63: Native Omni-lingual Versatile Assessments of 63 Disciplines (2025.emnlp-main)

Challenge:	Prompt-based fine-tuning has boosted performance of Pre-trained Language Models (PLMs) on few-shot text classification, but PLMs are unfamiliar with prompt-style expressions during pre-training, which limits the few- shot learning performance on downstream tasks.
Approach:	They propose a framework for prompt-based fine-tuning that captures prompting semantics from non-target NLP datasets and propose 'Prompt-Options-Verbalizer' for joint prompt learning across different NLP tasks.
Outcome:	Experiments show that the proposed framework outperforms state-of-the-art prompt-based fine-tuning frameworks on few-shot text classification tasks.

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Jinyang Zhang, Kexin Yang, Yu Wan, Muyang Ye, Baosong Yang, Fei Huang, Junyang Lin, Dayiheng Liu

Maximizing Local Entropy Where It Matters: Prefix-Aware Localized LLM Unlearning (2026.acl-long)

Challenge:	Existing multilingual benchmarks show severe drawbacks, such as overly translated content, the absence of difficulty control, and disciplinary imbalance, making the benchmarking process unreliable and showing low convincingness.
Approach:	They propose a multilingual benchmark that integrates LLM-assisted formatting, expert quality verification, and multi-level difficulty screening to provide a comprehensive, difficult multilingual assessment.
Outcome:	The proposed benchmark features 93,536 questions sourced from native speakers across 14 languages and 63 academic disciplines.

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Naixin Zhai, Pengyang Shao, Binbin Zheng, Yonghui Yang, Fei Shen, Long Bai, Xun Yang

Challenge:	Existing approaches to machine unlearning treat all tokens indiscriminately and enforce uncertainty over the entire vocabulary.
Approach:	They propose a framework that targets the prefix in a response and minimizes uncertainty in the critical subspace.
Outcome:	The proposed framework achieves superior forgetting efficacy and utility preservation compared to baselines.

HELENE: Hessian Layer-wise Clipping and Gradient Annealing for Accelerating Fine-tuning LLM with Zeroth-order Optimization (2025.emnlp-main)

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Huaqin Zhao, Jiaxi Li, Yi Pan, Shizhe Liang, Xiaofeng Yang, Fei Dou, Tianming Liu, Jin Lu

The Mirage of Model Editing: Revisiting Evaluation in the Wild (2025.acl-long)

Challenge:	Large language models (LLMs) face memory challenges due to the high cost of backpropagation.
Approach:	They propose a zeroth-order (ZO) optimization that matches memory usage to inference . they propose scalable and memory-efficient zeroth order (ZE) optimizer that integrates annealed A-GNB gradients with diagonal Hessian estimation and layer-wise clipping as a second-order pre-conditioner.
Outcome:	The proposed algorithm outperforms state-of-the-art methods with an average speedup of 20 over MeZO on RoBERTa-large and OPT-1.3B.

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Wanli Yang, Fei Sun, Jiajun Tan, Xinyu Ma, Qi Cao, Dawei Yin, Huawei Shen, Xueqi Cheng

Question Answering

Challenge:	despite near-perfect results, effectiveness of model editing in real-world applications remains unclear.
Approach:	They propose QAEdit and WILD to better reflect real-world use of model editing . they propose a benchmark aligned with widely used question answering datasets and a task-agnostic evaluation framework .
Outcome:	The proposed QAEdit benchmark and WILD evaluation framework show that current models perform worse than previously reported.

Knowledge Stimulated Contrastive Prompting for Low-Resource Stance Detection (2022.findings-emnlp)

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Kai Zheng, Qingfeng Sun, Yaming Yang, Fei Xu

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

Challenge:	Stance Detection Tasks require background knowledge especially when there is no explicit target mentioned in text.
Approach:	They propose a masked language prompt joint contrastive learning approach to stimulate the knowledge inherit from pre-trained models.
Outcome:	The proposed model is effective in stance detection on three benchmarks.

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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.

MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct (2025.findings-acl)

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Run Luo, Haonan Zhang, Longze Chen, Ting-En Lin, Xiong Liu, Yuchuan Wu, Min Yang, Yongbin Li, Minzheng Wang, Pengpeng Zeng, Lianli Gao, Heng Tao Shen, Yunshui Li, Hamid Alinejad-Rokny, Xiaobo Xia, Jingkuan Song, Fei Huang

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

Challenge:	a new framework for image-text instruction data evolution improves MLLM performance . lack of high-quality instruction data remains a major bottleneck in ML modeling .
Approach:	They propose a multimodal instruction data evolution framework that iteratively enhances data quality through fine-grained perception, cognitive reasoning, and interaction evolution.
Outcome:	The proposed approach improves MLLM performance in nine vision-language tasks while using significantly less data.

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

Rethinking Depression Prediction from a Fine-Grained Subscore Modeling Perspective via Multi-Task Learning (2026.acl-long)

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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Zhenguang Wang, Bo Li, Wenhui Tan, Peng Cao, Yang Wang, Jia Duan, Fei Wang, Osmar Zaiane

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

Challenge:	Existing methods for depression assessment rely on standardized ratings, but they are time-consuming and subject to inter-rater variability.
Approach:	They propose a fine-grained model for subscore prediction via multi-task learning that can be used to predict depression severity using multiple tasks.
Outcome:	The proposed model outperforms baselines and Qwen3-14B direct scoring on the public E-DAIC dataset and to a large-scale private clinical dataset.

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

CBT-Bench: Evaluating Large Language Models on Assisting Cognitive Behavior Therapy (2025.naacl-long)

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

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Mian Zhang, Xianjun Yang, Xinlu Zhang, Travis Labrum, Jamie C. Chiu, Shaun M. Eack, Fei Fang, William Yang Wang, Zhiyu Chen

Factual Consistency Evaluation for Text Summarization via Counterfactual Estimation (2021.findings-emnlp)

Challenge:	Existing research has explored mental health condition classifications, empathetic conversations, and chatbots designed for simple discourse structures.
Approach:	They propose a benchmark for systematic evaluation of cognitive behavioral therapy assistance using Large Language Models (LLMs).
Outcome:	The proposed benchmark includes three levels of tasks covering key aspects of cognitive behavioral therapy that could be enhanced through AI assistance.

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Yuexiang Xie, Fei Sun, Yang Deng, Yaliang Li, Bolin Ding

Text Generation

Challenge:	Existing methods to evaluate factual consistency in text summarization neglect the intrinsic cause of factual inconsistency or rely on auxiliary tasks.
Approach:	They propose a method to evaluate the factual consistency in text summarization via counterfactual estimation, which formulates the causal relationship between source document, generated summary, and the language prior.
Outcome:	The proposed metric improves correlation with human judgments and convenience of usage on three public abstractive text summarization datasets.

WESR: A Benchmark and Strong Baseline for Word-level Event-Speech Recognition (2026.findings-acl)

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Chenchen Yang, Kexin Huang, Liwei Fan, Qian Tu, Botian Jiang, Dong Zhang, Linqi Yin, Shimin Li, Zhaoye Fei, Qinyuan Cheng, Xipeng Qiu

STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing (2022.findings-emnlp)

Challenge:	aaron carroll: the precise localization of non-verbal vocal events remains a critical yet under-explored challenge. carroll says current methods suffer from insufficient task definitions with limited category coverage. carrol: knowing exactly where an event occurred is not enough; knowing exactly what it happened is.
Approach:	They propose a taxonomy of 21 vocal events with a new categorization into discrete versus continuous types.
Outcome:	The proposed model disentangles ASR errors from event detection while maintaining ASR quality.

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Zefeng Cai, Xiangyu Li, Binyuan Hui, Min Yang, Bowen Li, Binhua Li, Zheng Cao, Weijie Li, Fei Huang, Luo Si, Yongbin Li

Programming Languages in NLP

Challenge:	Extensive experiments show that STAR outperforms previous pre-training methods and ranks first on the leaderboard . text-to-SQL parsing aims to translate natural language (NL) questions into executable SQL queries .
Approach:	They propose a SQL guided pre-training framework STAR for context-dependent text-to-SQL parsing . they propose two objectives that explore context-dependence of NL utterances and SQL queries .
Outcome:	The proposed framework outperforms existing methods on two downstream benchmarks and ranks first on the leaderboard.

GSID: Generative Semantic Indexing for E-Commerce Product Understanding (2025.emnlp-industry)

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Haiyang Yang, Qinye Xie, Qingheng Zhang, Chen Li Yu, Huike Zou, Chengbao Lian, Shuguang Han, Fei Huang, Jufeng Chen, Bo Zheng

InfiGUIAgent: A Multimodal Generalist GUI Agent with Native Reasoning and Reflection (2026.eacl-long)

Challenge:	Structured product information is a major bottleneck for the efficiency of e-commerce platforms.
Approach:	They propose a data-driven approach to generate product structured representations using product metadata.
Outcome:	Extensive experiments show that GSID can generate better product representations on real-world e-commerce platforms.

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Yuhang Liu, Pengxiang Li, Zishu Wei, Congkai Xie, Xueyu Hu, Xinchen Xu, Shengyu Zhang, Xiaotian Han, Hongxia Yang, Fei Wu

How Should We Enhance the Safety of Large Reasoning Models: An Empirical Study (2026.acl-long)

Challenge:	Existing GUI Agents face challenges in multi-step reasoning and reliance on textual annotations, limiting their effectiveness.
Approach:	They propose an MLLM-based GUI Agent with a two-stage supervised fine-tuning pipeline that enhances GUI understanding and grounding.
Outcome:	InfiGUIAgent achieves competitive performance on several GUI benchmarks, highlighting the impact of native reasoning skills in enhancing GUI interaction for automation tasks.

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Zhexin Zhang, Xian Qi Loye, Victor Shea-Jay Huang, Junxiao Yang, Qi Zhu, Shiyao Cui, Fei Mi, Lifeng Shang, Yingkang Wang, Hongning Wang, Minlie Huang

RMTBench: Benchmarking LLMs Through Multi-Turn User-Centric Role-Playing (2025.findings-emnlp)

Challenge:	Large Reasoning Models have achieved remarkable success on reasoning-intensive tasks, but their enhanced reasoning capabilities do not translate to improved safety performance.
Approach:	They propose to use supervised fine tuning to enhance the safety of Large Reasoning Models.
Outcome:	The proposed method improves the safety of large reasoning models on reasoning-intensive tasks.

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Hao Xiang, Tianyi Tang, Yang Su, Bowen Yu, An Yang, Fei Huang, Yichang Zhang, Yaojie Lu, Hongyu Lin, Xianpei Han, Jingren Zhou, Junyang Lin, Le Sun

Large Language Models as Source Planner for Personalized Knowledge-grounded Dialogues (2023.findings-emnlp)

Challenge:	Existing benchmarks focus on character-centric approach and fail to reflect real-world applications.
Approach:	RMTBench is a user-centric bilingual role-playing benchmark featuring 80 diverse characters and over 8,000 dialogue rounds.
Outcome:	RMTBench features 80 diverse characters and over 8,000 dialogue rounds.

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Hongru Wang, Minda Hu, Yang Deng, Rui Wang, Fei Mi, Weichao Wang, Yasheng Wang, Wai-Chung Kwan, Irwin King, Kam-Fai Wong

LoraRetriever: Input-Aware LoRA Retrieval and Composition for Mixed Tasks in the Wild (2024.findings-acl)

Challenge:	Existing knowledge-grounded dialogue systems focus on a single knowledge source or ignore the dependency between multiple knowledge sources.
Approach:	They propose a framework that integrates multiple knowledge sources and dependencies between them.
Outcome:	The proposed framework can produce persona-consistent and knowledge-enhanced responses on a knowledge-grounded dialogue dataset.

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Ziyu Zhao, Leilei Gan, Guoyin Wang, Wangchunshu Zhou, Hongxia Yang, Kun Kuang, Fei Wu

Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs (2023.findings-emnlp)

Challenge:	Low-Rank Adaptation (LoRA) is an effective yet efficient solution for fine-tuning large language models.
Approach:	They propose a low-rank Adaptation framework that retrieves and composes multiple LoRAs according to input prompts.
Outcome:	Experimental results show that LoraRetriever outperforms baselines in terms of performance and versatility.

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Hongru Wang, Rui Wang, Fei Mi, Yang Deng, Zezhong Wang, Bin Liang, Ruifeng Xu, Kam-Fai Wong

Natural Language Interfaces

Challenge:	Existing LLMs generate responses based on the dialogue context, overlooking the underlying linguistic cues about the user status exhibited in the context.
Approach:	They propose a linguistic cue-based chain-of-thoughts method which enhances the LLMs inference with an intermediate reasoning step to find cues exhibited in the dialogue.
Outcome:	The proposed method outperforms standard prompting methods on in-depth dialogue questions and linguistic cues exhibited in the context.

ClusterUCB: Efficient Gradient-Based Data Selection for Targeted Fine-Tuning of LLMs (2025.findings-emnlp)

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Zige Wang, Qi Zhu, Fei Mi, Minghui Xu, Ruochun Jin, Wenjing Yang

CultureSynth: A Hierarchical Taxonomy-Guided and Retrieval-Augmented Framework for Cultural Question-Answer Synthesis (2025.findings-emnlp)

Challenge:	Gradient-based data influence approximation is not feasible in practice.
Approach:	They propose a gradient-based data selection framework with clustering and a modified Upper Confidence Bound algorithm to solve this problem.
Outcome:	The proposed framework can achieve comparable results to the original gradient-based data selection methods while reducing computational consumption.

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Xinyu Zhang, Pei Zhang, Shuang Luo, Jialong Tang, Yu Wan, Baosong Yang, Fei Huang

XY-Tokenizer: Mitigating the Semantic-Acoustic Conflict in Low-Bitrate Speech Codecs (2026.acl-long)

Challenge:	Cultural competence is defined as the ability to understand and adapt to multicultural contexts.
Approach:	They propose a framework that uses a hierarchical multilingual taxonomy and a Retrieval-Augmented Generation to synthesize culturally relevant question-answer pairs.
Outcome:	The proposed framework contains a hierarchical multilingual taxonomy covering 12 primary and 130 secondary topics and a Retrieval-Augmented Generation (RAG)-based methodology leveraging factual knowledge to synthesize culturally relevant question-answer pairs.

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Yitian Gong, Luozhijie Jin, Kuangwei Chen, Dong Zhang, Ruifan Deng, Xiaogui Yang, Xin Zhang, Zhaoye Fei, Qinyuan Cheng, Shimin Li, Xipeng Qiu

Visual Prompt Tuning for Few-Shot Text Classification (2022.coling-1)

Challenge:	Existing speech codecs struggle to balance these objectives at low bitrates . XY-Tokenizer achieves stronger semantic alignment than representative semantic-distillation codec .
Approach:	They propose a low-bitrate speech codec that aligns discrete speech representations with text while preserving fine-grained acoustic details for reconstruction.
Outcome:	The proposed codec outperforms existing low-bitrate speech codecs in speech understanding and generation tasks.

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Jingyuan Wen, Yutian Luo, Nanyi Fei, Guoxing Yang, Zhiwu Lu, Hao Jiang, Jie Jiang, Zhao Cao

Adversarial Knowledge Stimulated Contrastive Prompting for Few-shot Language Learners (2023.findings-acl)

Challenge:	Existing work on pretraining models for text classification uses image encoders instead of visual prompts.
Approach:	They propose a method to deploy large-scale pre-trained models in the prompt-tuning paradigm in few-shot learning.
Outcome:	The proposed method outperforms the most recent prompt-tuning methods on five public text classification datasets.

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Kai Zheng, Qingfeng Sun, Yaming Yang, Tengchao Lv, Yeyong Pi, Changlin Zhao, Fei Xu, Qi Zhang

Speech-Text Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment (2023.acl-long)

Challenge:	Prompt-based fine-tuning has boosted performance of Pre-trained language models on few-shot Natural Language Understanding (NLU) tasks by employing task-specific prompts.
Approach:	They propose a Cloze-driven prompt framework for prompt tuning that implicitly stimulates knowledge from pre-trained language models.
Outcome:	The proposed framework outperforms state-of-the-art for prompt-based fine-tuning on few-shot NLU tasks.

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Tianshu Yu, Haoyu Gao, Ting-En Lin, Min Yang, Yuchuan Wu, Wentao Ma, Chao Wang, Fei Huang, Yongbin Li

Consultant Decoding: Yet Another Synergistic Mechanism (2025.findings-acl)

Challenge:	Existing speech-text pre-training methods are limited to one or two specific tasks, despite their success in speech-language processing tasks.
Approach:	They propose a temporal position prediction task to capture the speech-text alignment . they use a textual dialog pre-training task to generalize a response selection task .
Outcome:	The proposed model is superior in learning speech-text alignment and multi-turn dialog context.

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Chuanghao Ding, Jiaping Wang, Ziqing Yang, Xiaoliang Wang, Dahua Lin, Nguyen Cam-Tu, Fei Tan

XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser (2025.coling-main)

Challenge:	Large language models (LLMs) have attracted widespread attention and adoption across diverse domains due to their exceptional performance and robust generalization abilities.
Approach:	They propose a synergetic mechanism for Consultant Decoding (CD) that achieves a 2.5-fold increase in inference speed compared to the target model while maintaining comparable generation quality.
Outcome:	The proposed mechanism achieves 2.5-fold increase in inference speed while maintaining comparable generation quality (100% of the target model’s performance).

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Xianfu Cheng, Hang Zhang, Jian Yang, Xiang Li, Weixiao Zhou, Fei Liu, Kui Wu, Xiangyuan Guan, Tao Sun, Xianjie Wu, Tongliang Li, Zhoujun Li

Bloom-Eval: A Hierarchical Evaluation Benchmark for Automatic Survey Generation Based on Bloom’s Taxonomy (2026.acl-long)

Challenge:	Document AI parsing semi-structured image form is a key information extraction task.
Approach:	They propose a multimodal and multilingual semi-structured FORM PARSER which integrates SER and relation extraction into a unified framework.
Outcome:	The proposed framework achieves up to 1.79% improvement on RE tasks in multilingual and zero-shot settings.

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Fei Zhang, Zhe Zhao, HaiBin Wen, Tianshuo Wei, Zaixi Zhang, Chao Yang, Ye Wei

Text Generation

Challenge:	Existing evaluation methods suffer from cognitive dimensional simplification and methodological unreliability due to the ”LLM-as-a-Judge” approach.
Approach:	They propose a six-tiered benchmark that evaluates ASG systems by prioritizing deterministic algorithms and introducing a GRADE approach for abstract abilities.
Outcome:	The proposed method provides the ASG field with a systematic, reproducible, and theoretically grounded benchmark to guide future research.

SpeechIQ: Speech-Agentic Intelligence Quotient Across Cognitive Levels in Voice Understanding by Large Language Models (2025.acl-long)

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Zhen Wan, Chao-Han Huck Yang, Yahan Yu, Jinchuan Tian, Sheng Li, Ke Hu, Zhehuai Chen, Shinji Watanabe, Fei Cheng, Chenhui Chu, Sadao Kurohashi

SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers (2022.coling-1)

Challenge:	SIQ quantifies voice understanding abilities and provides unified comparisons between cascaded methods and end-to-end models.
Approach:	They propose a human cognition-inspired evaluation pipeline for voice understanding large language models (LLM_Voice) that quantifies voice understanding abilities and provides unified comparisons between cascaded methods and end-to-end models.
Outcome:	The proposed framework quantifies voice understanding abilities and provides unified comparisons between cascaded methods and end-to-end models, identifies annotation errors in existing benchmarks, and detects hallucinations in LLM_Voice.

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Bowen Qin, Lihan Wang, Binyuan Hui, Bowen Li, Xiangpeng Wei, Binhua Li, Fei Huang, Luo Si, Min Yang, Yongbin Li

Programming Languages in NLP

Challenge:	Existing methods that learn from multiple semantically-equivalent questions are limited to one-to-one mapping .
Approach:	They propose a constraint to explore the underlying complementary semantic information among multiple semantically-equivalent questions and learn robust feature representations with reduced spurious associations.
Outcome:	The proposed method outperforms strong competitors and achieves state-of-the-art results on five benchmark datasets.

PaCE: Unified Multi-modal Dialogue Pre-training with Progressive and Compositional Experts (2023.acl-long)

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Yunshui Li, Binyuan Hui, ZhiChao Yin, Min Yang, Fei Huang, Yongbin Li

Multimodal Dialogue Response Generation (2022.acl-long)

Challenge:	Existing research on multi-modal dialogue pre-training is limited due to limited availability of multi-dimensional data . a recent emergence of chatGPT 1 has increased confidence in the potential for this goal .
Approach:	They propose a framework for multi-modal dialogue pre-training that integrates experts to accommodate multi-faceted tasks.
Outcome:	The proposed framework achieves state-of-the-art on eight multi-modal dialog benchmarks.

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Qingfeng Sun, Yujing Wang, Can Xu, Kai Zheng, Yaming Yang, Huang Hu, Fei Xu, Jessica Zhang, Xiubo Geng, Daxin Jiang

CodexGraph: Bridging Large Language Models and Code Repositories via Code Graph Databases (2025.naacl-long)

Challenge:	Existing studies focus on multimodal dialogue models but neglect generation methods.
Approach:	They propose a multimodal dialogue response generation task which requires multimodal dialogs containing both texts and images which are difficult to obtain.
Outcome:	Experiments show that the proposed model can generate informative text and high-resolution image responses.

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Xiangyan Liu, Bo Lan, Zhiyuan Hu, Yang Liu, Zhicheng Zhang, Fei Wang, Michael Qizhe Shieh, Wenmeng Zhou

P-MMEval: A Parallel Multilingual Multitask Benchmark for Consistent Evaluation of LLMs (2025.emnlp-main)

Challenge:	Large Language Models excel in stand-alone code tasks but struggle with handling entire code repositories.
Approach:	They propose a system that integrates LLM agents with graph database interfaces extracted from code repositories.
Outcome:	The proposed system integrates LLM agents with graph database interfaces extracted from code repositories.

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Yidan Zhang, Yu Wan, Boyi Deng, Baosong Yang, Hao-Ran Wei, Fei Huang, Bowen Yu, Dayiheng Liu, Junyang Lin, Fei Huang, Jingren Zhou

AutoEvolve: Automatically Evolving Queries for Applicable and Scalable Retrieval-Augmented Generation Benchmarking (2025.findings-emnlp)

Challenge:	Recent advances in large language models showcase varied multilingual capabilities across tasks . previous assessments focused on fundamental natural language processing (NLP) or isolated capability-specific tasks.
Approach:	They propose a multilingual multitask benchmark to assess multilingual capabilities . they use a large-scale benchmark covering fundamental and capability-specialized datasets .
Outcome:	The proposed benchmark compares models and tasks across languages and tasks and examines knowledge transfer from English to other languages.

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Ding-Chu Zhang, Xiaowen Zhang, Yue Fei, Renjun Hu, Xiao-Wen Yang, Zhi Zhou, Baixuan Li, Yu-Feng Li, Xing Shi, Wei Lin

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

Challenge:	Existing automated generation methods exhibit Weak Applicability and Weak Scalability . existing methods are limited by their reliance on metadata from specific corpora .
Approach:	They propose an approach to generate scalable RAG benchmarks using corpus-agnostic methods . they propose a difficulty-guided metric that directs query evolution process .
Outcome:	The proposed approach evolves queries significantly more challenging than existing methods . it is able to dynamically increase difficulty, limiting scalability of the query .

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

Balancing Speciality and Versatility: a Coarse to Fine Framework for Supervised Fine-tuning Large Language Model (2024.findings-acl)

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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Hengyuan Zhang, Yanru Wu, Dawei Li, Sak Yang, Rui Zhao, Yong Jiang, Fei Tan

Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances (2021.acl-long)

Challenge:	Aligned Large Language Models exhibit remarkable versatility, capable of handling diverse real-world tasks.
Approach:	They propose a coarse to fine framework to fine-tune aligned Large Language Models to achieve a balance between speciality and versatility.
Outcome:	The proposed framework outperforms baseline methods across diverse tasks and model scales.

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Zekang Li, Jinchao Zhang, Zhengcong Fei, Yang Feng, Jie Zhou

One-Shot Learning as Instruction Data Prospector for Large Language Models (2024.acl-long)

Challenge:	Recent intelligent open-domain chatbots have made substantial progress thanks to the rapid development of large-scale pre-training approaches.
Approach:	They propose a dynamic flow mechanism to model the context flow and a model to capture the information dynamics across dialogue utterances.
Outcome:	The proposed model outperforms the DialoGPT on the dialogue generation task.

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Yunshui Li, Binyuan Hui, Xiaobo Xia, Jiaxi Yang, Min Yang, Lei Zhang, Shuzheng Si, Ling-Hao Chen, Junhao Liu, Tongliang Liu, Fei Huang, Yongbin Li

Measuring Your ASTE Models in The Wild: A Diversified Multi-domain Dataset For Aspect Sentiment Triplet Extraction (2023.findings-acl)

Challenge:	Contemporary practices in instruction tuning often hinge on enlarging data scaling without a clear strategy for ensuring data quality.
Approach:	They propose a method that leverages one-shot learning to discern and select high-quality instruction data from extensive datasets.
Outcome:	Nuggets outperforms existing methods on MT-Bench and Alpaca-Eval benchmarks.

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Ting Xu, Huiyun Yang, Zhen Wu, Jiaze Chen, Fei Zhao, Xinyu Dai

Sentiment Analysis

Challenge:	Existing ASTE datasets are limited in their ability to represent real-world scenarios, hindering progress in this area.
Approach:	They propose a new ASTE dataset that is manually annotated to better fit real-world scenarios by providing more diverse and realistic reviews.
Outcome:	The proposed dataset is manually annotated to better fit real-world scenarios.

Addressing Inquiries about History: An Efficient and Practical Framework for Evaluating Open-domain Chatbot Consistency (2021.findings-acl)

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Zekang Li, Jinchao Zhang, Zhengcong Fei, Yang Feng, Jie Zhou

Blinded by Generated Contexts: How Language Models Merge Generated and Retrieved Contexts When Knowledge Conflicts? (2024.acl-long)

Challenge:	Existing methods to evaluate consistency capacity of open-domain chatbots are costly and low-efficient.
Approach:	They propose an efficient framework for evaluating consistency of open-domain chatbots . they use human judges to interact with chatbot, which is costly and low-efficient .
Outcome:	The proposed framework can assess the consistency capacity of chatbots and achieve a high ranking correlation with the human evaluation.

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Hexiang Tan, Fei Sun, Wanli Yang, Yuanzhuo Wang, Qi Cao, Xueqi Cheng

Transforming Visual Scene Graphs to Image Captions (2023.acl-long)

Challenge:	Recent advances in augmenting Large Language Models (LLMs) with auxiliary information have significantly revolutionized their efficacy in knowledge-intensive tasks.
Approach:	They propose a systematic framework to identify whether LLMs’ responses are attributed to either generated or retrieved contexts.
Outcome:	The proposed framework identifies whether LLMs’ responses are attributed to either generated or retrieved contexts.

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Xu Yang, Jiawei Peng, Zihua Wang, Haiyang Xu, Qinghao Ye, Chenliang Li, Songfang Huang, Fei Huang, Zhangzikang Li, Yu Zhang

Generating Classical Chinese Poems from Vernacular Chinese (D19-1)

Challenge:	Existing approaches to generate captions using image captioning are based on multi-head attention (MHA)
Approach:	They propose to transform scene graphs into more descriptive captions by using multi-head attention to build a Graph Neural Network (GNN) . they construct a Mixture-of-Expert (MOE)-based decoder where each expert is built on MHA for discriminating the graph embeddings to generate different kinds of words.
Outcome:	The proposed framework can generate captions from multiple visual features and objects . it is based on a mixture-of-expert (MOE)-based decoder based upon MHA .

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Zhichao Yang, Pengshan Cai, Yansong Feng, Fei Li, Weijiang Feng, Elena Suet-Ying Chiu, Hong Yu

Text Generation

Challenge:	Existing models for classical Chinese poetry generation only allow users to use keywords to interfere with the meaning of generated poems.
Approach:	They propose a model to generate classical Chinese poems from vernacular . their model uses unsupervised machine translation to generate Chinese poems . human evaluation shows it can generate high-quality poems comparable to amateur poems - authors .
Outcome:	The proposed model improves the perplexity and BLEU of the proposed model compared with typical models and human evaluation shows it generates high-quality poems comparable to amateur poems.

Multi-Value-Product Retrieval-Augmented Generation for Industrial Product Attribute Value Identification (2025.emnlp-industry)

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Huike Zou, Haiyang Yang, Yindu Su, Chen Li Yu, Qinye Xie, Chengbao Lian, Qingheng Zhang, Shuguang Han, Fei Huang, Jufeng Chen

The Butterfly Effect of Model Editing: Few Edits Can Trigger Large Language Models Collapse (2024.findings-acl)

Challenge:	Existing methods for product attribute value identification suffer from cascading errors and lack of generalization capability.
Approach:	They propose a multi-level retrieval scheme that uses products and attribute values as distinct hierarchical levels in PAVI domain.
Outcome:	The proposed method performs better than the state-of-the-art methods on a real-world industrial dataset.

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Wanli Yang, Fei Sun, Xinyu Ma, Xun Liu, Dawei Yin, Xueqi Cheng

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

Challenge:	Even a single edit can trigger model collapse, manifesting as significant performance degradation in various benchmark tasks.
Approach:	They propose to use perplexity as a surrogate metric to determine whether an edited model's performance is affected by a single edit.
Outcome:	The proposed method shows that even a single edit can cause model collapse, manifesting as significant performance degradation in various benchmark tasks.

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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

Visual Data in NLP

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.

Towards Efficient LLM Grounding for Embodied Multi-Agent Collaboration (2025.findings-acl)

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Yang Zhang, Shixin Yang, Chenjia Bai, Fei Wu, Xiu Li, Zhen Wang, Xuelong Li

Green & Sustainable NLP

Challenge:	Existing methods for grounding large language models suffer from inefficient querying . Existing approaches that rely on physical verification or self-reflection suffer from excessive querying.
Approach:	They propose a framework that introduces Reinforced Advantage feedback for efficient self-refinement of plans.
Outcome:	The proposed framework surpasses baselines in success rate and significantly decreases interaction steps of agents and query rounds of LLMs.

Extending Complex Logical Queries on Uncertain Knowledge Graphs (2025.acl-long)

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Weizhi Fei, Zihao Wang, Hang Yin, Yang Duan, Yangqiu Song

Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA (2024.emnlp-main)

Challenge:	Existing studies on logical queries on knowledge graphs overlook the incompleteness of KGs.
Approach:	They propose an ML-based approach to answer soft queries on uncertain knowledge . they propose to use forward inference and backward calibration to avoid catastrophic errors .
Outcome:	The proposed method ensures there are no catastrophic cascading errors while maintaining the same complexity as state-of-the-art inference algorithms for first-order queries.

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Minzheng Wang, Longze Chen, Fu Cheng, Shengyi Liao, Xinghua Zhang, Bingli Wu, Haiyang Yu, Nan Xu, Lei Zhang, Run Luo, Yunshui Li, Min Yang, Fei Huang, Yongbin Li

Translationese-index: Using Likelihood Ratios for Graded and Generalizable Measurement of Translationese (2025.emnlp-main)

Challenge:	Existing benchmarks for evaluating long-context language models employ irrelevant noise texts to artificially extend the length of test cases, diverging from the real-world scenarios of long-constituency applications.
Approach:	They propose a long-context benchmark, Loong, aligning with realistic scenarios through extended multi-document question answering (QA) .
Outcome:	The proposed model can scale up the context window of large language models to perform in-depth analysis of multiple long documents.

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Yikang Liu, Wanyang Zhang, Yiming Wang, Jialong Tang, Pei Zhang, Baosong Yang, Fei Huang, Rui Wang, Hai Hu

Machine Translation

Challenge:	Translationese is a linguistic property that is often introduced in the translation process that is different from those of original texts.
Approach:	They propose to use synthesized translations and translations in the wild to evaluate T-index's generalizability in cross-domain settings and its validity against human judgments.
Outcome:	The proposed measure can generalize to unseen genres, authors, and language pairs.

Rethinking Denoised Auto-Encoding in Language Pre-Training (2021.emnlp-main)

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Fuli Luo, Pengcheng Yang, Shicheng Li, Xuancheng Ren, Xu Sun, Songfang Huang, Fei Huang

Deputy: Accelerating Large Language Model Inference with Dynamic Low-Rank Substitution (2026.findings-acl)

Challenge:	Pre-trained models such as BERT have achieved success in learning sequence representations, but they tend to learn representations that are covariant with the noise of pre-training.
Approach:	They propose to train self-trained models to learn noise invariant sequence representations . they encourage consistency between original sequence and corrupted version via unsupervised instance-wise training signals.
Outcome:	The proposed model improves on 11 natural language understanding and cross-modal tasks and achieves 0.6% gain on GLUE benchmarks and 0.8% increment on NLVR2 .

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Yuhua Zhou, Shichao Weng, Changhai Zhou, Yuhan Wu, Qian Qiao, Jun Gao, Fei Yang, Aimin Pan

Supervised Optimism Correction: Be Confident When LLMs Are Sure (2025.findings-acl)

Challenge:	Existing dynamic schemes such as early-exit and layer-drop reduce FLOPs but break batch processing or introduce KV-cache inconsistency.
Approach:	They propose a dynamic low-rank substitution framework that employs a lightweight decision module at each layer to dynamically determine the execution branch for different tokens.
Outcome:	The proposed model reduces computation by approximately 40% compared to the original dense model while outperforming existing baseline methods.

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Junjie Zhang, Rushuai Yang, Shunyu Liu, Ting-En Lin, Fei Huang, Yi Chen, Yongbin Li, Dacheng Tao

Triggerless Backdoor Attack for NLP Tasks with Clean Labels (2022.naacl-main)

Challenge:	Recent advances in Large Language Models (LLMs) have demonstrated remarkable success across diverse tasks such as instruction following, code generation, and medical diagnosis.
Approach:	They propose a supervised fine-tuning-based auxiliary loss for Q-value estimations during supervised refinement.
Outcome:	The proposed method outperforms beam search on GSM8K, MATH, and GAOKAO on reasoning benchmarks.

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Leilei Gan, Jiwei Li, Tianwei Zhang, Xiaoya Li, Yuxian Meng, Fei Wu, Yi Yang, Shangwei Guo, Chun Fan

Robustness in NLP

Challenge:	Backdoor attacks are a new threat to neural natural language processing models due to the fragility and lack of interpretability of NLP models.
Approach:	They propose a method to perform backdoor attacks without an external trigger . they propose to use clean-labeled examples to generate poisoned clean-labelled examples .
Outcome:	The proposed strategy is effective and hard to defend due to its triggerless nature.

MPBoCo: Multimodal Prompt-based Boundary-enhanced Continual Framework for Joint Entity and Relation Extraction (2026.acl-long)

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Guanglu Sun, Xinyu Liu, Lili Liang, Yang Yu, Fei Lang, Suxia Zhu, Ming Liu

Low-Resource NLP

Challenge:	Existing methods struggle to balance real-time adaptability and computational efficiency in continual learning scenarios.
Approach:	They propose a Continual Multimodal Entity and Relation Joint Extraction task and a Multimodal Prompt-based Boundary-enhanced Continuum framework that stores task-specific knowledge via learnable multimodal prompts.
Outcome:	The proposed framework outperforms baseline methods in real-world scenarios by 5.5% and 7.2%.

Exploiting Contrastive Learning and Numerical Evidence for Confusing Legal Judgment Prediction (2023.findings-emnlp)

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Leilei Gan, Baokui Li, Kun Kuang, Yating Zhang, Lei Wang, Anh Luu, Yi Yang, Fei Wu

Self-Explanation Prompting Improves Dialogue Understanding in Large Language Models (2024.lrec-main)

Challenge:	Existing studies fail to distinguish different classification errors with a standard cross-entropy classification loss and ignore the numbers in the fact description for predicting the term of penalty.
Approach:	They propose to extract crime amounts from fact description and use them to learn distinguishable representations to exploit the numbers in the fact description for predicting the term of penalty.
Outcome:	The proposed method achieves state-of-the-art results on real-world datasets and ablation studies demonstrate the effectiveness of each component.

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Haoyu Gao, Ting-En Lin, Hangyu Li, Min Yang, Yuchuan Wu, Wentao Ma, Fei Huang, Yongbin Li

SPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understanding (2022.coling-1)

Challenge:	Recent advances in large language models (LLMs) have achieved great success in various NLP tasks, but the vast model parameters pose challenges in downstream fine-tuning.
Approach:	They propose a task-agnostic prompting strategy that analyzes each dialogue utterance before task execution to enhance LLMs' comprehension in multi-turn dialogues.
Outcome:	The proposed strategy outperforms other zero-shot prompts and matches or exceeds efficacy of few-shot ones.

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Wanwei He, Yinpei Dai, Binyuan Hui, Min Yang, Zheng Cao, Jianbo Dong, Fei Huang, Luo Si, Yongbin Li

Enhancing LLM Language Adaption through Cross-lingual In-Context Pre-training (2025.emnlp-main)

Challenge:	Existing methods for dialog understanding only consider self-augmented dialogs as positive samples and treat all other dialogs like negative ones.
Approach:	They propose a tree-structured pre-trained conversation model which learns dialog representations from limited labeled dialogs and large-scale unlabeled dialog corpora via semi-supervised contrastive pre-training.
Outcome:	The proposed model can achieve state-of-the-art results on the DialoGLUE benchmark.

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Linjuan Wu, Hao-Ran Wei, Huan Lin, Tianhao Li, Baosong Yang, Fei Huang, Weiming Lu

Defending Large Language Models Against Jailbreaking Attacks Through Goal Prioritization (2024.acl-long)

Challenge:	Existing methods for enhancing cross-lingual transfer are limited by parallel resources and lack linguistic and domain coverage.
Approach:	They propose a cross-lingual in-context pre-training approach that leverages semantically related bilingual Wikipedia documents to enhance cross-linguistic transfer.
Outcome:	The proposed approach improves multilingual performance on three models across six target languages.

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Zhexin Zhang, Junxiao Yang, Pei Ke, Fei Mi, Hongning Wang, Minlie Huang

Robustness in NLP

Challenge:	Despite the success of jailbreaking attacks, there is a lack of effort in defending against them.
Approach:	They propose to integrate goal prioritization at both training and inference stages to counteract this conflict between the goals of being helpful and ensuring safety.
Outcome:	The proposed approach reduces the Attack Success Rate (ASR) of jailbreaking from 66.4% to 3.6% for ChatGPT and reduces it from 71.0% to 6.6% for Llama2-13B.

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

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

FlattenQuant: Breaking through the Inference Compute-bound for Large Language Models with Per-tensor Quantization (2024.lrec-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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Yi Zhang, Fei Yang, Shuang Peng, Fangyu Wang, Aimin Pan

DAMP: Doubly Aligned Multilingual Parser for Task-Oriented Dialogue (2023.acl-long)

Challenge:	Large language models (LLMs) have demonstrated state-of-the-art accuracies across tasks, but their latency and GPU memory consumption limit their performance.
Approach:	They propose a method which flattens the tensor to achieve low bit per-tensori quantization with minimal accuracy loss.
Outcome:	The proposed method achieves low bit per-tensor quantization with minimal accuracy loss.

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William Held, Christopher Hidey, Fei Liu, Eric Zhu, Rahul Goel, Diyi Yang, Rushin Shah

Robustness in NLP

Challenge:	Existing studies show that multilingual models are less robust for semantic parsing compared to other tasks.
Approach:	They propose a constrained optimization technique to optimize multilingual parsing systems for multilingual use.
Outcome:	The proposed technique outperforms XLM-R and mT5-Large on three benchmarks and significantly outperformed other models.

OpenUE: An Open Toolkit of Universal Extraction from Text (2020.emnlp-demos)

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Ningyu Zhang, Shumin Deng, Zhen Bi, Haiyang Yu, Jiacheng Yang, Mosha Chen, Fei Huang, Wei Zhang, Huajun Chen

Information Extraction & Text Mining

Challenge:	a large number of natural language processing tasks focus on token-level or sentence-level understandings.
Approach:	They propose an open-source and extensible toolkit for various extraction tasks . they deploy an online demo with restful APIs to support real-time extraction .
Outcome:	The proposed model can be used to extract information from text without training and deployment.

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

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

Learning Dynamic Context Augmentation for Global Entity Linking (D19-1)

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

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Xiyuan Yang, Xiaotao Gu, Sheng Lin, Siliang Tang, Yueting Zhuang, Fei Wu, Zhigang Chen, Guoping Hu, Xiang Ren

Knowledge Representation

Challenge:	Existing collective entity linking methods are expensive and often lack local context information.
Approach:	They propose a dynamic context-augmented inference model that can be used to make collective inference.
Outcome:	The proposed model can cope with different local EL models with different learning settings, base models, decision orders and attention mechanisms.

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

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

MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance (D19-1)

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

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Wei Zhao, Maxime Peyrard, Fei Liu, Yang Gao, Christian M. Meyer, Steffen Eger

Representation Learning

Challenge:	Existing evaluation metrics are not capable of evaluating text quality.
Approach:	They propose a metric that compares system output against reference texts based on semantics rather than surface forms.
Outcome:	The proposed metric shows a high correlation with human judgment of text quality on a number of text generation tasks.

ReSee: Responding through Seeing Fine-grained Visual Knowledge in Open-domain Dialogue (2023.emnlp-main)

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Haoqin Tu, Yitong Li, Fei Mi, Zhongliang Yang

The Fall of ROME: Understanding the Collapse of LLMs in Model Editing (2024.findings-emnlp)

Challenge:	Existing multimodal dialogue systems are limited by the scale and quality of available datasets or the coarse concept of visual knowledge.
Approach:	They propose to explicitly split visual knowledge into finer granularity and turn-level . they propose a framework to add visual representation into vanilla dialogue models .
Outcome:	The proposed framework outperforms state-of-the-art methods on automatic and human evaluations.

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Wanli Yang, Fei Sun, Jiajun Tan, Xinyu Ma, Du Su, Dawei Yin, Huawei Shen

BaseCal: Unsupervised Confidence Calibration via Base Model Signals (2026.acl-long)

Challenge:	Recent studies have found that model editing methods can cause large language models to collapse with just a single edit.
Approach:	They propose a method that uses prefixed keys and adds prefixes during testing to prevent model collapse.
Outcome:	The proposed method prevents model collapse while maintaining effectiveness, the authors show . Rank-One Model Editing (ROME) has been found to cause model collapse with just a single edit .

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Hexiang Tan, Wanli Yang, Junwei Zhang, Xin Chen, Rui Tang, Du Su, Jingang Wang, Yuanzhuo Wang, Fei Sun, Xueqi Cheng

More Data or Better Data? A Critical Analysis of Data Selection and Synthesis for Mathematical Reasoning (2025.emnlp-industry)

Challenge:	Post-trained LLMs typically compromise reliability with severe overconfidence, resulting in inaccurate responses.
Approach:	They propose a solution that feeds PoLLMs into the base LLM to get confidence.
Outcome:	The proposed solution reduces expected calibration error (ECE) by 42.90% compared to the best unsupervised baselines.

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Yike Zhao, Simin Guo, Ziqing Yang, Shifan Han, Dahua Lin, Fei Tan