Papers by Jie Huang

Mirroring Users: Towards Building Preference-aligned User Simulator with User Feedback in Recommendation (2026.acl-long)

Challenge:	Pre-trained language models (PLMs) are prone to leaking personal information due to memorization, but the risk of specific personal information being extracted by attackers is low.
Approach:	They analyze whether large pre-trained language models are prone to leaking personal information due to memorization.
Outcome:	The proposed model is weak at association, so the risk of specific personal information being extracted by attackers is low.

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Tianjun Wei, Huizhong Guo, Yingpeng Du, Zhu Sun, Huang Chen, Dongxia Wang, Jie Zhang

Green & Sustainable NLP

Challenge:	Large Language Models lack specific task alignment and large-scale simulations are challenging due to their ambiguity, noise and massive volume.
Approach:	They propose a framework that leverages user feedback in RSs with advanced LLM capabilities to generate high-quality simulation data.
Outcome:	The proposed framework boosts the alignment with human preferences and in-domain reasoning capabilities of the fine-tuned LLMs.

LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing (2024.emnlp-main)

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Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing, Cheng Jiayang, Zhaowei Wang, Ying Su, Raj Shah, Ruohao Guo, Jing Gu, Haoran Li, Kangda Wei, Zihao Wang, Lu Cheng, Surangika Ranathunga, Meng Fang, Jie Fu, Fei Liu, Ruihong Huang, Eduardo Blanco, Yixin Cao, Rui Zhang, Philip Yu, Wenpeng Yin

Challenge:	a comparative analysis of paper (meta-)reviews by large language models (LLMs) aims to identify and distinguish LLMs from human activities .
Approach:	They present a comparative analysis to identify and distinguish LLM activities from human activities.
Outcome:	The proposed analysis aims to improve recognition of instances when someone implicitly uses LLMs for reviewing activities.

PopAlign: Diversifying Contrasting Patterns for a More Comprehensive Alignment (2025.acl-long)

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Zekun Moore Wang, Shenzhi Wang, King Zhu, Jiaheng Liu, Ke Xu, Jie Fu, Wangchunshu Zhou, Wenhao Huang

Challenge:	Typical approaches to training large language models rely on limited contrasting patterns . contrasting data is limited and models are susceptible to harmful response tendencies .
Approach:	They propose a framework that integrates contrasting patterns across the prompt, model, and pipeline levels.
Outcome:	The proposed framework outperforms existing methods in the comparison of RQ1 and RQ2 . the proposed framework significantly outperformed existing methods, leading to more comprehensive alignment.

Causal Intervention Improves Implicit Sentiment Analysis (2022.coling-1)

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Siyin Wang, Jie Zhou, Changzhi Sun, Junjie Ye, Tao Gui, Qi Zhang, Xuanjing Huang

Sentiment Analysis

Challenge:	Existing neural models struggle with implicit sentiment analysis because they latch onto spurious correlations, resulting in poor generalization and robustness.
Approach:	They propose a CausaL intervention model for implicit sEntiment ANalysis using instrumental variable to eliminate confounding causal effects and extract the pure causal effect between sentence and sentiment.
Outcome:	The proposed model extracts the pure causal effect between sentence and sentiment using instrumental variable.

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

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

Ask To The Point: Open-Domain Entity-Centric Question Generation (2023.findings-emnlp)

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

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Yuxiang Liu, Jie Huang, Kevin Chang

Question Generation

Challenge:	*entity-centric question generation (ECQG) is a task motivated by real-world applications such as topic-specific learning, assisted reading, and fact-checking.
Approach:	They propose a PLM-based framework GenCONE with two modules: content focusing and question verification.
Outcome:	The proposed framework outperforms baselines and is effective and complementary in generating high-quality questions.

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

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

Descriptive Knowledge Graph in Biomedical Domain (2023.emnlp-demo)

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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Kerui Zhu, Jie Huang, Kevin Chen-Chuan Chang

Information Retrieval

Challenge:	Existing systems that retrieve unconnected passages do not provide efficient search for relational knowledge.
Approach:	They propose a system that automatically extracts and generates informative and descriptive sentences from the biomedical corpus and facilitates efficient search for relational knowledge.
Outcome:	The proposed system extracts and generates informative and descriptive sentences from the biomedical corpus and facilitates the efficient search for relational knowledge.

PositionID: LLMs can Control Lengths, Copy and Paste with Explicit Positional Awareness (2024.findings-emnlp)

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Noah Wang, Feiyu Duan, Yibo Zhang, Wangchunshu Zhou, Ke Xu, Wenhao Huang, Jie Fu

Learning by Analogy: Diverse Questions Generation in Math Word Problem (2023.findings-acl)

Challenge:	Large Language Models (LLMs) have impressive capabilities across various domains, including role-playing, creative writing, mathematical reasoning, and coding.
Approach:	They propose two methods to improve the model’s adherence to length constraints and copy-paste accuracy without compromising response quality.
Outcome:	The proposed methods improve the model’s adherence to length constraints and copy-paste accuracy without compromising response quality.

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Zihao Zhou, Maizhen Ning, Qiufeng Wang, Jie Yao, Wei Wang, Xiaowei Huang, Kaizhu Huang

In-the-wild Audio Spatialization with Flexible Text-guided Localization (2025.acl-long)

Challenge:	Existing methods for solving math word problem (MWP) use shortcut learning to train solvers based on samples with a single question.
Approach:	They propose to generate diverse yet consistent questions from a common scenario . they then feed the equations to a question generator to obtain the diverse questions . their method leads to performance improvement on the current benchmark Math23K .
Outcome:	The proposed method generates diverse yet consistent questions with a variety of equations and questions . it improves on the current benchmark, which is based on the proposed method .

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Tianrui Pan, Jie Liu, Zewen Huang, Jie Tang, Gangshan Wu

Governance in Motion: Co-evolution of Constitutions and AI models for Scalable Safety (2025.emnlp-main)

Challenge:	Existing methods for mapping monaural audio to binaural signals lack flexibility and interactive control needed in complex multi-object user-interactive environments.
Approach:	They propose a text-guided audio spatialization framework that utilizes diverse text prompts to evaluate binaural audio models.
Outcome:	The proposed framework learns binaural differences guided by 3D spatial location and relative position prompts, enhanced with flipped-channel audio.

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Chenhao Huang, Ziyu Shen, Yicong Ren, Huiyuan Zheng, Jiazheng Zhang, Mingxu Chai, Ming Zhang, Shihan Dou, Fan Mo, Jie Shi, Tao Gui, Qi Zhang, Xuanjing Huang

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

Challenge:	Existing approaches to align large language models with human preferences lack flexibility . static alignment preferences lack the ability to correct misaligned behaviors as they emerge .
Approach:	They propose a framework that enables dynamic and continuous alignment of large language models with human preferences.
Outcome:	The proposed framework improves safety and accuracy of a 7B model with human annotations.

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

Citation: A Key to Building Responsible and Accountable Large Language Models (2024.findings-naacl)

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

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Jie Huang, Kevin Chang

Data Efficient RLVR via Off-Policy Influence Guidance (2026.acl-long)

Challenge:	Large Language Models (LLMs) bring transformative benefits alongside unique challenges, including intellectual property (IP) and ethical concerns.
Approach:	They propose a new approach to mitigate intellectual property and ethical risks associated with large language models.
Outcome:	The proposed approach could enhance content transparency and verifiability . it should account for both non-parametric and parametric content .

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Erle Zhu, Dazhi Jiang, Yuan Wang, Xujun Li, Jiale Cheng, Yuxian Gu, Yilin Niu, Aohan Zeng, Jie Tang, Minlie Huang, Hongning Wang

Green & Sustainable NLP

Challenge:	Existing data selection methods for RLVR are heuristic-based, lacking theoretical guarantees and generalizability.
Approach:	They propose an off-policy influence estimation method that approximates data influence using offline trajectories.
Outcome:	The proposed method reduces the computational cost of policy rollouts and improves storage and computation efficiency.

A Confidence-based Partial Label Learning Model for Crowd-Annotated Named Entity Recognition (2023.findings-acl)

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Limao Xiong, Jie Zhou, Qunxi Zhu, Xiao Wang, Yuanbin Wu, Qi Zhang, Tao Gui, Xuanjing Huang, Jin Ma, Ying Shan

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

Challenge:	Existing models for named entity recognition (NER) are based on large-scale labeled datasets, which always obtain using crowdsourcing.
Approach:	They propose a CONfidence-based partial Label Learning method to integrate prior and posterior confidences for crowd-annotated named entity recognition models.
Outcome:	The proposed model improves on real-world and synthetic datasets compared with baselines.

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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

A Multi-Format Transfer Learning Model for Event Argument Extraction via Variational Information Bottleneck (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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Jie Zhou, Qi Zhang, Qin Chen, Qi Zhang, Liang He, Xuanjing Huang

Nishant Balepur, Jie Huang, Kevin Chang

Challenge:	Event argument extraction (EAE) aims to extract arguments with given roles from texts.
Approach:	They propose a multi-format transfer learning model with variational information bottleneck to learn from existing datasets.
Outcome:	The proposed model improves on three benchmark datasets and obtains state-of-the-art performance on EAE.

Text Fact Transfer (2023.emnlp-main)

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Text Generation

Challenge:	Existing text style transfer models struggle with text fact transfer due to their inability to preserve the specificity and phrasing of the source text and tendency to hallucinate errors.
Approach:	They propose a task that seeks to transfer factual content between topics without changing its style.
Outcome:	The proposed framework can transfer factual content without sacrificing style without changing the style of the source text.

Expository Text Generation: Imitate, Retrieve, Paraphrase (2023.emnlp-main)

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Nishant Balepur, Jie Huang, Kevin Chang

Text Generation

Challenge:	Expository documents are vital resources for conveying complex information to readers.
Approach:	They propose a task to generate an accurate and stylistically consistent expository text by intelligently searching a knowledge source.
Outcome:	The proposed framework overcomes the limitations of retrieval-augmented models and produces factual and organized expository texts that accurately inform readers.

Domain Generalization via Causal Adjustment for Cross-Domain Sentiment Analysis (2024.lrec-main)

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Siyin Wang, Jie Zhou, Qin Chen, Qi Zhang, Tao Gui, Xuanjing Huang

Sentiment Analysis

Challenge:	Existing approaches to domain adaptation fail to generalize well on unknown test data.
Approach:	They propose a backdoor adjustment-based causal model to disentangle domain-specific and domain-invariant representations that play essential roles in tackling domain shift.
Outcome:	The proposed model disentangles domain-specific and domain-invariant representations that play essential roles in tackling domain shift.

AutoDetect: Towards a Unified Framework for Automated Weakness Detection in Large Language Models (2024.findings-emnlp)

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Jiale Cheng, Yida Lu, Xiaotao Gu, Pei Ke, Xiao Liu, Yuxiao Dong, Hongning Wang, Jie Tang, Minlie Huang

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

Challenge:	Large Language Models (LLMs) exhibit significant but subtle weaknesses, such as mistakes in instruction-following or coding tasks.
Approach:	They propose a framework to automatically expose weaknesses in Large Language Models (LLMs) they use three LLM-powered agents to perform comprehensive weakness identification .
Outcome:	The proposed framework shows that it is more effective than untargeted data augmentation methods like Self-Instruct to identify weaknesses in LLMs.

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

CTRLEval: An Unsupervised Reference-Free Metric for Evaluating Controlled Text Generation (2022.acl-long)

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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Pei Ke, Hao Zhou, Yankai Lin, Peng Li, Jie Zhou, Xiaoyan Zhu, Minlie Huang

Low-Resource NLP

Challenge:	Existing reference-free metrics have obvious limitations for evaluating controlled text generation models.
Approach:	They propose an unsupervised reference-free metric which evaluates controlled text generation from different aspects by formulating each aspect into multiple text infilling tasks.
Outcome:	The proposed metric has higher correlations with human judgments while obtaining better generalization of evaluating generated texts from different models and with different qualities.

APB: Accelerating Distributed Long-Context Inference by Passing Compressed Context Blocks across GPUs (2025.acl-long)

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Yuxiang Huang, Mingye Li, Xu Han, Chaojun Xiao, Weilin Zhao, Sun Ao, Hao Zhou, Jie Zhou, Zhiyuan Liu, Maosong Sun

Multi-Granularity Semantic Revision for Large Language Model Distillation (2026.acl-long)

Challenge:	Long-context inference is crucial for advancing large language models, but its prefill speed remains a bottleneck.
Approach:	They propose an efficient long-context inference framework that leverages multi-host approximate attention to enhance prefill speed.
Outcome:	The proposed framework achieves speedups of 9.2, 4.2, and 1.6 without any degradation in performance.

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Xiaoyu Liu, Yun Zhang, Wei Li, Simiao Li, Xudong Huang, Hanting Chen, Yehui Tang, Jie Hu, Zhiwei Xiong, Yunhe Wang

[MASK] Insertion: a robust method for anti-adversarial attacks (2023.findings-eacl)

Challenge:	Existing methods for generating large language models rely on student-generated outputs, which introduce generation errors and misguide the distillation process.
Approach:	They propose a multi-granularity semantic revision method for LLM distillation that corrects errors using teacher-generated tokens and re-generates the sequence to minimize errors.
Outcome:	The proposed method reduces errors and misguides distillation on student models and improves consistency between teacher and student outputs.

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Xinrong Hu, Ce Xu, Junlong Ma, Zijian Huang, Jie Yang, Yi Guo, Johan Barthelemy

An Operation Network for Abstractive Sentence Compression (C18-1)

Challenge:	Existing studies have focused on adversarial defenses against pretrained language models.
Approach:	They propose an adversarial defensing algorithm that inserts tokens into input sequences . they show an improvement in accuracy between 3.2 and 11.1 absolute points .
Outcome:	The proposed algorithm improves model accuracy on clean and polluted inputs compared with state-of-the-art models .

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Naitong Yu, Jie Zhang, Minlie Huang, Xiaoyan Zhu

Challenge:	Sentence compression is a natural language generation task that condenses a sentence . Delete-based models remove unimportant words from the source sentence and generate a shorter sentence if the source is not a word deletion problem.
Approach:	They propose a neural network approach for abstractive sentence compression . they model the sentence compression process as an editing procedure .
Outcome:	The proposed approach outperforms state-of-the-art models in the abstractive sentence compression field.

ZoomRAG: Hierarchical Random-walk Zooming across Multi-scale Information Graphs for Fast and Accurate RAG (2026.findings-acl)

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Xianming Hu, Jingyang Chen, Bin Tang, Yihe Liu, Yihong Huang, Hongbo Zhao, Nuoyi Chen, Jie Zhang, Ping Li, Kai Zhang

Structured Data in NLP

Challenge:	retrieval-augmented generation (RAG) is a powerful tool for NLP applications . but it is challenging to encode large knowledge bases as compact offline structures .
Approach:	They propose a coarse-to-fine hierarchical graph inference method that uses random walks to retrieve information from a corpus of documents.
Outcome:	The proposed method reduces offline indexing costs and accelerates retrieval.

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

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

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

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

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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

DEER: Descriptive Knowledge Graph for Explaining Entity Relationships (2022.emnlp-main)

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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Jie Huang, Kerui Zhu, Kevin Chen-Chuan Chang, Jinjun Xiong, Wen-mei Hwu

ExpanRL: Hierarchical Reinforcement Learning for Course Concept Expansion in MOOCs (2020.aacl-main)

Challenge:	Existing knowledge graphs lack two desired features for modeling entity relationships: openness and informativeness.
Approach:	They propose a self-supervised learning method to extract relation descriptions with the analysis of dependency patterns and generate relation descriptions using a transformer-based relation description synthesizing model.
Outcome:	The proposed system extracts and generates high-quality relation descriptions without human labeling.

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Jifan Yu, Chenyu Wang, Gan Luo, Lei Hou, Juanzi Li, Jie Tang, Minlie Huang, Zhiyuan Liu

Challenge:	Existing methods for concept expansion in MOOCs are inefficient because of the diversity of MOOC courses and rapid updates.
Approach:	They propose an end-to-end hierarchical reinforcement learning (HRL) model for concept expansion in MOOCs that employs a two-level mechanism of seed selection and concept expansion.
Outcome:	The proposed model improves on nine real MOOC datasets and maintains competitive performance under different settings.

Reconciliation of Pre-trained Models and Prototypical Neural Networks in Few-shot Named Entity Recognition (2022.findings-emnlp)

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Youcheng Huang, Wenqiang Lei, Jie Fu, Jiancheng Lv

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

Challenge:	Existing methods do not consider that pre-trained models contain a prominently large amount of information regarding word frequencies, thus biasing prototypical neural networks against learning word entities.
Approach:	They propose a one-line-code normalization method to reconcile such a mismatch with empirical and theoretical grounds and propose 'references' for the model enhancement.
Outcome:	The proposed method outperforms the state-of-the-art models on nine benchmark datasets and is comparable to the state of the art.

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

Glyph: Scaling Context Windows via Visual-Text Compression (2026.acl-long)

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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Jiale Cheng, Yusen Liu, Xinyu Zhang, Yulin Fei, Wenyi Hong, Ruiliang Lyu, Weihan Wang, Zhe Su, Xiaotao Gu, Xiao Liu, Yushi Bai, Jie Tang, Hongning Wang, Minlie Huang

Understanding Jargon: Combining Extraction and Generation for Definition Modeling (2022.emnlp-main)

Challenge:	Large language models (LLMs) traditionally represent text as sequences of discrete tokens . a long-context scaling problem requires processing more tokens more efficiently .
Approach:	They propose a framework that renders long texts into compact visual pages and processes them with a vision-language model.
Outcome:	The proposed framework renders long texts into compact visual pages and processes them with a vision-language model.

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Jie Huang, Hanyin Shao, Kevin Chen-Chuan Chang, Jinjun Xiong, Wen-mei Hwu

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

Challenge:	Existing methods for generating definitions of words/phrases perform poorly on jargon.
Approach:	They propose to combine extraction and generation for jargon definition modeling by extracting definitional information from the Web and incorporating extracted definitional data.
Outcome:	The proposed method outperforms state-of-the-art models significantly on jargon definitions.

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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.

CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models (2024.findings-acl)

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Yizhi Li, Ge Zhang, Xingwei Qu, Jiali Li, Zhaoqun Li, Noah Wang, Hao Li, Ruibin Yuan, Yinghao Ma, Kai Zhang, Wangchunshu Zhou, Yiming Liang, Lei Zhang, Lei Ma, Jiajun Zhang, Zuowen Li, Wenhao Huang, Chenghua Lin, Jie Fu

LogicGame: Benchmarking Rule-Based Reasoning Abilities of Large Language Models (2025.findings-acl)

Challenge:	a recent study shows that large language models have limited generalization in low-resource languages like Chinese.
Approach:	They propose to evaluate the zero-shot generalizability of large language models to the Chinese language . they release only half of the dataset publicly, with the remainder kept private .
Outcome:	The Chinese Instruction-Following Benchmark evaluates the generalizability of LLMs to the Chinese language.

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Jiayi Gui, Yiming Liu, Jiale Cheng, Xiaotao Gu, Xiao Liu, Hongning Wang, Yuxiao Dong, Jie Tang, Minlie Huang

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

Challenge:	Large Language Models (LLMs) have demonstrated notable capabilities across various tasks, showcasing complex problem-solving abilities.
Approach:	They propose a benchmark to evaluate the rule-based logical reasoning capabilities of Large Language Models (LLMs) they create simulated scenarios in which models execute or plan operations to achieve specific outcomes.
Outcome:	The proposed benchmark evaluates the performance of large language models on a variety of scenarios with varying difficulty levels.

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

Green & Sustainable NLP

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

U-CORE: A Unified Deep Cluster-wise Contrastive Framework for Open Relation Extraction (2023.tacl-1)

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Jie Zhou, Shenpo Dong, Yunxin Huang, Meihan Wu, Haili Li, Jingnan Wang, Hongkui Tu, Xiaodong Wang

Quantifying Association Capabilities of Large Language Models and Its Implications on Privacy Leakage (2024.findings-eacl)

Challenge:	Existing methods for Relation Extraction (RE) are limited due to the overlap between predefined and undefined relations.
Approach:	They propose a unified framework for both Zero-shot and Unsupervised Relation Extraction tasks by leveraging techniques from Contrastive Learning and Clustering.
Outcome:	The proposed framework improves on three well-known datasets showing an average improvement of 7.35% ARI on Zero-shot ORE tasks and 15.24% ARI for Unsupervised ORE.

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Hanyin Shao, Jie Huang, Shen Zheng, Kevin Chang

Black-Box Prompt Optimization: Aligning Large Language Models without Model Training (2024.acl-long)

Challenge:	a new study examines the association capabilities of large language models . as models scale up, their ability to associate entities/information intensifies . however, there is a performance gap when associating commonsense knowledge versus PII, with the latter showing lower accuracy.
Approach:	They examine the association capabilities of large language models and identify factors that influence their proficiency in associating information.
Outcome:	The proposed models show a performance gap when associating commonsense knowledge versus PII, with the latter showing lower accuracy.

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Jiale Cheng, Xiao Liu, Kehan Zheng, Pei Ke, Hongning Wang, Yuxiao Dong, Jie Tang, Minlie Huang

RethinkingTMSC: An Empirical Study for Target-Oriented Multimodal Sentiment Classification (2023.findings-emnlp)

Challenge:	Large language models are often not well aligned with human intents, which requires additional training.
Approach:	They propose to use Black-Box Prompt Optimization (BPO) to perform alignments on large language models that are not well aligned with human intents.
Outcome:	The proposed model outperforms existing models and is model-agnostic.

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Junjie Ye, Jie Zhou, Junfeng Tian, Rui Wang, Qi Zhang, Tao Gui, Xuanjing Huang

Information Retrieval

Challenge:	Recent studies have shown that current TMSC systems rely on textual information, and the progress in tackling this task has slowed down.
Approach:	They propose to integrate both visual and textual information to improve the performance of TMSC by considering multimodal information.
Outcome:	The proposed model integrates both visual and textual information to improve performance.

FR-Spec: Accelerating Large-Vocabulary Language Models via Frequency-Ranked Speculative Sampling (2025.acl-long)

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Weilin Zhao, Tengyu Pan, Xu Han, Yudi Zhang, Sun Ao, Yuxiang Huang, Kaihuo Zhang, Weilun Zhao, Yuxuan Li, Jie Zhou, Hao Zhou, Jianyong Wang, Maosong Sun, Zhiyuan Liu

MetaASSIST: Robust Dialogue State Tracking with Meta Learning (2022.emnlp-main)

Challenge:	Speculative sampling is an efficient way to accelerate the auto-regressive generation process of large language models.
Approach:	They propose a frequency-ranked speculative sampling framework that optimizes draft candidate selection through vocabulary space compression.
Outcome:	Experiments show that FR-Spec reduces LM Head computation overhead by 75% while ensuring the equivalence of the final output distribution.

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Fanghua Ye, Xi Wang, Jie Huang, Shenghui Li, Samuel Stern, Emine Yilmaz

Robustness in NLP

Challenge:	Existing dialogue datasets contain lots of noise in their state annotations.
Approach:	They propose a framework to train robust dialogue state tracking models by combining pseudo and vanilla labels by a common weighting parameter.
Outcome:	The proposed framework achieves state-of-the-art accuracy of 80.10% on multiWOZ 2.4.

ChatMusician: Understanding and Generating Music Intrinsically with LLM (2024.findings-acl)

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Ruibin Yuan, Hanfeng Lin, Yi Wang, Zeyue Tian, Shangda Wu, Tianhao Shen, Ge Zhang, Yuhang Wu, Cong Liu, Ziya Zhou, Liumeng Xue, Ziyang Ma, Qin Liu, Tianyu Zheng, Yizhi Li, Yinghao Ma, Yiming Liang, Xiaowei Chi, Ruibo Liu, Zili Wang, Chenghua Lin, Qifeng Liu, Tao Jiang, Wenhao Huang, Wenhu Chen, Jie Fu, Emmanouil Benetos, Gus Xia, Roger Dannenberg, Wei Xue, Shiyin Kang, Yike Guo

Towards Reasoning in Large Language Models: A Survey (2023.findings-acl)

Challenge:	Despite LLMs' impressive capabilities in musical knowledge, music reasoning remains an unsolved task.
Approach:	They propose an open-source large language model (LLM) that integrates intrinsic musical abilities into LLaMA2 and GPT-3.5.
Outcome:	The proposed model can understand and generate music with a pure text tokenizer without external multi-modal neural structures or tokenizers.

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Jie Huang, Kevin Chen-Chuan Chang

Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approach (2021.acl-long)

Challenge:	Reasoning is a fundamental aspect of human intelligence that plays a crucial role in many intellectual activities.
Approach:	They propose to improve LLMs' ability to elicit reasoning by providing exemplars or prompts to model reasoning.
Outcome:	This paper provides a comprehensive overview of the state of knowledge on reasoning in large language models.

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Jie Huang, Kevin Chang, JinJun Xiong, Wen-mei Hwu

Unlocking Emergent Modularity in Large Language Models (2024.naacl-long)

Challenge:	Existing methods to measure fine-grained domain relevance are needed for downstream tasks in natural language processing.
Approach:	They propose to measure fine-grained domain relevance, defined as the degree that a term is relevant to a given domain.
Outcome:	The proposed method outperforms baselines and surpasses professional human performance.

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Zihan Qiu, Zeyu Huang, Jie Fu

Re3Dial: Retrieve, Reorganize and Rescale Conversations for Long-Turn Open-Domain Dialogue Pre-training (2023.emnlp-main)

Challenge:	Existing MNNs are explicit, with predefined modular architectures and individual modules expected to implement distinct functions.
Approach:	They propose to unlock emergent modularity in language models by fine-tuning them as Mixture-of-Experts (MoEs) EMoE is robust to various configurations and can scale up to Large Language Models .
Outcome:	The proposed models can be fine-tuned as Mixture-of-Expert (MoE) counterparts without introducing any extra parameters.

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Jiaxin Wen, Hao Zhou, Jian Guan, Jie Zhou, Minlie Huang

Natural Language Interfaces

Challenge:	Existing pre-training models lack long-turn dialogue sessions due to the scarcity of long-term sessions.
Approach:	They propose a framework that can automatically construct billion-scale long-turn dialogues by reorganizing existing short-turn ones.
Outcome:	The proposed framework can automatically construct billion-scale long-turn dialogues by reorganizing existing short-turn ones.

DimonGen: Diversified Generative Commonsense Reasoning for Explaining Concept Relationships (2023.acl-long)

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Chenzhengyi Liu, Jie Huang, Kerui Zhu, Kevin Chen-Chuan Chang

Beyond Literal Mapping: Benchmarking and Improving Non-Literal Translation Evaluation (2026.acl-long)

Challenge:	Existing models that describe concepts in everyday situations are difficult to summarize in a single sentence.
Approach:	They propose DimonGen, which generates sentences describing concept relationships in everyday scenarios.
Outcome:	The proposed model outperforms baseline models in terms of quality and diversity of generated sentences.

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Yanzhi Tian, Cunxiang Wang, Zeming Liu, Heyan Huang, Wenbo Yu, Dawei Song, Jie Tang, Yuhang Guo

Machine Translation

Challenge:	Large Language Models (LLMs) have advanced machine translation (MT) a meta-evaluation dataset focused on non-literal translations is lacking . experimental results show the inaccuracies of traditional MT metrics and the limitations of LLM-as-a-Judge.
Approach:	They propose a meta-evaluation framework that leverages sub-agents to evaluate machine translation metrics.
Outcome:	The proposed framework improves on the knowledge cutoff and score inconsistency problem.

CMDAG: A Chinese Metaphor Dataset with Annotated Grounds as CoT for Boosting Metaphor Generation (2024.lrec-main)

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Yujie Shao, Xinrong Yao, Xingwei Qu, Chenghua Lin, Shi Wang, Wenhao Huang, Ge Zhang, Jie Fu

Challenge:	Metaphors are a prominent linguistic device in human language and literature, as they add color, imagery, and emphasis to enhance effective communication.
Approach:	They propose a large-scale high quality annotated Chinese Metaphor Corpus . they use a set of guidelines to ensure the accuracy and consistency of their annotations .
Outcome:	The proposed corpus generates metaphors that resonate more with real-world intuition.

OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems (2024.acl-long)

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Chaoqun He, Renjie Luo, Yuzhuo Bai, Shengding Hu, Zhen Thai, Junhao Shen, Jinyi Hu, Xu Han, Yujie Huang, Yuxiang Zhang, Jie Liu, Lei Qi, Zhiyuan Liu, Maosong Sun

Think Natively: Unlocking Multilingual Reasoning with Consistency-Enhanced Reinforcement Learning (2026.acl-long)

Challenge:	Large Language Models (LLMs) and Large Multimodal Models have exceeded general human capabilities in various tasks.
Approach:	They present an Olympiad-level bilingual multimodal scientific benchmark featuring 8,476 problems from Olympiad level mathematics and physics competitions.
Outcome:	The best performing model, GPT-4V, attains an average score of 17.97% on OlympiadBench, with a mere 10.74% in physics, highlighting the benchmark rigor and the intricacy of physical reasoning.

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Xue Zhang, Yunlong Liang, Fandong Meng, Songming Zhang, Kaiyu Huang, Yufeng Chen, Xu Jinan, Jie Zhou

VehicleWorld: A Highly Integrated Multi-Device Environment for Intelligent Vehicle Interaction (2025.findings-emnlp)

Challenge:	Current Large Reasoning Models exhibit two critical limitations when processing non-English languages: (1) They struggle to maintain input-output language consistency; (2) They generally perform poorly with wrong reasoning paths and lower answer accuracy compared to English.
Approach:	They propose a language-consistency reward and a cross-lingual thinking alignment reward to improve the model's interpretability and accuracy.
Outcome:	The proposed model achieves nearly 100% language consistency and superior performance on two multilingual benchmarks (MMATH and PolyMath).

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Jie Yang, Jiajun Chen, Zhangyue Yin, Shuo Chen, Yuxin Wang, Yiran Guo, Yuan Li, Yining Zheng, Xuanjing Huang, Xipeng Qiu

Challenge:	Traditional Function Calling (FC) approaches operate statelessly, requiring multiple exploratory calls to build environmental awareness before execution, leading to inefficiency and limited error recovery.
Approach:	They propose a state-based function call approach that maintains explicit system state awareness and implements direct state transitions to achieve target conditions.
Outcome:	The proposed approach outperforms traditional function calling approaches, achieving superior execution accuracy and reduced latency.

Open Relation Modeling: Learning to Define Relations between Entities (2022.findings-acl)

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Jie Huang, Kevin Chang, Jinjun Xiong, Wen-mei Hwu

A Closer Look into Mixture-of-Experts in Large Language Models (2025.findings-naacl)

Challenge:	Existing systems identify related entities but do not provide features for exploring relations between entities.
Approach:	They propose to teach machines to generate definition-like relation descriptions by letting them learn from defining entities.
Outcome:	The proposed model can generate definition-like relation descriptions that capture the representative characteristics of entities.

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Ka Man Lo, Zeyu Huang, Zihan Qiu, Zili Wang, Jie Fu

Exploring Semantic Capacity of Terms (2020.emnlp-main)

Challenge:	Mixture-of-experts (MoE) architectures are gaining increasing attention for their unique properties and remarkable performance.
Approach:	They propose a mixture-of-experts architecture that allows for model scaling without sacrificing computational efficiency.
Outcome:	The proposed model increases model size without sacrificing computational efficiency . the proposed model is modular and can be used by a broad spectrum of practitioners .

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Jie Huang, Zilong Wang, Kevin Chang, Wen-mei Hwu, JinJun Xiong

Challenge:	Existing models that measure semantic capacity of terms are not all considered equal . a good command of semantic capacity will give us more insight into the granularity of terms .
Approach:	They propose a model that evaluates semantic capacity of terms if text corpus can provide enough co-occurrence information of terms.
Outcome:	The proposed model can evaluate semantic capacity of terms if the corpus can provide enough co-occurrence information of terms.

MIO: A Foundation Model on Multimodal Tokens (2025.emnlp-main)

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Zekun Moore Wang, King Zhu, Chunpu Xu, Wangchunshu Zhou, Jiaheng Liu, Yibo Zhang, Jessie Wang, Ning Shi, Siyu Li, Yizhi Li, Haoran Que, Zhaoxiang Zhang, Yuanxing Zhang, Ge Zhang, Ke Xu, Jie Fu, Wenhao Huang

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

Challenge:	Existing models lack multimodal understanding capabilities, resulting in closed-source model that does not support multimodal interleaved sequences.
Approach:	They propose a foundation model built on multimodal tokens capable of understanding and generating speech, text, images, and videos in an end-to-end, autoregressive manner.
Outcome:	The proposed model is able to understand speech, text, images, and videos in an end-to-end, autoregressive manner.

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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

Enhancing Large Vision-Language Models with Ultra-Detailed Image Caption Generation (2025.emnlp-main)

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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Yu Zeng, Yukun Qi, Yiming Zhao, Xikun Bao, Lin Chen, Zehui Chen, Shiting Huang, Jie Zhao, Feng Zhao

Abstract then Play: A Skill-centric Reinforcement Learning Framework for Text-based Games (2023.findings-acl)

Challenge:	Existing pipelines for generating high-quality, ultra-detailed image captions are limited by the scarcity of image caption data.
Approach:	They propose a pipeline for generating high-quality, ultra-detailed image captions that integrates both pre-processing and post-processor stages.
Outcome:	The proposed pipeline improves LVLMs' perception and cognitive abilities across multiple vision-language benchmarks.

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Anjie Zhu, Peng-Fei Zhang, Yi Zhang, Zi Huang, Jie Shao

Challenge:	Existing reinforcement learning frameworks fail to decompose the task and abstract the action autonomously.
Approach:	They propose a skill-centric reinforcement learning framework capable of abstracting the action in an end-to-end manner.
Outcome:	Empirical experiments on the Jericho environment validate the proposed framework against state-of-the-art baselines.

Biomedical Question Answering via Multi-Level Summarization on a Local Knowledge Graph (2026.acl-long)

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Lingxiao Guan, Yuanhao Huang, Jie Liu

VER: Unifying Verbalizing Entities and Relations (2023.findings-emnlp)

Challenge:	Existing work on how to effectively capture multi-document relationships remains an open question . Existing techniques to mitigate this problem include hierarchical summarization of semantically related chunks or integrating Knowledge Graphs (KGs).
Approach:	They propose a method which constructs a local knowledge graph from retrieved documents . they use propositional claims to construct a knowledge graph and contextualize a small language model .
Outcome:	The proposed method outperforms RAG on biomedical benchmarks and is generalizable and effective.

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Jie Huang, Kevin Chang

Multi-Source Probing for Open-Domain Conversational Understanding (2023.emnlp-main)

Challenge:	a new model for verbalizing entities and relations is proposed to help understand entities and relationships . a unified model for Verbalizing Entities and Relations is proposed .
Approach:	They propose a model that takes any entity or entity set as input and generates a sentence to represent entities and relations.
Outcome:	The proposed model can generate sentences describing entities and relations . it can be used to explain entities and relationships, and to perform commonsense reasoning tasks .

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Yuanxi Li, Hao Zhou, Jie Zhou, Minlie Huang

Natural Language Interfaces

Challenge:	Existing models of open-domain dialogue comprehension have limited conversational understanding and response generation.
Approach:	They propose a multi-source probing method to probe dialogue comprehension abilities of open-domain dialogue models.
Outcome:	The proposed method aggregates features from multiple sources to accomplish diverse task goals and conducts downstream tasks in a generative manner consistent with dialogue model pre-training to leverage model capabilities.

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

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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.

Unlocking Continual Learning Abilities in Language Models (2024.findings-emnlp)

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Wenyu Du, Shuang Cheng, Tongxu Luo, Zihan Qiu, Zeyu Huang, Ka Chun Cheung, Reynold Cheng, Jie Fu

COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values (2026.findings-eacl)

Challenge:	Existing approaches to learning models (LMs) incorporate old task data or task-wise inductive bias into LMs, but old data and accurate task information are often unavailable or costly to collect.
Approach:	They propose a rehearsal-free method that updates model parameters with large magnitudes . they found that the L1-normalized magnitude distribution is different when different task data is used .
Outcome:	The proposed method improves accuracy and performance on four CL benchmarks.

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Siwei Wu, JinCheng Ren, Xeron Du, Shuyue Guo, Xingwei Qu, Yiming Liang, Jie Liu, Yunwen Li, Tyler Loakman, Tianyu Zheng, Boyu Feng, Huaqing Yuan, Zili Wang, Jiaheng Liu, Wenhao Huang, Chenglin Cai, Haoran Que, Jian Yang, Yuelin Bai, Zekun Moore Wang, Zhouliang Yu, Qunshu Lin, Ding Pan, Yuchen Eleanor Jiang, Tiannan Wang, Wangchunshu Zhou, Shenzhi Wang, Xingyuan Bu, Minghao Liu, Guoyin Wang, Ge Zhang, Chenghua Lin

LongSafety: Evaluating Long-Context Safety of Large Language Models (2025.acl-long)

Challenge:	Existing Chinese preference datasets suffer from limited scale, restricted domain coverage, and insufficiently rigorous data validation.
Approach:	They propose an LLM-based data annotation pipeline with no human intervention to annotate Chinese preference datasets.
Outcome:	The proposed pipeline outperforms existing Chinese preference datasets on AlignBench and Chinese Reward Benchmark.

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Yida Lu, Jiale Cheng, Zhexin Zhang, Shiyao Cui, Cunxiang Wang, Xiaotao Gu, Yuxiao Dong, Jie Tang, Hongning Wang, Minlie Huang

Text Editing as Imitation Game (2022.findings-emnlp)

Challenge:	Large Language Models (LLMs) have demonstrated remarkable capabilities in understanding and generating long sequences.
Approach:	They propose a benchmark to evaluate LLM safety in open-ended long-context tasks . they find that relevant context and extended input sequences can exacerbate safety risks .
Outcome:	The proposed benchmark identifies significant safety vulnerabilities in 16 LLMs . strong safety performance in short-context scenarios does not correlate with safety in long-contact tasks .

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Ning Shi, Bin Tang, Bo Yuan, Longtao Huang, Yewen Pu, Jie Fu, Zhouhan Lin

Syntactic Text Processing

Challenge:	Text editing is an important domain of processing tasks to edit the text in a localized fashion, such as text simplification.
Approach:	They propose a nonautoregressive decoder for state-to-action demonstrations that parallels the decoding while retaining the dependencies between tokens.
Outcome:	The proposed model outperforms the autoregressive baselines on a suite of Arithmetic Equation benchmarks in terms of performance, efficiency, and robustness.

Lightweight LLM Agent Memory with Small Language Models (2026.acl-long)

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Jiaquan Zhang, Chaoning Zhang, Shuxu Chen, Zhenzhen Huang, Pengcheng Zheng, Zhicheng Wang, Ping Guo, Fan Mo, Sung-Ho Bae, Jie Zou, Jiwei Wei, Yang Yang

SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval (2024.findings-acl)

Challenge:	Existing external memory systems for LLMs have low online overhead but are unstable in accumulating latency over long interactions.
Approach:	They propose a lightweight memory system for better agent memory driven by Small Language Models . lightmem modularizes memory retrieval, writing, and long-term consolidation . they show consistent gains across model scales and high efficiency .
Outcome:	The proposed system improves agent memory but has low latency and low online overhead . it separates online processing from offline consolidation to enable efficient memory invocation . the proposed system achieves an average F1 improvement of 2.5 over A-MEM on LoCoMo .

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Siwei Wu, Yizhi Li, Kang Zhu, Ge Zhang, Yiming Liang, Kaijing Ma, Chenghao Xiao, Haoran Zhang, Bohao Yang, Wenhu Chen, Wenhao Huang, Noura Al Moubayed, Jie Fu, Chenghua Lin

Exploring Dynamic Selection of Branch Expansion Orders for Code Generation (2021.acl-long)

Challenge:	Multi-modal information retrieval (MMIR) is a rapidly evolving field . current benchmarks for image-text pairings overlook the scientific domain .
Approach:	They develop a scientific domain-specific MMIR benchmark to evaluate image-text pairings using open-access research paper corpora.
Outcome:	The proposed benchmarks are based on 530K image-text pairs extracted from scientific documents with detailed captions.

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Hui Jiang, Chulun Zhou, Fandong Meng, Biao Zhang, Jie Zhou, Degen Huang, Qingqiang Wu, Jinsong Su

Programming Languages in NLP

Challenge:	Existing code generation models model abstract syntax tree (AST) but not suitable for all multi-branch nodes.
Approach:	They propose to equip a Seq2Tree model with a branch selector to determine optimal expansion orders for multi-branch nodes.
Outcome:	The proposed model can determine optimal expansion orders of branches for multi-branch nodes.

Attending via both Fine-tuning and Compressing (2021.findings-acl)

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Jie Zhou, Yuanbin Wu, Qin Chen, Xuanjing Huang, Liang He

Explainability & Interpretability in NLP

Challenge:	Existing studies show that attention mechanisms can improve models' interpretation, but they are not explicable.
Approach:	They propose a framework consisting of a learner and a compressor to purify attention scores . they propose to fine-tune and compress the attention mechanism to obtain a more faithful explanation .
Outcome:	The proposed framework improves performance and interpretability on eight benchmark datasets.

RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models (2024.findings-acl)

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Noah Wang, Z.y. Peng, Haoran Que, Jiaheng Liu, Wangchunshu Zhou, Yuhan Wu, Hongcheng Guo, Ruitong Gan, Zehao Ni, Jian Yang, Man Zhang, Zhaoxiang Zhang, Wanli Ouyang, Ke Xu, Wenhao Huang, Jie Fu, Junran Peng

All Changes May Have Invariant Principles: Improving Ever-Shifting Harmful Meme Detection via Design Concept Reproduction (2026.acl-long)

Challenge:	Large Language Models (LLMs) have paved the way for complex tasks such as role-playing.
Approach:	They propose a framework to benchmark, elicit, and enhance role-playing abilities in Large Language Models.
Outcome:	The proposed framework improves role-playing abilities with 168,093 samples.

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Ziyou Jiang, Mingyang Li, Junjie Wang, Yuekai Huang, Jie Huang, Zhiyuan Chang, Zhaoyang Li, Qing Wang

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

Challenge:	Existing methods for harmful meme detection only learn the combination of harmful elements and lack understanding of these implicit expressions.
Approach:	They propose a method that detects harmful memes by replicating the design concept of malicious users.
Outcome:	The proposed method achieves the highest accuracy with 81.1% and has slight accuracy decreases when generalized to type-shifting and temporal-evolving memes.

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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

Multilinguality

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.

ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning (2021.acl-long)

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Yujia Qin, Yankai Lin, Ryuichi Takanobu, Zhiyuan Liu, Peng Li, Heng Ji, Minlie Huang, Maosong Sun, Jie Zhou

OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models (2025.acl-long)

Challenge:	Existing pre-training objectives do not explicitly model relational facts in text . Experimental results show that ERICA can improve typical PLMs on several language understanding tasks, including relation extraction, entity typing and question answering.
Approach:	They propose a contrastive learning framework ERICA to obtain a deep understanding of entities and relations in text.
Outcome:	The proposed framework can improve PLMs on several language understanding tasks, especially under low-resource settings.

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Siming Huang, Tianhao Cheng, Jason Klein Liu, Weidi Xu, Jiaran Hao, Liuyihan Song, Yang Xu, Jian Yang, Jiaheng Liu, Chenchen Zhang, Linzheng Chai, Ruifeng Yuan, Xianzhen Luo, Qiufeng Wang, YuanTao Fan, Qingfu Zhu, Zhaoxiang Zhang, Yang Gao, Jie Fu, Qian Liu, Houyi Li, Ge Zhang, Yuan Qi, Xu Yinghui, Wei Chu, Zili Wang

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

Challenge:	Code LLMs lack reproducible data pipelines and training protocols for reproducible advancements in code intelligence.
Approach:	They propose a top-tier code LLM that releases model weights and inference code . reproducible data pipelines, rigorous experimental ablation results and training protocols are included .
Outcome:	The proposed model achieves comparable performance to leading models and serves as an "open cookbook" reproducible training data, rigorous experimental ablation results, and detailed training protocols are also included in the model.

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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.

HPSS: Heuristic Prompting Strategy Search for LLM Evaluators (2025.findings-acl)

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Bosi Wen, Pei Ke, Yufei Sun, Cunxiang Wang, Xiaotao Gu, Jinfeng Zhou, Jie Tang, Hongning Wang, Minlie Huang

CritiqueLLM: Towards an Informative Critique Generation Model for Evaluation of Large Language Model Generation (2024.acl-long)

Challenge:	Existing efforts to optimize text evaluation prompts neglect the combinatorial impact of multiple factors, leading to insufficient optimization of the evaluation pipeline.
Approach:	They propose to integrate 8 key factors for evaluation prompts and integrate them into an algorithm that searches for well-behaved prompting strategies for LLM evaluators.
Outcome:	The proposed method outperforms existing methods and human-designed evaluation prompts on four evaluation tasks.

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Pei Ke, Bosi Wen, Andrew Feng, Xiao Liu, Xuanyu Lei, Jiale Cheng, Shengyuan Wang, Aohan Zeng, Yuxiao Dong, Hongning Wang, Jie Tang, Minlie Huang

CharacterGLM: Customizing Social Characters with Large Language Models (2024.emnlp-industry)

Challenge:	Existing models for NLP evaluations lack the ability to generate informative critiques in pointwise grading and pairwise comparison especially without references.
Approach:	They propose a method which can acquire pointwise grading critiques with pseudo references and revise these critiques via multi-path prompting to obtain informative evaluation data in different tasks and settings.
Outcome:	The proposed method outperforms all open-source models and even GPT-4 in system-level correlations of pointwise grading.

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Jinfeng Zhou, Zhuang Chen, Dazhen Wan, Bosi Wen, Yi Song, Jifan Yu, Yongkang Huang, Pei Ke, Guanqun Bi, Libiao Peng, JiaMing Yang, Xiyao Xiao, Sahand Sabour, Xiaohan Zhang, Wenjing Hou, Yijia Zhang, Yuxiao Dong, Hongning Wang, Jie Tang, Minlie Huang

HoWToBench: Holistic Evaluation for LLM’s Capability in Human-level Writing using Tree of Writing (2026.acl-long)

Challenge:	Character-based dialogue systems (CharacterDial) allow users to customize social characters for social interactions.
Approach:	They will collect a large-scale Chinese corpus of characters with diverse categories and behaviors and develop CharacterGLM models to address these challenges.
Outcome:	Experiments show that CharacterGLM outperforms most popular open- and closed-source LLMs and performs comparable to GPT-4.

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Andrew Zhuoer Feng, Cunxiang Wang, Yu Luo, Lin Fan, Irene Zhou, Zikang Wang, Xiaotao Gu, Jie Tang, Hongning Wang, Minlie Huang

Challenge:	Evaluating the writing capabilities of large language models remains a significant challenge due to the multidimensional nature of writing skills and the limitations of existing metrics.
Approach:	They propose to model the aggregation weights of sub-features in a tree-structured workflow and propose a Chinese writing benchmark that mitigates biases.
Outcome:	The proposed tree-of-writing (ToW) measures the writing capabilities of large language models (LLMs) in Chinese and shows that it mitigates biases and achieves a 0.93 Pearson correlation with human judgments.

Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings (2021.emnlp-main)

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Shaopeng Lai, Ante Wang, Fandong Meng, Jie Zhou, Yubin Ge, Jiali Zeng, Junfeng Yao, Degen Huang, Jinsong Su

Structured Data in NLP

Challenge:	Existing sentence ordering models can be classified into pairwise ordering models and set-to-sequence models.
Approach:	They propose a novel sentence ordering framework which introduces two classifiers to make better use of pairwise orderings for graph-based sentence ordering.
Outcome:	The proposed model achieves state-of-the-art performance on five commonly-used datasets.

Mixture of Attention Heads: Selecting Attention Heads Per Token (2022.emnlp-main)

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Xiaofeng Zhang, Yikang Shen, Zeyu Huang, Jie Zhou, Wenge Rong, Zhang Xiong

When and Who? Conversation Transition Based on Bot-Agent Symbiosis Learning Network (2020.coling-main)

Challenge:	Mixture-of-Experts (MoE) networks have been proposed as an efficient way to scale up model capacity and implement conditional computing.
Approach:	They propose a new architecture that combines multi-head attention with the MoE mechanism and a sparsely gated architecture that allows for faster computations.
Outcome:	The proposed architecture can scale up the number of attention heads and the number parameters while preserving computational efficiency.

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Yipeng Yu, Ran Guan, Jie Ma, Zhuoxuan Jiang, Jingchang Huang

Natural Language Interfaces

Challenge:	a bot-agent symbiosis is a method for transparent conversation transition in online customer service applications.
Approach:	They propose a bot-agent symbiosis approach to solve conversation transition problems . they provide user feedback and develop deep neural networks to predict the NPS .
Outcome:	The proposed approach outperforms state-of-the-art methods on real-time data generated from an online service support platform.

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

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

COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning (2025.findings-naacl)

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

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Yuelin Bai, Xeron Du, Yiming Liang, Leo Jin, Junting Zhou, Ziqiang Liu, Feiteng Fang, Mingshan Chang, Tianyu Zheng, Xincheng Zhang, Nuo Ma, Zekun Moore Wang, Ruibin Yuan, Haihong Wu, Hongquan Lin, Wenhao Huang, Jiajun Zhang, Chenghua Lin, Jie Fu, Min Yang, Shiwen Ni, Ge Zhang

Hiring Now: A Skill-Aware Multi-Attention Model for Job Posting Generation (2020.acl-main)

Challenge:	Existing datasets for Chinese instruction tuning are not well-aligned with Chinese users’ interaction patterns.
Approach:	They propose to use Chinese instruction tuning datasets to improve instruction fine-tuning for Chinese users.
Outcome:	The proposed dataset shows that Chinese models achieve competitive performance in diverse benchmarks.

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Liting Liu, Jie Liu, Wenzheng Zhang, Ziming Chi, Wenxuan Shi, Yalou Huang

Challenge:	Creating job requirements is a crucial step in the recruiting process, but it is difficult to specify the level of education, experience, relevant skills per the job description.
Approach:	They propose a conditional text generation task to generate job requirements based on job descriptions . they use a hierarchical decoder to label the job description with multiple skills . a skill knowledge graph is constructed to capture the global prior knowledge about skills based upon the model .
Outcome:	The proposed method is evaluated on real-world job posting data.

Can Language Models Be Specific? How? (2023.findings-acl)

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Jie Huang, Kevin Chen-Chuan Chang, Jinjun Xiong, Wen-mei Hwu

Multi-step Jailbreaking Privacy Attacks on ChatGPT (2023.findings-emnlp)

Challenge:	Existing pre-trained language models have a preference for more specific answers . however, there may exist multiple answers for a query, while not all answers are equally specific.
Approach:	They propose to build a benchmark for specificity testing by forming masked token prediction tasks with prompts.
Outcome:	The proposed methods improve the specificity of pre-trained language models without additional training.

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Haoran Li, Dadi Guo, Wei Fan, Mingshi Xu, Jie Huang, Fanpu Meng, Yangqiu Song

Asclepius: A Spectrum Evaluation Benchmark for Medical Multi-Modal Large Language Models (2025.acl-long)

Challenge:	With the rapid evolution of large language models (LLMs), many downstream NLP tasks can be well solved given appropriate prompts.
Approach:	They propose to integrate ChatGPT and Bing GPT3 into their applications to create a set of LLMs that can be used to generate NLP tasks with appropriate prompts.
Outcome:	The proposed models can be zero-shot or few-shot learners to solve specified tasks and can even be zero or few shot learners.

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Jie Liu, Wenxuan Wang, Su Yihang, Jingyuan Huang, Yudi Zhang, Cheng-Yi Li, Wenting Chen, Xiaohan Xing, Kao-Jung Chang, Linlin Shen, Michael R. Lyu

Neural-DINF: A Neural Network based Framework for Measuring Document Influence (2020.acl-main)

Challenge:	Medical Multi-Modal Large Language Models (Med-MLLMs) are a promising new form of artificial general intelligence due to their ability to tackle complex tasks.
Approach:	They propose a new benchmark that comprehensively assesses medical multi-modal large language models in terms of distinct medical specialties and different diagnostic capacities.
Outcome:	The proposed model covers 15 medical specialties and different diagnostic capacities, and excludes overlap with existing VQA dataset.

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Jie Tan, Changlin Yang, Ying Li, Siliang Tang, Chen Huang, Yueting Zhuang

Challenge:	Existing methods to measure scholarly impact of documents without citations only consider word frequency change.
Approach:	They propose a neural network framework that measures document influence without citations by using word frequency changes and word semantic shifts.
Outcome:	The proposed model outperforms existing models on document influence evaluation without citations.

AutoCAD: Automatically Generate Counterfactuals for Mitigating Shortcut Learning (2022.findings-emnlp)

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Jiaxin Wen, Yeshuang Zhu, Jinchao Zhang, Jie Zhou, Minlie Huang

Challenge:	Existing methods for generating counterfactuals rely on human efforts or task-specific designs.
Approach:	They propose to use a fully automatic and task-agnostic CAD generation framework to generate diverse counterfactuals.
Outcome:	The proposed framework outperforms human-in-the-loop and task-specific CAD methods on multiple out-of-domain and challenge benchmarks.

Towards Robust k-Nearest-Neighbor Machine Translation (2022.emnlp-main)

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Hui Jiang, Ziyao Lu, Fandong Meng, Chulun Zhou, Jie Zhou, Degen Huang, Jinsong Su

Text Generation

Challenge:	k-Nearest-Neighbor Machine Translation (kNN-MT) is a popular research paradigm in machine translation.
Approach:	They propose a confidence-enhanced kNN-MT model with robust training to reduce noise . they introduce NMT confidence to refine the modeling of important components of kN-MT .
Outcome:	The proposed model improves on four benchmark datasets and is robust to training.

Trial and Error: Exploration-Based Trajectory Optimization of LLM Agents (2024.acl-long)

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Yifan Song, Da Yin, Xiang Yue, Jie Huang, Sujian Li, Bill Yuchen Lin

Challenge:	Large Language Models (LLMs) have become integral components in various autonomous agent systems.
Approach:	They propose an exploration-based trajectory optimization approach that allows agents to learn from their exploration failures.
Outcome:	The proposed method outperforms baseline methods on three complex tasks by a large margin.

Domain Representative Keywords Selection: A Probabilistic Approach (2022.findings-acl)

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Pritom Saha Akash, Jie Huang, Kevin Chang, Yunyao Li, Lucian Popa, ChengXiang Zhai

Challenge:	a probabilistic approach to select a subset of a target domain representative keywords is crucial for many downstream tasks in natural language processing.
Approach:	They propose a probabilistic approach to select a subset of a target domain representative keywords from a candidate set, contrasting with a context domain.
Outcome:	The proposed approach provides more importance to distinctive keywords than common keywords contrasting with the context domain.

DocRED: A Large-Scale Document-Level Relation Extraction Dataset (P19-1)

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Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, Maosong Sun