Papers by Xin Huang

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

Challenge:	Large Language Models (LLMs) have shown impressive language capabilities, but most of them have very unbalanced performance across different languages.
Approach:	They propose to use question translation data to enhance LLMs' multilingual capabilities by using mechanistic interpretability methods.
Outcome:	The proposed method improves multilingual alignment even with unannotated answers in English and a wide range of languages even with instruction-tuned LLMs.

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

AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments (2026.acl-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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Zhiheng Xi, Dingwen Yang, Jiaqi Liu, Jixuan Huang, Honglin Guo, Baodai Huang, Tinggang Chen, Qi Zhang, Zhonghang Lu, Chenyu Liu, Jiajun Sun, Jiazheng Zhang, Dingwei Zhu, Xin Guo, Junzhe Wang, Zhihao Zhang, Yuming Yang, Junjie Ye, Minghe Gao, Dongrui Liu, Jiaming Ji, Guohao Li, Tao Gui, Qi Zhang, Xuanjing Huang

MentalSeek-Dx: Towards Progressive Hypothetico-Deductive Reasoning for Real-world Psychiatric Diagnosis (2026.acl-long)

Challenge:	Existing benchmarks evaluate agents in simplified, idealized settings, relying on pre-packaged tool interfaces, overlooking critical steps, and assume inputs are clean and fully specified.
Approach:	They propose a framework that evaluates language agents in simplified, idealized settings . they show that even SOTA systems like Gemini and GPT-5 struggle on AgentGym2 .
Outcome:	Experiments on 15 proprietary and open-source models show that even SOTA systems like Gemini and GPT-5 struggle on AgentGym2 .

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Xiao Sun, null Ymyang, Xinyi Jiang, Yu Tian, Junnan Zhu, Jiang Zhong, Qin Lei, Jingwang Huang, Haoyang Zeng, Xinyu Zhou, Xin Xiao, Kaiwen Wei

Taming System Complexity: Demystifying Software Engineering Agents in Diagnosing Linux Kernel Faults (2026.acl-long)

Challenge:	Mental health disorders represent a burgeoning global public health challenge . lack of ecological validity and fine-grained diagnostic supervision limits their utility .
Approach:	They propose a medical-specialized LLM trained to internalize clinical reasoning process through supervised trajectory construction and curriculum-based reinforcement learning.
Outcome:	The proposed model achieves state-of-the-art with only 14B parameters, establishing a clinically grounded framework for reliable psychiatric diagnosis.

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Zhenhao Zhou, Zhuochen Huang, Yike He, Chong Wang, Jiajun Wang, Yijian Wu, Xin Peng, Yiling Lou

Challenge:	Existing LLM agents struggle with identifying bugs in the Linux kernel . bugs can affect billions of users, affecting the Linux Foundation's research on the topic .
Approach:	They propose a LinuxFLBench benchmark to measure the accuracy of LLM agents on the Linux kernel.
Outcome:	The proposed framework improves FL accuracy with minimal costs.

TextMixer: Mixing Multiple Inputs for Privacy-Preserving Inference (2023.findings-emnlp)

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Xin Zhou, Yi Lu, Ruotian Ma, Tao Gui, Qi Zhang, Xuanjing Huang

Q-TOD: A Query-driven Task-oriented Dialogue System (2022.emnlp-main)

Challenge:	Pre-trained language models (PLMs) are often deployed as cloud services, enabling users to upload textual data and perform inference remotely.
Approach:	They propose a privacy-preserving inference framework called MixPi which aims to obfuscate a user's private input by mixing it with multiple other inputs.
Outcome:	The proposed framework surpasses existing privacy-preserving methods on token and sentence classification tasks.

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Xin Tian, Yingzhan Lin, Mengfei Song, Siqi Bao, Fan Wang, Huang He, Shuqi Sun, Hua Wu

Generative Text-to-Image Retrieval via Hierarchical Identifiers and Semantic Internalization (2026.findings-acl)

Challenge:	Existing pipelined task-oriented dialogue systems have difficulties adapting to unseen domains . end-to-end systems are plagued by large-scale knowledge bases in practice .
Approach:	They propose a query-driven task-oriented dialogue system that extracts dialogue context information into a natural language query.
Outcome:	The proposed system outperforms strong baselines and establishes a new state-of-the-art performance on three publicly available task-oriented dialogue datasets.

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Jie Huang, Junjie Wang, Xin Liao, Ziyou Jiang, Wenshuo Wang, Shoubin Li, Qing Wang

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

Challenge:	Existing text-to-image retrieval methods suffer from limited semantic discriminability, alignment bias, and closed-set restrictions.
Approach:	They propose a framework for semantic internalization for Generative Multimodal Alignment . they construct multi-granularity hierarchical identifiers to ensure unique, semantically consistent image representations .
Outcome:	The proposed framework outperforms state-of-the-art frameworks on Flickr30K and MS-COCO datasets . it achieves average Recall@1, Recall @5, and Recall_10 improvements of 10.65%, 8.50%, and 7.00% .

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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

Unlocking Human-Like Visible Logic: How Logic Diagrams Boost Logic Reasoning in Large Language Models? (2026.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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Xin Wu, Yuqi Bu, Mengchen Zhao, Qingbao Huang, Yi Cai

Temporal Scaling Law for Large Language Models (2025.emnlp-main)

Challenge:	Recent advances in large language models (LLMs) have demonstrated their remarkable capabilities in natural language understanding and generation, but they struggle with formal logical reasoning.
Approach:	They propose to incorporate visual logic diagrams into LLMs’ reasoning workflows to enhance their performance on formal logic tasks.
Outcome:	The proposed model improves on syllogistic and conditional reasoning with programmatically generated Venn, Euler, and Linear diagrams.

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Yizhe Xiong, Xiansheng Chen, Xin Ye, Hui Chen, Zijia Lin, Haoran Lian, Zhenpeng Su, Wei Huang, Jianwei Niu, Jungong Han, Guiguang Ding

ProofInfer: Generating Proof via Iterative Hierarchical Inference (2022.emnlp-main)

Challenge:	Existing studies have found that the test loss of LLMs scales as power-laws with model size, computational budget, and dataset size.
Approach:	They propose a concept of Temporal Scaling Law to study test loss of LLMs . they break down test loss into fine-grained token positions and develop a dynamic hyperbolic-law .
Outcome:	The proposed model predicts the test loss of LLMs as the training steps scale up.

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Zichu Fei, Qi Zhang, Xin Zhou, Tao Gui, Xuanjing Huang

Hephaestus: Improving Fundamental Agent Capabilities of Large Language Models through Continual Pre-Training (2025.naacl-long)

Challenge:	Existing proof generation models focus on generating several proof paths instead of a whole tree.
Approach:	They propose a method that generates the proof tree via iterative hierarchical inference . they propose coding the proof as plain text without losing structure information .
Outcome:	The proposed proof generation model significantly improves performance on widely-used datasets.

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Yuchen Zhuang, Jingfeng Yang, Haoming Jiang, Xin Liu, Kewei Cheng, Sanket Lokegaonkar, Yifan Gao, Qing Ping, Tianyi Liu, Binxuan Huang, Zheng Li, Zhengyang Wang, Pei Chen, Ruijie Wang, Rongzhi Zhang, Nasser Zalmout, Priyanka Nigam, Bing Yin, Chao Zhang

GMFL: Efficient Global Masking for Federated LLM Fine-tuning (2026.acl-long)

Challenge:	Existing LLMs often rely on complex prompting or extensive fine-tuning to introduce new capabilities while preserving strong generalizability.
Approach:	They propose a large-scale pre-training corpus to enhance LLM agents' capabilities . they use 103B agent-specific data encompassing 76,537 APIs .
Outcome:	The proposed training corpus outperforms open-source LLMs and commercial LLM agents on three agent 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.

What Knowledge Is Needed? Towards Explainable Memory for kNN-MT Domain Adaptation (2023.findings-acl)

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Wenhao Zhu, Shujian Huang, Yunzhe Lv, Xin Zheng, Jiajun Chen

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

Challenge:	kNN-MT builds an external datastore, which saves all target language token occurrences in the parallel corpus.
Approach:	They propose a new paradigm for domain adaptation by building an external datastore which usually saves all target language token occurrences in the parallel corpus.
Outcome:	The proposed model can be easily pruned according to local correctness, and it is more explainable.

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

PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoning (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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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

LFKQG: A Controlled Generation Framework with Local Fine-tuning for Question Generation over Knowledge Bases (2022.coling-1)

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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Zichu Fei, Xin Zhou, Tao Gui, Qi Zhang, Xuanjing Huang

When Efficiency Becomes a Vulnerability: Computational Cost Attacks on WebAgents (2026.acl-long)

Challenge:	Existing KBQG models focus on the most relevant part of the answer entity, while neglecting the rest of the subgraph.
Approach:	They propose a controlled generation framework for Question Generation over Knowledge Bases that generates questions with out-of-vocabulary (OOV) predicates.
Outcome:	The proposed framework outperforms existing methods significantly on three widely-used benchmark datasets SimpleQuestion, PathQuestions, and WebQuestIONS.

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Liang-Bo Ning, Yuchen Zhu, Heqing Huang, Xin Wang, Yi Chang, Li Qing, Wenqi Fan

Unveiling and Consulting Core Experts in Retrieval-Augmented MoE-based LLMs (2024.emnlp-main)

Challenge:	Existing WebAgents suffer from computational cost attacks due to long reasoning processes and excessive computational cost.
Approach:	They propose a framework that generates adversarial prompts and a reinforcement learning-enhanced selector to identify the most effective perturbations.
Outcome:	The proposed framework exploits large language models to generate diverse adversarial prompts and a reinforcement learning–enhanced selector to identify the most effective perturbations.

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Xin Zhou, Ping Nie, Yiwen Guo, Haojie Wei, Zhanqiu Zhang, Pasquale Minervini, Ruotian Ma, Tao Gui, Qi Zhang, Xuanjing Huang

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

Challenge:	Existing research seeks to enhance RAG performance by retrieving higher-quality documents or designing RAG-specific LLMs, but internal mechanisms that contribute to RAG’s effectiveness remain underexplored.
Approach:	They propose to examine the internal mechanisms within the popular Mixture-of-Expert (MoE)-based LLMs and examine their ability to improve RAG by examining expert activations.
Outcome:	The proposed method significantly improved the ability of Large Language Models (LLMs) to solve knowledge-intensive tasks.

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

InsLogicBench: An Argumentation Logic Grounded Benchmark for Complex Insurance Claims Adjudication (2026.acl-long)

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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Jin Liu, Yunpeng Liu, Keyi Wang, Jie Shi, Xiao Xu, Wenkang Huang, Xingzhong Xu, Xin Liang, Yanghua Xiao

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

Challenge:	Existing benchmarks for insurance claims adjudication are limited to information retrieval or simple multiple-choice setups.
Approach:	They propose a benchmark that provides complete reasoning traces linking factual inputs, relevant policy clauses, and final verdicts.
Outcome:	The proposed benchmark shows that models often produce correct decisions but fail to provide precise justifications, highlighting a critical discrepancy between decision accuracy and logical reasoning capabilities.

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

Counteracting the Matthew Effect in Self-Improvement of LVLMs through Head-Tail Re-balancing (2026.acl-long)

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

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Xin Guo, Zhiheng Xi, Yiwen Ding, Yitao Zhai, Xiaowei Shi, Xunliang Cai, Tao Gui, Qi Zhang, Xuanjing Huang

SQLWOZ: A Realistic Task-Oriented Dialogue Dataset with SQL-Based Dialogue State Representation for Complex User Requirements (2025.emnlp-main)

Challenge:	Large vision language models have impressive reasoning capabilities across complex multimodal tasks.
Approach:	They propose to use distribution-reshaping and trajectory-rebalancing to improve visual reasoning capabilities.
Outcome:	Experiments on Qwen2-VL-7B-Instruct and InternVL2.5-4B models show that their methods outperform baselines by 3.86 points.

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Heng-Da Xu, Xian-Ling Mao, Fanshu Sun, Tian-Yi Che, Cheng-Xin Xin, Heyan Huang

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

Challenge:	Existing TOD datasets present simplified interactions with simple slot-value style constraints and preferences.
Approach:	They propose a novel TOD dataset that captures complex user requirements using SQL statements.
Outcome:	The proposed dataset captures complex, real-world user requirements.

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

PRIM: Towards Practical In-Image Multilingual Machine Translation (2025.emnlp-main)

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

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Yanzhi Tian, Zeming Liu, Zhengyang Liu, Chong Feng, Xin Li, Heyan Huang, Yuhang Guo

Adaptive Nearest Neighbor Machine Translation (2021.acl-short)

Challenge:	Current research on in-image machine translation focuses on synthetic data with simple background, single font, fixed text position, and bilingual translation.
Approach:	They propose an end-to-end model to handle the challenge of practical conditions in PRIM . they annotate a real-world one-line text image with complex background, fonts, diverse text positions .
Outcome:	The proposed model improves translation quality and visual effect compared to other models.

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Xin Zheng, Zhirui Zhang, Junliang Guo, Shujian Huang, Boxing Chen, Weihua Luo, Jiajun Chen

Machine Translation

Challenge:	kNN-MT uses pre-trained NMT model with token-level k-nearest-neighbor retrieval to improve translation accuracy.
Approach:	They propose a method that combines a pre-trained NMT model with token-level k-nearest-neighbor retrieval to improve translation accuracy.
Outcome:	The proposed method outperforms the existing model on four benchmark datasets and is open-source.

MAGRET: Machine-generated Text Detection with Rewritten Texts (2025.coling-main)

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Yifei Huang, Jiuxin Cao, Hanyu Luo, Xin Guan, Bo Liu

A Survey of Post-Training Scaling in Large Language Models (2025.acl-long)

Challenge:	Existing studies focus on detecting machine-generated text in open-source models, but their performance on closed-source large models is limited.
Approach:	They propose a method to detect rewritten text from large language models using a BERT encoder and propose to refine it to achieve semantic alignment.
Outcome:	The proposed method outperforms baseline methods on three text-generated datasets.

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Hanyu Lai, Xiao Liu, Junjie Gao, Jiale Cheng, Zehan Qi, Yifan Xu, Shuntian Yao, Dan Zhang, Jinhua Du, Zhenyu Hou, Xin Lv, Minlie Huang, Yuxiao Dong, Jie Tang

GhostBERT: Generate More Features with Cheap Operations for BERT (2021.acl-long)

Challenge:	Large language models (LLMs) have demonstrated proficiency in understanding and generating human natural languages.
Approach:	They propose a framework for scaling large language models using supervised fine-tuning, RLxF and test-time compute methodologies.
Outcome:	The proposed model can be used to understand and generate human natural languages.

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Zhiqi Huang, Lu Hou, Lifeng Shang, Xin Jiang, Xiao Chen, Qun Liu

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

Challenge:	Existing studies show that some parameters in pre-trained language models can be pruned away without severe accuracy degradation.
Approach:	They propose a method which generates more features with very cheap operations from the remaining features and can be applied to unpruned BERT models to enhance their performance.
Outcome:	Empirical results on the GLUE benchmark on three backbone models (i.e., BERT, RoBERTa and ELECTRA) verify the efficacy of the proposed method.

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

ToolPRM: Fine-Grained Inference Scaling of Structured Outputs for Function Calling (2026.acl-long)

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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Jianghao Lin, Yuanyuan Shi, Xin Peng, Renjie Ding, Hairui Wang, Yuxuan Peng, Bizhe Bai, Weixi Song, Fengshuo Bai, Huacan Chai, Weinan Zhang, Fei Huang, Ying Wen

Challenge:	Existing research on inference scaling focuses on unstructured output generation tasks, such as mathematical problems.
Approach:	They propose an inference-scaling framework that combines fine-grained beam search with ToolPRM, a process reward model scoring each intra-call decision.
Outcome:	The proposed framework outperforms outcome and coarse-grained reward models in predictive accuracy and yields consistent test-time gains on multiple function-calling benchmarks.

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

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

Green & Sustainable NLP

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

Evidence-Augmented Policy Optimization with Reward Co-Evolution for Long-Context Reasoning (2026.acl-long)

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Xin Guan, Zijian Li, Shen Huang, Pengjun Xie, Jingren Zhou, Jiuxin Cao

Making Parameter-efficient Tuning More Efficient: A Unified Framework for Classification Tasks (2022.coling-1)

Challenge:	Evidence-Augmented Policy Optimization (EAPO) improves long-context reasoning performance . Xu et al., 2025): large language models are a critical part of NLP .
Approach:	They propose an Evidence-Augmented Reasoning paradigm that uses a group-relative reward to improve evidence quality.
Outcome:	EAPO significantly improves long-context reasoning performance compared to baselines.

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Xin Zhou, Ruotian Ma, Yicheng Zou, Xuanting Chen, Tao Gui, Qi Zhang, Xuanjing Huang, Rui Xie, Wei Wu

Support or Refute: Analyzing the Stance of Evidence to Detect Out-of-Context Mis- and Disinformation (2023.emnlp-main)

Challenge:	Large pre-trained language models (PLMs) have demonstrated superior performance in industrial applications.
Approach:	They propose a framework that re-uses existing parameter-efficient methods with a unified classifier.
Outcome:	The proposed framework improves the efficiency of existing parameter-efficient methods with a unified classifier.

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Xin Yuan, Jie Guo, Weidong Qiu, Zheng Huang, Shujun Li

Investigating and Scaling up Code-Switching for Multilingual Language Model Pre-Training (2025.findings-acl)

Challenge:	Mis- and disinformation online are a major source of harms of different kinds . out-of-context information is where different pieces of information are falsely associated . past studies have attempted to defend against OOC mis- and deinformation through external evidence, but they disregard the role of different pieces with different stances.
Approach:	They propose a stance extraction network that can extract stances of different pieces of evidence in a single framework.
Outcome:	The proposed model outperforms the state-of-the-art models on a public large-scale dataset with a performance gain of 3.2% in accuracy.

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Zhijun Wang, Jiahuan Li, Hao Zhou, Rongxiang Weng, Jingang Wang, Xin Huang, Xue Han, Junlan Feng, Chao Deng, Shujian Huang

LogRules: Enhancing Log Analysis Capability of Large Language Models through Rules (2025.findings-naacl)

Challenge:	Large language models (LLMs) exhibit remarkable multilingual capabilities despite the extreme language imbalance in the pre-training data.
Approach:	They investigate the existence of code-switching in the pre-training corpus and categorize it into four types within two quadrants.
Outcome:	The proposed approach improves performance across benchmarks and representation space.

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Xin Huang, Ting Zhang, Wen Zhao

PLATO-XL: Exploring the Large-scale Pre-training of Dialogue Generation (2022.findings-aacl)

Challenge:	Existing large language models (LLMs) exhibit hallucinations when analyzing logs due to the implicit knowledge and rules in logs that LLMs cannot capture.
Approach:	They propose a lightweight log analysis framework that generates and utilizes rules through LLMs.
Outcome:	The proposed framework outperforms LLM-based methods in log parsing and anomaly detection tasks and achieves better performance compared to case-based approaches.

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Siqi Bao, Huang He, Fan Wang, Hua Wu, Haifeng Wang, Wenquan Wu, Zhihua Wu, Zhen Guo, Hua Lu, Xinxian Huang, Xin Tian, Xinchao Xu, Yingzhan Lin, Zheng-Yu Niu

Dialogue Systems & Conversational Agents

Challenge:	Experimental results show PLATO-XL achieves state-of-the-art results across multiple conversational tasks.
Approach:	They propose to train PLATO-XL models with up to 11 billion parameters, trained on Chinese and English social media conversations.
Outcome:	The proposed model achieves state-of-the-art on multiple conversational tasks, verifying its potential as a foundation model of conversational AI.

Segment-Level and Category-Oriented Network for Knowledge-Based Referring Expression Comprehension (2023.findings-acl)

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Yuqi Bu, Xin Wu, Liuwu Li, Yi Cai, Qiong Liu, Qingbao Huang

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

Challenge:	Existing methods employ sentence-level retrieval and fusion methods, which may lead to similarity bias and interference from irrelevant information in unstructured knowledge sentences.
Approach:	They propose a segment-level and category-oriented network to solve similarity bias problem by segmenting and prompting knowledge retrieval methods and a category-based grounding method.
Outcome:	The proposed model eliminates similarity bias and improves the overall performance of the KB-REC task.

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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

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

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

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

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

Robustness in NLP

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.

Unseen Entity Handling in Complex Question Answering over Knowledge Base via Language Generation (2021.findings-emnlp)

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Xin Huang, Jung-Jae Kim, Bowei Zou

A Graph Interaction Framework on Relevance for Multimodal Named Entity Recognition with Multiple Images (2025.coling-main)

Challenge:	Existing methods for complex question answering are limited in the search space of all possible relation paths.
Approach:	They propose a method that directly generates an executable SPARQL query without simplification.
Outcome:	The proposed method significantly outperforms the previous methods and has higher interpretability and computational efficiency than the previous ones.

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Jiachen Zhao, Shizhou Huang, Xin Lin

Position IDs Matter: An Enhanced Position Layout for Efficient Context Compression in Large Language Models (2025.findings-emnlp)

Challenge:	Existing methods to determine whether images are related to named entities are not effective in multi-image scenarios.
Approach:	They propose a graph interaction framework on relevance for Multimodal Named Entity Recognition with multiple images to integrate human abilities into the model.
Outcome:	The proposed framework achieves state-of-the-art on benchmark datasets and compares with CLIP and CLIP-based approaches.

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Runsong Zhao, Xin Liu, Xinyu Liu, Pengcheng Huang, Chunyang Xiao, Tong Xiao, JingBo Zhu

Syntax-Aware Graph Attention Network for Aspect-Level Sentiment Classification (2020.coling-main)

Challenge:	Existing methods to compress context information ignore holistic contextual dependencies.
Approach:	They propose a method that adjusts position encodings to minimize the distance between context tokens and special tokens.
Outcome:	Enhanced Position Layout (EPL) improves compression of context information in large language models.

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Lianzhe Huang, Xin Sun, Sujian Li, Linhao Zhang, Houfeng Wang

Sentiment Analysis

Challenge:	Existing approaches to aspect-level sentiment classification focus on modeling the relationship between aspect words and their contexts with attention, and ignore the use of elaborate knowledge implicit in the context.
Approach:	They exploit syntactic awareness to the model by the graph attention network on the dependency tree structure and external pre-training knowledge by BERT language model, which helps to model the interaction between the context and aspect words better.
Outcome:	The proposed model can model the interaction between the context and aspect words better by using syntactic awareness and external pre-training knowledge.

Self-Supervised Sentence Polishing by Adding Engaging Modifiers (2023.acl-demo)

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Zhexin Zhang, Jian Guan, Xin Cui, Yu Ran, Bo Liu, Minlie Huang

Capturing Nuanced Preferences: Preference-Aligned Distillation for Small Language Models (2025.findings-acl)

Challenge:	a typical way to polish sentences is to add engaging modifiers, which enhance the meaning of the sentence.
Approach:	They propose a task that requires polishing sentences while maintaining fluency . they remove engaging modifiers from public resources and fine-tune LongLM to reconstruct original sentences from corrupted ones.
Outcome:	The proposed model generates more engaging sentences with suitable modifiers than strong baselines while keeping fluency.

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Yanggan Gu, Junzhuo Li, Sirui Huang, Xin Zou, Zhenghua Li, Xuming Hu

Large Language Models Are Cross-Lingual Knowledge-Free Reasoners (2025.naacl-long)

Challenge:	Existing methods for aligning small language models with human values model preference knowledge from large language models (LLMs) however, this limitation hinders student SLMs from capturing nuanced preferences for multiple responses.
Approach:	They propose a framework which models teacher's preference knowledge as a probability distribution over all potential preferences, thereby providing more nuanced supervisory signals.
Outcome:	The proposed framework outperforms existing methods on four benchmark tasks and achieves 20% improvement on AlpacaEval 2 and Arena-Hard.

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Peng Hu, Sizhe Liu, Changjiang Gao, Xin Huang, Xue Han, Junlan Feng, Chao Deng, Shujian Huang

Entity-level Cross-modal Learning Improves Multi-modal Machine Translation (2021.findings-emnlp)

Challenge:	Large language models have demonstrated impressive reasoning capabilities across multiple languages, but the relationship between capabilities in different languages is less explored.
Approach:	They decompose the process of reasoning tasks into two separate components: knowledge retrieval and knowledge-free reasoning.
Outcome:	The proposed model can be transferred across source-target languages despite secondary impact of resource in some specific target languages, while cross-lingual knowledge retrieval significantly hinders the transfer.

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Xin Huang, Jiajun Zhang, Chengqing Zong

Demystifying Data Organization for Enhanced LLM Training (2026.acl-long)

Challenge:	Multi-modal machine translation aims at improving translation performance by incorporating visual information.
Approach:	They propose an explicit entity-level cross-modal learning approach that aims to augment the entity representation by combining a translation task and a reconstruction task.
Outcome:	The proposed approach achieves comparable or even better performance than state-of-the-art models.

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Yalun Dai, Yangyu Huang, Tongshen Yang, Yonghan Wang, Xin Zhang, Wenshan Wu, Qihao Zhao, Hao Li, Yuanyuan Gao, Kim-Hui Yap, Scarlett Li

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

Challenge:	Large Language Models (LLMs) have revolutionized various fields, yet their training efficiency is heavily reliant on effective data curation.
Approach:	They propose to reuse pre-computed sample-level scores originally generated for data efficiency and introduce two new data ordering methods to improve LLM training.
Outcome:	The proposed methods improve the stability and performance of LLM training.

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

Information Extraction & Text Mining

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

AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora (2026.acl-long)

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Jiaxin Bai, Wei Fan, Qi Hu, Qing Zong, Chunyang Li, Hong Ting Tsang, Hongyu Luo, Yauwai Yim, Haoyu Huang, Xiao Zhou, Feng Qin, Tianshi Zheng, Xi Peng, Xin Yao, Huiwen Yang, Leijie Wu, JI Yi, Gong Zhang, Renhai Chen, Yangqiu Song

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

Challenge:	Existing knowledge graph construction frameworks require predefined schemas, limiting their scalability and domain coverage.
Approach:	They propose a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas.
Outcome:	The proposed framework outperforms state-of-the-art models on multi-hop QA tasks and enhances LLM factuality.

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

Machine Translation

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

FEA-Bench: A Benchmark for Evaluating Repository-Level Code Generation for Feature Implementation (2025.acl-long)

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Wei Li, Xin Zhang, Zhongxin Guo, Shaoguang Mao, Wen Luo, Guangyue Peng, Yangyu Huang, Houfeng Wang, Scarlett Li

Programming Languages in NLP

Challenge:	Existing benchmarks focus on standalone programming problems, such as HumanEval, MBPP, and LiveCodeBench.
Approach:	They propose to use large language models to evaluate their ability to perform incremental development within code repositories by collecting pull requests from 83 GitHub repositorias and using rule-based and intent-based filtering to construct task instances focused on new feature development.
Outcome:	The proposed benchmarks show that large language models perform significantly worse in the FEA-Bench, highlighting considerable challenges in repository-level incremental code development.

Mnemis: Dual-Route Retrieval on Hierarchical Graphs for Long-Term LLM Memory (2026.acl-long)

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Zihao Tang, Xin Yu, Ziyu Xiao, Zengxuan Wen, Zelin Li, Jiaxi Zhou, Hualei Wang, Haohua Wang, Haizhen Huang, Weiwei Deng, Feng Sun, Qi Zhang

MMLU-CF: A Contamination-free Multi-task Language Understanding Benchmark (2025.acl-long)

Challenge:	Existing methods for retrieving historical messages are based on similarity-based mechanisms.
Approach:	They propose a system that integrates System-1 similarity search with a complementary System-2 mechanism, termed Global Selection.
Outcome:	The proposed framework achieves state-of-the-art on long-term memory benchmarks and 93.9 on LoCoMo and 91.6 on LongMemEval-S.

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Qihao Zhao, Yangyu Huang, Tengchao Lv, Lei Cui, Qinzheng Sun, Shaoguang Mao, Xin Zhang, Ying Xin, Qiufeng Yin, Scarlett Li, Furu Wei

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

Challenge:	Multiple-choice question datasets like Massive Multitask Language Understanding (MMLU) have inevitably led to benchmark contamination, resulting in unreliable evaluation.
Approach:	They propose a contamination-free MCQ benchmark called MMLU-CF which reassesses LLMs’ understanding of world knowledge by averting both unintentional and malicious data contamination.
Outcome:	The proposed MMLU-CF reassesses LLMs’ understanding of world knowledge by averting both unintentional and malicious data contamination.

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

Learning to Maximize Mutual Information for Chain-of-Thought Distillation (2024.findings-acl)

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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Xin Chen, Hanxian Huang, Yanjun Gao, Yi Wang, Jishen Zhao, Ke Ding

Hyperbolic Capsule Networks for Multi-Label Classification (2020.acl-main)

Challenge:	Knowledge distillation is a technique of transferring knowledge from large, complex models to smaller ones.
Approach:	They propose a method utilizing chain-of-thought distillation to transfer knowledge from large, complex models to smaller ones by maximizing mutual information of the representation features of the two tasks.
Outcome:	The proposed method outperforms the state-of-the-art knowledge distillation method on four datasets.

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Boli Chen, Xin Huang, Lin Xiao, Liping Jing

TextFusion: Privacy-Preserving Pre-trained Model Inference via Token Fusion (2022.emnlp-main)

Challenge:	Existing methods for classification of labels are limited by feature aggregation and encoding.
Approach:	They propose to use hyperbolic capsule networks to capture fine-grained label information . they also propose a new routing method to adaptively adjust capsule number during routing .
Outcome:	The proposed method significantly improves the performance of multi-label classification on tail labels.

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Xin Zhou, Jinzhu Lu, Tao Gui, Ruotian Ma, Zichu Fei, Yuran Wang, Yong Ding, Yibo Cheung, Qi Zhang, Xuanjing Huang

Fast Quiet-STaR: Thinking Without Thought Tokens (2025.findings-emnlp)

Challenge:	Existing methods to preserve inference privacy are available as cloud services . however, the risk of privacy leakage remains, according to recent studies .
Approach:	They propose a method to preserve inference privacy by fusing token representations in the cloud.
Outcome:	The proposed method preserves inference privacy without sacrificing performance on different scenarios.

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Wei Huang, Yizhe Xiong, Xin Ye, Zhijie Deng, Hui Chen, Zijia Lin, Guiguang Ding

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

Challenge:	Large Language Models have achieved impressive performance across a range of tasks, but further gains require more than scaling up model sizes or training data.
Approach:	They propose a method that gradually reduces the number of thought tokens . this method allows models to internalize more abstract reasoning processes .
Outcome:	The proposed framework preserves the benefits of token-level reasoning while reducing computational cost.

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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

R-PRM: Reasoning-Driven Process Reward Modeling (2025.emnlp-main)

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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Shuaijie She, Junxiao Liu, Yifeng Liu, Jiajun Chen, Xin Huang, Shujian Huang

Hypernetwork-Assisted Parameter-Efficient Fine-Tuning with Meta-Knowledge Distillation for Domain Knowledge Disentanglement (2024.findings-naacl)

Challenge:	Existing Process Reward Models (PRMs) output evaluation scores directly, limiting both learning efficiency and evaluation accuracy.
Approach:	They propose a Reasoning-Driven Process Reward Modeling (R-PRM) which activates inherent reasoning to enhance process-level evaluation.
Outcome:	The proposed model outperforms baseline models on ProcessBench and PRMBench by 13.9 and 8.5 F1 scores.

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Changqun Li, Linlin Wang, Xin Lin, Shizhou Huang, Liang He

From Objectives to Questions: A Planning-based Framework for Educational Mathematical Question Generation (2025.acl-long)

Challenge:	Recent work on domain adaptation for text summarization fails to account for the huge gap between dialogue and general articles.
Approach:	They propose a hypernetwork-assisted encoder-decoder architecture with parameter-efficient fine-tuning to disentangle domain-invariant knowledge from source domains while learning specific knowledge of the target domain.
Outcome:	The proposed model can disentangle domain-invariant knowledge from source domains while learning specific knowledge of the target domain.

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Cheng Cheng, Zhenya Huang, GuanHao Zhao, Yuxiang Guo, Xin Lin, Jinze Wu, Xin Li, Shijin Wang

Text Generation

Challenge:	Traditional generation methods focus primarily on textual quality, but they fail to meet complex, multifaceted educational requirements.
Approach:	They propose a method for automatic generating high-quality mathematical problems that align with educational objectives using a dataset of 16k mathematical questions with multi-dimensional educational objectives.
Outcome:	The proposed method improves generating high-quality mathematical questions that meet multi-dimensional educational objectives.

JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs (2021.findings-acl)

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Pei Ke, Haozhe Ji, Yu Ran, Xin Cui, Liwei Wang, Linfeng Song, Xiaoyan Zhu, Minlie Huang

Teaching Your Models to Understand Code via Focal Preference Alignment (2025.emnlp-main)

Challenge:	Existing pre-trained models for knowledgegraph-to-text generation ignore graph structure during encoding and lack elaborate pre-training tasks to explicitly model graph-text alignments.
Approach:	They propose a graph-text joint representation learning model called JointGT which incorporates a structure-aware semantic aggregation module into each Transformer layer to preserve the graph structure.
Outcome:	The proposed model achieves state-of-the-art performance on various KG-to-text datasets.

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Jie Wu, Haoling Li, Xin Zhang, Xiao Liu, Yangyu Huang, Jianwen Luo, Yizhen Zhang, Zuchao Li, Ruihang Chu, Yujiu Yang, Scarlett Li

Programming Languages in NLP

Challenge:	Existing methods for supervised fine-tuning focus on unit test feedback to construct preference pairs.
Approach:	They propose a preference alignment framework that mimics human iterative debugging to refine Code LLMs.
Outcome:	Experiments show that Preference Learning improves on BigCodeBench and BigCodeBind tasks.

Universal Information Extraction with Meta-Pretrained Self-Retrieval (2023.findings-acl)

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Xin Cong, Bowen Yu, Mengcheng Fang, Tingwen Liu, Haiyang Yu, Zhongkai Hu, Fei Huang, Yongbin Li, Bin Wang

PEGRL: Improving Machine Translation by Post-Editing Guided Reinforcement Learning (2026.findings-acl)

Challenge:	Existing methods for IE are task-specific, resulting in specialized and isolated approaches for different tasks.
Approach:	They propose a method to retrieve task-specific knowledge from pretrained language models to enhance universal IE by using a Meta-Pretraining Algorithm.
Outcome:	The proposed method achieves the new state-of-the-art on 4 IE tasks, 12 datasets under fully-supervised, low-resource and few-shot scenarios.

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Yunzhi Shen, Hao Zhou, Xin Huang, Xue Han, Junlan Feng, Shujian Huang

CTR-Guided Generative Query Suggestion in Conversational Search (2025.emnlp-industry)

Challenge:	Reinforcement learning (RL) has shown strong promise for LLM-based machine translation . however, translation-oriented RL remains challenged by high-variance policy gradients induced by Monte Carlo baselines and large trajectory space that favors global exploration over fine-grained local optimization.
Approach:	They propose a two-stage RL framework that uses post-editing as an auxiliary task to stabilize training and guide overall optimization.
Outcome:	The proposed framework supports global exploration and fine-grained optimization while supporting global exploration.

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Erxue Min, Hsiu-Yuan Huang, Xihong Yang, Min Yang, Xin Jia, Yunfang Wu, Hengyi Cai, Junfeng Wang, Shuaiqiang Wang, Dawei Yin

Battling against Tough Resister: Strategy Planning with Adversarial Game for Non-collaborative Dialogues (2025.acl-long)

Challenge:	Generating effective query suggestions requires aligning model outputs with user click preferences.
Approach:	They propose a generative framework that leverages click modeling to denoise implicit feedback and enables reliable preference optimization for improving real-world user engagement.
Outcome:	The proposed framework outperforms strong baselines in CTR, relevance, diversity and diversity.

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Haiyang Wang, Zhiliang Tian, Yuchen Pan, Xin Song, Xin Niu, Minlie Huang, Bin Zhou

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

Challenge:	Non-collaborative dialogue involves two participants with conflicting interests engaging in multiround dialogue to achieve their own goals.
Approach:	They propose a Game-based Adversarial self-play InterActive training paradigm which constructs an adversarial two-player (a persuader and a resister) zero-sum game and guides the game to approximate Nash Equilibrium (NE) via reinforcement learning.
Outcome:	The proposed model achieves state-of-the-art performance on three datasets.

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

On Domain-Adaptive Post-Training for Multimodal Large Language Models (2025.findings-emnlp)

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

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Daixuan Cheng, Shaohan Huang, Ziyu Zhu, Xintong Zhang, Xin Zhao, Zhongzhi Luan, Bo Dai, Zhenliang Zhang

Language-Specific Neurons: The Key to Multilingual Capabilities in Large Language Models (2024.acl-long)

Challenge:	Adapting general multimodal large language models to specific domains is important for practical applications.
Approach:	They investigate domain adaptation of multimodal large language models via post-training . they develop a generate-then-filter pipeline that curates diverse visual instruction tasks .
Outcome:	The proposed model outperforms existing models in domain adaptation by combining data from open-source models with training pipelines.

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Tianyi Tang, Wenyang Luo, Haoyang Huang, Dongdong Zhang, Xiaolei Wang, Xin Zhao, Furu Wei, Ji-Rong Wen

Incorporating Hierarchy into Text Encoder: a Contrastive Learning Approach for Hierarchical Text Classification (2022.acl-long)

Challenge:	Despite the impressive multilingual capabilities demonstrated by LLMs, the understanding of how these abilities develop and function remains nascent.
Approach:	They propose a novel detection method to pinpoint language-specific neurons within LLMs by selectively activating or deactivating these neurons.
Outcome:	The proposed method can “steer” the output language of LLMs by selectively activating or deactivating language-specific neurons.

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Zihan Wang, Peiyi Wang, Lianzhe Huang, Xin Sun, Houfeng Wang

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

Challenge:	Existing methods encode text and label hierarchy separately and mix their representations for classification, where the hierarchy remains unchanged for all input text.
Approach:	They propose to embed hierarchy into a text encoder by combining input and output data to generate a hierarchy-aware representation.
Outcome:	Extensive experiments on three benchmark datasets verify the effectiveness of the proposed model.

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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.

LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding (2024.acl-long)

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Yushi Bai, Xin Lv, Jiajie Zhang, Hongchang Lyu, Jiankai Tang, Zhidian Huang, Zhengxiao Du, Xiao Liu, Aohan Zeng, Lei Hou, Yuxiao Dong, Jie Tang, Juanzi Li

Dynamic Collaboration of Multi-Language Models based on Minimal Complete Semantic Units (2025.emnlp-main)

Challenge:	Large language models (LLMs) can only handle texts a few thousand tokens long, limiting their applications on longer sequence inputs, such as books, reports, and codebases.
Approach:	They propose a bilingual, multi-task benchmark for long context understanding that extends context windows and more sophisticated memory mechanisms to improve models' long context capabilities.
Outcome:	The proposed model outperforms open-source models but struggles on longer contexts.

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Chao Hao, Zezheng Wang, Yanhua Huang, Ruiwen Xu, Wenzhe Niu, Xin Liu, Zitong Yu

Making Harmful Behaviors Unlearnable for Large Language Models (2024.findings-acl)

Challenge:	Existing methods to enhance reasoning capabilities of language models are expensive and often lack the ability to perform complex reasoning tasks.
Approach:	They propose a token-level multi-model collaboration strategy to enhance reasoning capabilities in language models by selecting the optimal tokens from the next token distributions.
Outcome:	The proposed method is superior to existing methods and will be released soon.

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Xin Zhou, Yi Lu, Ruotian Ma, Yujian Wei, Tao Gui, Qi Zhang, Xuanjing Huang

TIGEr: Text-to-Image Grounding for Image Caption Evaluation (D19-1)

Challenge:	Large language models (LLMs) are often customized by fine-tuning for the requirements of different domains.
Approach:	They propose a controllable training framework to make undesired behaviors unlearnable during the fine-tuning process.
Outcome:	The proposed framework makes undesired behaviors unlearnable during the fine-tuning process while preserving the ability to learn other information.

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Ming Jiang, Qiuyuan Huang, Lei Zhang, Xin Wang, Pengchuan Zhang, Zhe Gan, Jana Diesner, Jianfeng Gao

Towards Identifying Social Bias in Dialog Systems: Framework, Dataset, and Benchmark (2022.findings-emnlp)

Challenge:	Existing metrics based on text-level comparisons fail to assess the quality of captions produced by machines.
Approach:	They propose to use a machine-learned text-image grounding model to measure the accuracy of machine-generated captions and their correlation with human judgments.
Outcome:	The proposed metric has higher consistency with human judgments and is more accurate than existing metrics.

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Jingyan Zhou, Jiawen Deng, Fei Mi, Yitong Li, Yasheng Wang, Minlie Huang, Xin Jiang, Qun Liu, Helen Meng

Dialogue Systems & Conversational Agents

Challenge:	a number of safety concerns hinder the deployment of open-domain dialog systems, such as offensive languages and toxic behaviors, such social bias is difficult to detect.
Approach:	They propose a Dial-Bias Framework for analyzing social bias in conversations . they introduce a Chinese social bias dialog dataset and conduct in-depth ablation studies .
Outcome:	The proposed framework is the first annotated Chinese social bias dialog dataset . the proposed framework also provides a fine-grained dialog bias measurement benchmark .

SAGE: Synergistic Adaptive Gating of Experts for Hateful Video Detection (2026.acl-long)

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Jie Huang, Xin Liao, Junjie Wang, Mingyang Li, Wenshuo Wang, Ziyou Jiang, Shoubin Li, Qing Wang

Visual Data in NLP

Challenge:	Existing methods for hateful video detection rely on multimodal feature fusion . existing methods rely only on blind feature mixing, which leads to feature dilution .
Approach:	They propose a framework that shifts from blind feature mixing to decision-level arbitration . it instantiates disentangled experts to rigorously preserve modality-specific semantics .
Outcome:	The proposed framework outperforms state-of-the-art methods on HateMM and MultiHateClip benchmarks.

Towards Building More Robust NER datasets: An Empirical Study on NER Dataset Bias from a Dataset Difficulty View (2023.emnlp-main)

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Ruotian Ma, Xiaolei Wang, Xin Zhou, Qi Zhang, Xuanjing Huang

Robustness in NLP

Challenge:	Named Entity Recognition (NER) models rely on superficial entity patterns for predictions, without considering evidence from the context.
Approach:	They propose to de-bias NER datasets by altering entity-context distribution . they also validate the feasibility of the proposed de-bianking techniques .
Outcome:	The proposed methods can be applied to different models and improve existing models.

Understanding LLMs’ Cross-Lingual Context Retrieval: How Good It Is And Where It Comes From (2025.emnlp-main)

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Changjiang Gao, Hankun Lin, Xin Huang, Xue Han, Junlan Feng, Chao Deng, Jiajun Chen, Shujian Huang

Cross-Lingual Transfer

Challenge:	Cross-lingual context retrieval is a fundamental aspect of cross-lingual alignment, but the performance and mechanism of it for large language models (LLMs) remains unclear.
Approach:	They evaluate cross-lingual context retrieval of over 40 large language models . they use cross-linguistic machine reading comprehension as a representative scenario .
Outcome:	The results show that open LLMs show strong cross-lingual context retrieval ability . the results also show that their oracle performances improve after training .

Not All Metrics Are Guilty: Improving NLG Evaluation by Diversifying References (2024.naacl-long)

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Tianyi Tang, Hongyuan Lu, Yuchen Jiang, Haoyang Huang, Dongdong Zhang, Xin Zhao, Tom Kocmi, Furu Wei

Text Generation

Challenge:	Existing evaluation benchmarks with limited references may not accurately reflect the quality of the model’s hypotheses.
Approach:	They propose a method to enrich evaluation benchmarks by diversifying the expression of a single reference into multiple high-quality ones to cover the semantic space of the reference sentence as much as possible.
Outcome:	The proposed method can enhance evaluation benchmarks by diversifying the expression of reference into multiple high-quality ones to cover the semantic space of the reference sentence as much as possible.

LogicPoison: Logical Attacks on Graph Retrieval-Augmented Generation (2026.acl-long)

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Yilin Xiao, Jin Chen, Qinggang Zhang, Yujing Zhang, Chuang Zhou, Longhao Yang, Lingfei Ren, Xin Yang, Xiao Huang

Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models (2026.acl-long)

Challenge:	Graph-based Retrieval-Augmented Generation (GraphRAG) enhances the reasoning capabilities of Large Language Models (LLMs) however, traditional RAG attacks are difficult to pose an effective threat to GraphRAg systems.
Approach:	They propose a novel attack framework that targets logical reasoning rather than injecting false contents into GraphRAG systems by grounding their responses in structured knowledge graphs.
Outcome:	The proposed framework outperforms state-of-the-art attacks on GraphRAG systems in both effectiveness and stealth.

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Xin Cheng, Wangding Zeng, Damai Dai, Qinyu Chen, Bingxuan Wang, Zhenda Xie, Kezhao Huang, Xingkai Yu, Zhewen Hao, Han Zhang, Yu-Kun Li, Huishuai Zhang, Dongyan Zhao, Wenfeng Liang

MNER-MI: A Multi-image Dataset for Multimodal Named Entity Recognition in Social Media (2024.lrec-main)

Challenge:	Mixture-of-Experts (MoE) scales capacity via conditional computation, but lacks knowledge lookup primitive.
Approach:	They propose a conditional memory instantiated via Deep Sparse Embedding (DSE) they propose 'u-shaped scaling law' that identifies optimal balance between MoE experts and DSE memory .
Outcome:	The proposed model outperforms an iso-parameter and isoFLOPs MoE baseline across knowledge and reasoning benchmarks and is infrastructure-efficient.

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Shizhou Huang, Bo Xu, Changqun Li, Jiabo Ye, Xin Lin

Label-Specific Document Representation for Multi-Label Text Classification (D19-1)

Challenge:	Recent research has focused on multimodal named entity recognition (MNER) but current approaches focus on text and a single accompanying image, leaving a significant research gap in multi-image scenarios.
Approach:	They propose to construct a human-annotated MNER dataset with multiple images called MNER-MI and a temporal prompt model with multiple image to address the new challenges in multi-image scenarios.
Outcome:	The proposed method achieves state-of-the-art results on both MNER-MI and MNER -MI-Plus, demonstrating its effectiveness.

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Lin Xiao, Xin Huang, Boli Chen, Liping Jing

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

Challenge:	Existing methods to classify documents using labels only assign one label to document . multi-label text classification is a challenging task because of the huge amount of documents, words and labels.
Approach:	They propose a Label-Specific Attention Network (LSAN) to learn a label-specific document representation.
Outcome:	The proposed model outperforms state-of-the-art methods on four datasets . it can predict low-frequency labels, and it can be used in sentimental analysis .

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

Planning, Creation, Usage: Benchmarking LLMs for Comprehensive Tool Utilization in Real-World Complex Scenarios (2024.findings-acl)

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

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Shijue Huang, Wanjun Zhong, Jianqiao Lu, Qi Zhu, Jiahui Gao, Weiwen Liu, Yutai Hou, Xingshan Zeng, Yasheng Wang, Lifeng Shang, Xin Jiang, Ruifeng Xu, Qun Liu

WaveCoder: Widespread And Versatile Enhancement For Code Large Language Models By Instruction Tuning (2024.acl-long)

Challenge:	Existing benchmarks focus on simple synthesized queries that do not reflect real-world complexity, thereby offering limited perspectives in evaluating tool utilization.
Approach:	They propose a benchmark to evaluate LLMs’ ability in tool utilization within real-world scenarios.
Outcome:	The proposed benchmark improves LLMs’ ability in tool utilization within real-world scenarios and eliminates the restriction of pre-defined toolset.

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Zhaojian Yu, Xin Zhang, Ning Shang, Yangyu Huang, Can Xu, Yishujie Zhao, Wenxiang Hu, Qiufeng Yin

Template-free Prompt Tuning for Few-shot NER (2022.naacl-main)

Challenge:	Recent work shows that Code Large Language Models can address a wide range of code-related tasks.
Approach:	They propose a method to generate widespread and versatile instruction data from open source code datasets and use it to train code-related models.
Outcome:	The proposed model outperforms open-source models in generalization ability across code-related tasks.

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Ruotian Ma, Xin Zhou, Tao Gui, Yiding Tan, Linyang Li, Qi Zhang, Xuanjing Huang

FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models (2025.naacl-long)

Challenge:	Prompt-based methods have been successfully applied in few-shot learning tasks . however, when applied to token-level labeling tasks, it would be time-consuming to enumerate the template queries over all potential entity spans.
Approach:	They propose a method to reformulate NER tasks as LM problems without templates.
Outcome:	The proposed method is 30.12 times faster than the template-based method under few-shot settings.

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Xin Guo, Haotian Xia, Zhaowei Liu, Hanyang Cao, Zhi Yang, Zhiqiang Liu, Sizhe Wang, Jinyi Niu, Chuqi Wang, Yanhui Wang, Xiaolong Liang, Xiaoming Huang, Bing Zhu, Zhongyu Wei, Yun Chen, Weining Shen, Liwen Zhang

Intention Knowledge Graph Construction for User Intention Relation Modeling (2026.eacl-long)

Challenge:	Large language models have demonstrated outstanding performance in various natural language processing tasks, but their security capabilities in the financial domain have not been explored.
Approach:	They propose to use a benchmark to evaluate large language models' financial domain knowledge and practical abilities.
Outcome:	The proposed benchmark evaluates large language models' financial domain knowledge and practical abilities.

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Jiaxin Bai, Zhaobo Wang, Junfei Cheng, Dan Yu, Zerui Huang, Weiqi Wang, Xin Liu, Chen Luo, Yanming Zhu, Bo Li, Yangqiu Song

On Construction of the ASR-oriented Indian English Pronunciation Dictionary (2020.lrec-1)

Challenge:	Existing knowledge graphs focus on connecting intentions but lacks the ability to model the relationships between different intentions.
Approach:	They propose a framework to automatically generate an intention knowledge graph, capturing connections between user intentions.
Outcome:	The proposed model outperforms state-of-the-art methods and shows its utility.

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Xian Huang, Xin Jin, Qike Li, Keliang Zhang

Speech & Audio in NLP

Challenge:	Indian English (IE) has distinctive characteristics, especially phonologically, from other varieties of English.
Approach:	They build a small IE spontaneous speech corpus and use a linguistically-guided IE pronunciation dictionary to apply it to IE.
Outcome:	The proposed system performs better on IE spontaneous speech data than the one trained with CMUdict.

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

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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.

CoCGAN: Contrastive Learning for Adversarial Category Text Generation (2022.coling-1)

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Xin Sheng, Linli Xu, Yinlong Xu, Changcun Bao, Huang Chen, Bo Ren

Pyramid-BERT: Reducing Complexity via Successive Core-set based Token Selection (2022.acl-long)

Challenge:	Experimental results on synthetic and real category text generation datasets demonstrate that CoCGAN can achieve significant improvements over the baseline category text generators.
Approach:	They propose to incorporate contrastive learning into adversarial category text generation by using a discriminator to optimize a contrastive learn objective to capture more flexible data-to-class relations and data- to-data relations among training samples.
Outcome:	The proposed model improves on synthetic and real category text generation datasets.

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Xin Huang, Ashish Khetan, Rene Bidart, Zohar Karnin

Leveraging High-Resource English Corpora for Cross-lingual Domain Adaptation in Low-Resource Japanese Medicine via Continued Pre-training (2025.findings-emnlp)

Challenge:	Existing models that use heuristics to shorten sequence lengths are computationally prohibitive.
Approach:	They propose a new method to shorten sequence lengths by transforming tokens through encoders and a core-set based token selection method that avoids expensive pre-training and fine tuning.
Outcome:	The proposed model outperforms existing models on GLUE benchmarks and Long Range Arena datasets and demonstrates that it is cost-effective and space-efficient.

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Kazuma Kobayashi, Zhen Wan, Fei Cheng, Yuma Tsuta, Xin Zhao, Junfeng Jiang, Jiahao Huang, Zhiyi Huang, Yusuke Oda, Rio Yokota, Yuki Arase, Daisuke Kawahara, Akiko Aizawa, Sadao Kurohashi

SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural Reasoning (2024.naacl-long)

Challenge:	low-resource language corpora in professional domains like medicine hinder cross-lingual domain adaptation of pre-trained large language models.
Approach:	They examine how linguistic features affect performance on a Japanese–English medical knowledge benchmark.
Outcome:	The proposed model can leverage English-language resources in medical domains while ensuring sufficient coverage of language-specific expressions in a target language.

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Bin Wang, Zhengyuan Liu, Xin Huang, Fangkai Jiao, Yang Ding, AiTi Aw, Nancy Chen

MGCL: Multi-Granularity Clue Learning for Emotion-Cause Pair Extraction via Cross-Grained Knowledge Distillation (2024.findings-emnlp)

Challenge:	a new benchmark for multilingual foundation models is being developed . brittleness of foundation models in the dimensions of semantics and multilinguality is a key limitation .
Approach:	They propose a benchmark for multilingual foundation models, SeaEval . they examine how well these models comprehend cultural practices, nuances, and values .
Outcome:	The proposed model can be used to evaluate multilingual and multicultural scenarios.

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Yang Yu, Xin Lin, Changqun Li, Shizhou Huang, Liang He

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

Challenge:	Traditional methods often rely on coarse-grained clause-level annotations, which overlook valuable fine-grain clues.
Approach:	They propose a method that captures fine-grained clues from a weakly-supervised perspective efficiently by using a teacher model to give sub-clause clues without needing fine-grain annotations.
Outcome:	The proposed method achieves state-of-the-art performance while offering improved interpretability.

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

Robustness in NLP

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

Process-based Self-Rewarding Language Models (2025.findings-acl)

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Shimao Zhang, Xiao Liu, Xin Zhang, Junxiao Liu, Zheheng Luo, Shujian Huang, Yeyun Gong

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

Challenge:	Existing methods to reward LLMs' outputs are not effective in mathematical reasoning scenarios and may lead to a decline in performance.
Approach:	They propose a process-based self-rewarding pipeline that integrates long-thought reasoning, step-wise LLM-as-a-Judge, and step- wise preference optimization within the existing paradigm.
Outcome:	The proposed model improves the performance of Large Language Models on multiple mathematical reasoning benchmarks and shows that it can surpass human capabilities.

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

Speech & Audio in NLP

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

Query Enhanced Knowledge-Intensive Conversation via Unsupervised Joint Modeling (2023.acl-long)

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Mingzhu Cai, Siqi Bao, Xin Tian, Huang He, Fan Wang, Hua Wu

DAPE V2: Process Attention Score as Feature Map for Length Extrapolation (2025.acl-long)

Challenge:	Existing methods to retrieve knowledge-intensive conversations are based on external resources such as Wikipedia databases or search engine results.
Approach:	They propose an unsupervised query enhanced approach for knowledge-intensive conversations . they conduct experiments on three knowledge- intensive conversation datasets .
Outcome:	The proposed approach performs better than all unsupervised methods across three datasets and achieves competitive performance compared to supervised methods.

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Chuanyang Zheng, Yihang Gao, Han Shi, Jing Xiong, Jiankai Sun, Jingyao Li, Minbin Huang, Xiaozhe Ren, Michael Ng, Xin Jiang, Zhenguo Li, Yu Li

SecureVibeBench: Benchmarking Secure Vibe Coding of AI Agents via Reconstructing Vulnerability-Introducing Scenarios (2026.acl-long)

Challenge:	Extensive experiments demonstrate that treating attention as a feature map and applying convolution as . a processing method significantly enhances Transformer performance.
Approach:	They propose to use the convolution operator to mimic the processing methods in computer vision to treat attention as a feature map and apply it to neighboring attention scores across different heads.
Outcome:	The proposed model can be adapted to various attention-related models and achieves high performance.

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Junkai Chen, Huihui Huang, Yunbo Lyu, Junwen An, Jieke Shi, Chengran Yang, Ting Zhang, Haoye Tian, Yikun Li, Zhenhao Li, Xin Zhou, Xing Hu, David Lo

Challenge:	Existing benchmarks fail to capture scenarios in which vulnerabilities are introduced by humans . we evaluate 5 popular code agents supported by 5 LLMs on SecureVibeBench .
Approach:	They propose a benchmarking tool that compares 105 C/C++ secure coding tasks . they use real-world open-source vulnerabilities and a comprehensive evaluation tool .
Outcome:	The proposed benchmarks show that code agents struggle to produce correct and secure code . the best performing agent produces merely 23.8% correct and secured solutions .

TextObfuscator: Making Pre-trained Language Model a Privacy Protector via Obfuscating Word Representations (2023.findings-acl)

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Xin Zhou, Yi Lu, Ruotian Ma, Tao Gui, Yuran Wang, Yong Ding, Yibo Zhang, Qi Zhang, Xuanjing Huang

Parrot: Enhancing Multi-Turn Instruction Following for Large Language Models (2024.acl-long)

Challenge:	Existing inference services are plagued by privacy concerns, such as sharing sensitive data with service providers.
Approach:	They propose a framework for protecting inference privacy by applying random perturbations to clustered representations.
Outcome:	The proposed framework protects inference privacy by applying random perturbations to clustered representations.

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Yuchong Sun, Che Liu, Kun Zhou, Jinwen Huang, Ruihua Song, Xin Zhao, Fuzheng Zhang, Di Zhang, Kun Gai