Papers by Chao Huang

RecGPT: A Foundation Model for Sequential Recommendation (2025.emnlp-main)

Challenge:	Modern recommender systems aim to understand user-item relationships through past interactions, but their effectiveness is limited when handling sparse data or zero-shot scenarios.
Approach:	They propose a model-agnostic recommendation instruction-tuning paradigm that integrates large language models with collaborative filtering.
Outcome:	The proposed model-agnostic recommendation instruction-tuning paradigm improves performance across various settings and plug-and-play compatibility with state-of-the-art recommender systems.

Copied to clipboard

Yangqin Jiang, Xubin Ren, Lianghao Xia, Da Luo, Kangyi Lin, Chao Huang

Make-A-Voice: Revisiting Voice Large Language Models as Scalable Multilingual and Multitask Learners (2024.acl-long)

Challenge:	Existing approaches fail in cold-start and cross-domain scenarios where new users or items lack sufficient interaction history.
Approach:	They propose a foundation model for sequential recommendation that achieves genuine zero-shot generalization capabilities by deriving item representations exclusively from textual features.
Outcome:	The proposed model achieves zero-shot generalization capabilities in cold-start and cross-domain scenarios.

Copied to clipboard

Rongjie Huang, Chunlei Zhang, Yongqi Wang, Dongchao Yang, Jinchuan Tian, Zhenhui Ye, Luping Liu, Zehan Wang, Ziyue Jiang, Xuankai Chang, Jiatong Shi, Chao Weng, Zhou Zhao, Dong Yu

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

Challenge:	Large language models (LLMs) have been used for general-purpose interfaces across multiple tasks and languages.
Approach:	They propose to use large language models as a general-purpose interface across multiple tasks and languages.
Outcome:	The proposed model performs better on 200K hours of 6-language data for voice generation applications.

Copied to clipboard

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

AutoAgent: A Fully-Automated and Zero-Code Framework for LLM Agents (2026.findings-acl)

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.

Copied to clipboard

Jiabin Tang, Tianyu Fan, Chao Huang

Challenge:	Large Language Model (LLM) agents have demonstrated remarkable capabilities in task automation and intelligent decision-making.
Approach:	They propose a Fully-Automated and highly Self-Developing framework that enables users to create and deploy LLM agents using natural language alone.
Outcome:	AutoAgent is a fully-automated and highly self-developing framework that enables users to create and deploy LLM agents using natural language alone.

Rewrite to Jailbreak: Discover Learnable and Transferable Implicit Harmfulness Instruction (2025.findings-acl)

Copied to clipboard

Yuting Huang, Chengyuan Liu, Yifeng Feng, Yiquan Wu, Chao Wu, Fei Wu, Kun Kuang

Responsible & Trustworthy NLP

Challenge:	Existing jailbreak methods create a forced instruction-following scenario, or search adversarial prompts with prefix or suffix tokens to achieve a specific representation manually or automatically.
Approach:	They propose a method that rewrites the original instruction to achieve a jailbreak . they propose rewriting the original instructions to improve the attack strategy .
Outcome:	The proposed method is more efficient and easier to identify since no additional features are introduced.

TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance (2021.acl-long)

Copied to clipboard

Fengbin Zhu, Wenqiang Lei, Youcheng Huang, Chao Wang, Shuo Zhang, Jiancheng Lv, Fuli Feng, Tat-Seng Chua

Structured Data in NLP

Challenge:	Existing QA systems focus on unstructured text, structured knowledge base, or semi-structured tables.
Approach:	They propose a large-scale question answering model based on financial reports . numerical reasoning is usually required to infer the answer .
Outcome:	The proposed model achieves 58.0% inF1, an 11.1% increase over the baseline model, but still lags behind the best human model.

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

Copied to clipboard

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

Responsible & Trustworthy NLP

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

Text Classification Using Label Names Only: A Language Model Self-Training Approach (2020.emnlp-main)

Copied to clipboard

Yu Meng, Yunyi Zhang, Jiaxin Huang, Chenyan Xiong, Heng Ji, Chao Zhang, Jiawei Han

Information Retrieval

Challenge:	Current text classification methods require a large number of labeled documents as training data.
Approach:	They propose a model that uses only the label name of each class to train classification models on unlabeled data without using any labeled examples.
Outcome:	The proposed model achieves 90% accuracy on four benchmark datasets using label names as the only supervision .

MCS: An In-battle Commentary System for MOBA Games (2022.coling-1)

Copied to clipboard

Xiaofeng Qi, Chao Li, Zhongping Liang, Jigang Liu, Cheng Zhang, Yuanxin Wei, Lin Yuan, Guang Yang, Lanxiao Huang, Min Li

Challenge:	In-battle commentary is an important component of live streaming of e-sports competitions and is applicable to a wide range of scenarios like combat information analysis and live streaming.
Approach:	They propose a generative system for in-battle real-time commentary in mobile MOBA games and propose 'transform' method to convert match statistics and utterances into consistent encoding space.
Outcome:	The proposed system is based on real-time match statistics and events and can be used for live streaming, e-sports commentary and combat information analysis.

Intrinsic Model Weaknesses: How Priming Attacks Unveil Vulnerabilities in Large Language Models (2025.findings-naacl)

Copied to clipboard

Yuyi Huang, Runzhe Zhan, Derek F. Wong, Lidia S. Chao, Ailin Tao

A Novel Matching Paradigm: Unified Generative and Discriminative LLM with Prompt Compression for Relevance Learning (2026.acl-industry)

Challenge:	Large language models (LLMs) have significant impact on various industries and societal functions due to advanced instruction-following capabilities.
Approach:	They developed and tested novel attack strategies on popular LLMs to expose their vulnerabilities in generating harmful content.
Outcome:	The proposed attacks achieved an ASR of 100% on open-source models, including Meta’s Llama-3.2, Google’s Gemma-2, Mistral’s Mistral-NeMo, Falcon’s Falcon-mamba, Apple’s DCLM, Microsoft’s Phi3, and Qwen’s Qwend2.5, among others.

Copied to clipboard

Guoliang Zhao, Zixin Cui, Chao Ye, Dengwu He, Fei Huang, Yubo Liu, Shuanglong Li, Tzungren Kuo, Bin Ding, Shuang Zhang, null KunhongZhu, Zhi Guo, Liu Lin

Responsible & Trustworthy NLP

Challenge:	Existing approaches to matching use Large Language Models as feature extractors, underutilizing their full modeling capabilities.
Approach:	They propose a matching paradigm that integrates two-tower, single-towing, and generative tasks within a unified LLM framework via attention-mask partitioning.
Outcome:	The proposed model achieves superior performance and strong practical value in an industrial search engine.

From Scenes to Elements: Multi-Granularity Evidence Retrieval for Verifiable Multimodal RAG (2026.findings-acl)

Copied to clipboard

Guanhua Chen, Chuyue Huang, Yutong Yao, Shudong Liu, Xueqing Song, Lidia S. Chao, Derek F. Wong

Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking (2021.findings-acl)

Challenge:	Existing multimodal Retrieval-Augmented Generation (RAG) systems retrieve evidence at coarse granularities, making failures unverifiable.
Approach:	They propose a multimodal benchmark that features real-world landmarks with annotations across multiple viewpoints and a framework that treats visual elements as first-class retrieval units through three stages: element-level detection and classification, multi-granularity cross-modal alignment for evidence retrieval, and attribution-constrained generation.
Outcome:	The proposed framework achieves up to 29.2% improvement over six strong baselines for this task.

Copied to clipboard

Heng-Da Xu, Zhongli Li, Qingyu Zhou, Chao Li, Zizhen Wang, Yunbo Cao, Heyan Huang, Xian-Ling Mao

Syntactic Text Processing

Challenge:	Chinese Spell Checking (CSC) aims to detect and correct erroneous characters for usergenerated text in Chinese.
Approach:	They propose a Chinese spell checker that leverages multimodal Chinese characters' information to predict the correct output.
Outcome:	The proposed model outperforms strong baselines on the SIGHAN benchmarks by a large margin.

Copied to clipboard

Pengwei Zhan, Jing Yang, He Wang, Chao Zheng, Xiao Huang, Liming Wang

Semantic Text Processing

Challenge:	Neural language models are vulnerable to word-level adversarial text attacks . previous word-based search methods assume important words influence prediction .
Approach:	They propose a method for similarizing the influence of words with contrast learning that encourages model to learn sentence representations in which words of varying importance have a more uniform influence on prediction.
Outcome:	The proposed method is compatible with various training methods and improves model robustness against various adversarial attacks.

Multi-Stage LLM Fine-Tuning with a Continual Learning Setting (2025.findings-naacl)

Copied to clipboard

Changhao Guan, Chao Huang, Hongliang Li, You Li, Ning Cheng, Zihe Liu, Yufeng Chen, Jinan Xu, Jian Liu

Traffic-R1: Reinforced LLMs Bring Human-Like Reasoning to Traffic Signal Control Systems (2026.acl-long)

Challenge:	Large language models (LLMs) have made significant progress in knowledge-intensive applications, but they may face a multi-stage continuous learning scenario.
Approach:	They propose a multi-stage continuous learning paradigm that includes a preference-based learning bias to identify potential knowledge conflicts and a self-distillation-based data augmentation strategy to expand and enrich the training corpus.
Outcome:	The proposed learning paradigm achieves a significant improvement in accuracy after 7 stages of fine-tuning compared to previous methods while preserving general knowledge.

Copied to clipboard

Xingchen Zou, Yuhao Yang, Zheng Chen, Xixuan Hao, Yiqi Chen, Chao Huang, Yuxuan Liang

One Pair Suffices: Unlocking Universal Zero-Shot Translation via Cross-Architecture Alignment (2026.acl-long)

Challenge:	Rapid urbanization and surging vehicle ownership intensify congestion . rapid urbanization drives crash rates, slow emergency response, and burden transit-poor communities .
Approach:	They introduce a 3B-parameter foundation model with human-like reasoning for Traffic signal control (TSC) they use reinforcement learning and network communication to convert LLM into a traffic-control model that operates like a human traffic agent.
Outcome:	The proposed model outperforms baselines and training-intensive RL controllers on a simulated traffic environment and reduces queue lengths by more than 5%.

Copied to clipboard

Hao Zong, Cong Hu Yuan, Chao Bei, Wentao Chen, Huan Liu, Kaiyu Huang, Degen Huang

Attribution-Based Analysis and Optimization of Modular Agentic Workflows (2026.findings-acl)

Challenge:	Current paradigms for empowering Large Language Models with multilingual capabilities rely heavily on massive instruction tuning.
Approach:	They propose a hybrid cross-alignment approach that fuses a frozen NLLB encoder with a Qwen decoder via a closed-loop dual-adapter architecture.
Outcome:	The proposed model outperforms towerPlus-9B and Aya-101 on language-agnostic projection protocols.

Copied to clipboard

Yingxuan Yang, Bo Huang, Siyuan Qi, Chao Feng, Haoyi Hu, Yuxuan Zhu, Jinbo Hu, Haoran Zhao, Ziyi He, Xiao Liu, ZongYu Wang, Muning Wen, Lin Qiu, Xuezhi Cao, Xunliang Cai, Yong Yu, Weinan Zhang

Challenge:	Large Language Models (LLMs) have driven the rise of agentic workflows . yet, how can we attribute performance gains to individual upgrades and their interactions?
Approach:	They propose a game-theoretic framework that models component upgrades as players and evaluates component coalitions to compute Shapley values.
Outcome:	The proposed framework provides interaction-aware attribution and recommendation for model allocation under a fixed workflow structure.

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

Copied to clipboard

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

Multilinguality

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.

Training Language Model to Critique for Better Refinement (2025.findings-acl)

Copied to clipboard

Tianshu Yu, Chao Xiang, Mingchuan Yang, Pei Ke, Bosi Wen, Cunxiang Wang, Jiale Cheng, Li Zhang, Xinyu Mu, Chuxiong Sun, Minlie Huang

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

Challenge:	Large language models (LLMs) have remarkable evaluation and critique capabilities, providing insightful feedback and identifying flaws in various tasks.
Approach:	They propose a framework to train critic models using refinement signals to generate feedback loops where critiques guide the model in refining its responses.
Outcome:	The proposed framework outperforms traditional methods and open-source models in terms of critique quality and refinement outcomes.

Copied to clipboard

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

OD-Stega: LLM-Based Relatively Secure Steganography via Optimized Distributions (2026.eacl-long)

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

Copied to clipboard

Yu-Shin Huang, Peter Just, Hanyun Yin, Krishna Narayanan, Ruihong Huang, Chao Tian

A Speaker-Aware Co-Attention Framework for Medical Dialogue Information Extraction (2022.emnlp-main)

Challenge:	In coverless steganography, secret bits are embedded in as few language tokens as possible . stego-texts can be decoded by eavesdroppers, but are difficult to detect .
Approach:	They propose a method to embed secret bits in language tokens using a Large Language Model . they propose maximizing the entropy of a replacement probability distribution .
Outcome:	The proposed method should embed secret bits in as few language tokens as possible while keeping the stego-text as natural as possible.

Copied to clipboard

Yuan Xia, Zhenhui Shi, Jingbo Zhou, Jiayu Xu, Chao Lu, Yehui Yang, Lei Wang, Haifeng Huang, Xia Zhang, Junwei Liu

Natural Language Interfaces

Challenge:	With the development of medical digitization, the extraction and structuring of electronic medical records (EMRs) have become challenging but fundamental tasks.
Approach:	They propose a speaker-aware dialogue encoder with multi-task learning which takes the speaker's identity into account and a co-attention fusion network to aggregate the utterance information.
Outcome:	The proposed framework outperforms the state-of-the-art methods on the public medical dialogue extraction datasets to demonstrate its superiority.

Path Drift in Large Reasoning Models: How First-Person Commitments Override Safety (2025.emnlp-main)

Copied to clipboard

Yuyi Huang, Runzhe Zhan, Lidia S. Chao, Ailin Tao, Derek F. Wong

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

Challenge:	Existing studies on prompt injection and jailbreak attacks primarily target the surface structure of input prompts.
Approach:	They propose a three-stage approach to mitigate the risk of Long-CoT reasoning drift . they propose 'path-level defense' strategy that incorporates role attribution correction and metacognitive reflection .
Outcome:	The proposed framework reduces refusal rates and ethical evaporation, while ethical escalation and layered disclaimers progressively steer models toward unsafe completions.

Copied to clipboard

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

Palette of Language Models: A Solver for Controlled Text Generation (2025.naacl-long)

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.

Copied to clipboard

Zhe Yang, Yi Huang, Yaqin Chen, XiaotingWu XiaotingWu, Junlan Feng, Chao Deng

OpenGraph: Towards Open Graph Foundation Models (2024.findings-emnlp)

Challenge:	Recent advances in large language models have revolutionized text generation with their remarkable capabilities.
Approach:	They propose to combine a single-attribute model with a discriminative model to achieve a combination strategy that incorporates positive correlation and attribute enhancement.
Outcome:	The proposed method is adapted for single-attribute control scenario and achieves surpassing results.

Copied to clipboard

Lianghao Xia, Ben Kao, Chao Huang

Structured Data in NLP

Challenge:	Graph Neural Networks (GNNs) have emerged as promising techniques for encoding structural information and improving performance in tasks like link prediction and node classification.
Approach:	They propose a graph foundation model that generalizes to unseen graph data with different properties.
Outcome:	The proposed model achieves remarkable zero-shot graph learning performance across various settings.

TransLLM: A Unified Multi-Task Large Language Model for Urban Transportation via Learnable Prompting (2026.acl-long)

Copied to clipboard

Jiaming Leng, Yunying Bi, Chuan Qin, Zhenya Huang, Bing Yin, Haojie Ren, Yanyong Zhang, Chao Wang

XRec: Large Language Models for Explainable Recommendation (2024.findings-emnlp)

Challenge:	Existing models lack generalization capabilities and lack structured spatiotemporal data.
Approach:	They propose a unified multi-task framework that synergizes spatiotemporal encoding with LLM reasoning through learnable prompt composition.
Outcome:	The proposed framework outperforms baseline models on seven datasets and three tasks on supervised and zero-shot settings with excellent generalization and robustness.

Copied to clipboard

Qiyao Ma, Xubin Ren, Chao Huang

Explainability & Interpretability in NLP

Challenge:	Collaborative filtering (CF) is a widely adopted approach, but lacks the ability to provide explanations for the recommended items.
Approach:	They propose a model-agnostic framework that enables large language models to provide comprehensive explanations for user behaviors in recommender systems.
Outcome:	The proposed framework outperforms baseline approaches in explainable recommender systems.

Causal Document-Grounded Dialogue Pre-training (2023.emnlp-main)

Copied to clipboard

Yingxiu Zhao, Bowen Yu, Bowen Li, Haiyang Yu, Jinyang Li, Chao Wang, Fei Huang, Yongbin Li, Nevin Zhang

Dialogue Systems & Conversational Agents

Challenge:	Existing methods for document-grounded dialogue (DocGD) rely on general pre-trained language models without a tailored pre-training approach that explicitly captures causal relationships.
Approach:	They propose a causally-complete dataset construction strategy for developing million-scale DocGD pre-training corpora and a perturbation-based strategy to capture causality.
Outcome:	The proposed strategy yields significant and consistent improvements in fully-supervised, low-resource, few-shot, and zero-shot settings.

Boosting Data Utilization for Multilingual Dense Retrieval (2025.emnlp-main)

Copied to clipboard

Chao Huang, Fengran Mo, Yufeng Chen, Changhao Guan, Zhenrui Yue, Xinyu Wang, Jinan Xu, Kaiyu Huang

Information Retrieval

Challenge:	Existing studies focus on fine-tuning multilingual dense retrieval models, but data scarcity for low-resource languages makes it difficult to align representations in a shared vector space.
Approach:	They propose to obtain high-quality hard negative samples and effective mini-batch data to boost data utilization for multilingual dense retrieval by obtaining high-quality negative samples.
Outcome:	The proposed method outperforms existing baselines on a multilingual retrieval benchmark, MIRACL, with 16 languages.

Read Top News First: A Document Reordering Approach for Multi-Document News Summarization (2022.findings-acl)

Copied to clipboard

Chao Zhao, Tenghao Huang, Somnath Basu Roy Chowdhury, Muthu Kumar Chandrasekaran, Kathleen McKeown, Snigdha Chaturvedi

Information Extraction & Text Mining

Challenge:	Existing methods for extracting multi-document news summarization neglect relative importance of documents.
Approach:	They propose to concatenate all documents into a single meta-document and then summarize it using an SDS model.
Outcome:	The proposed approach outperforms state-of-the-art methods with more complex architectures.

GraphAgent: Agentic Graph Language Assistant (2025.emnlp-main)

Copied to clipboard

Yuhao Yang, Jiabin Tang, Lianghao Xia, Xingchen Zou, Yuxuan Liang, Chao Huang

MiniRAG: A Lightweight RAG system with Small Language Models (2026.acl-long)

Challenge:	Real-world data combines structured and unstructured formats, capturing explicit relationships and implicit semantic interdependencies.
Approach:	They propose GraphAgent, an automated agent pipeline addressing both explicit and implicit graph-enhanced semantic dependencies for predictive and generative tasks.
Outcome:	Extensive experiments on diverse datasets validate GraphAgent’s effectiveness in graph-related predictive and text generative tasks.

Copied to clipboard

Tianyu Fan, Jingyuan Wang, Xubin Ren, Chao Huang

Green & Sustainable NLP

Challenge:	Existing RAG frameworks rely on Large Language Models (LLMs) for all stages of the process, resulting in high computational costs and resource demands.
Approach:	They propose a semantic-aware heterogeneous graph indexing mechanism that combines text chunks and named entities in a unified structure and a lightweight topology-enhanced retrieval approach that leverages graph structures for efficient knowledge discovery without requiring advanced language capabilities.
Outcome:	The proposed system achieves comparable performance to LLM-based methods while requiring only 25% of the storage space.

Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking (2022.findings-emnlp)

Copied to clipboard

Yinghui Li, Shirong Ma, Qingyu Zhou, Zhongli Li, Li Yangning, Shulin Huang, Ruiyang Liu, Chao Li, Yunbo Cao, Haitao Zheng

Syntactic Text Processing

Challenge:	Chinese Spell Checking (CSC) aims to detect and correct Chinese spelling errors.
Approach:	They propose a framework which renders Chinese Spell Checking model to learn heterogeneous knowledge from the dictionary in terms of phonetics, vision, and meaning.
Outcome:	The proposed framework renders the CSC model to learn heterogeneous knowledge from the dictionary in terms of phonetics, vision, and meaning.

DRIFT: Transferring Reasoning Priors for Efficient MLLM Fine-Tuning (2026.findings-acl)

Copied to clipboard

Chao Huang, Zeliang Zhang, Jiang Liu, Ximeng Sun, Jialian Wu, Xiaodong Yu, Ze Wang, Chenliang Xu, Emad Barsoum, Zicheng Liu

LightReasoner: Can Small Language Models Teach Large Language Models Reasoning? (2026.acl-long)

Challenge:	Multimodal large language models (MLLMs) have made rapid progress in perception and alignment, but their reasoning ability often lags behind strong text-only LLMs.
Approach:	They propose a method that transfers reasoning knowledge in the gradient space while preserving multimodal alignment.
Outcome:	Experiments on multimodal reasoning benchmarks show that DRIFT outperforms naive merging and standard SFT.

Copied to clipboard

Jingyuan Wang, Yankai Chen, Zhonghang Li, Chao Huang

Evaluating the Expressive Appropriateness of Speech in Rich Contexts (2026.acl-long)

Challenge:	Large language models (LLMs) have demonstrated remarkable progress in reasoning, but are resource-intensive and require large curated datasets.
Approach:	They propose a framework that leverages the behavioral divergence between a stronger expert model and a weaker amateur model.
Outcome:	The proposed framework improves accuracy by up to 28.1% while reducing time consumption by 90% and tuning token usage by 99%.

Copied to clipboard

Tianrui Wang, Ziyang Ma, Yizhou Peng, Haoyu Wang, Zhikang Niu, Zikang Huang, Yihao Wu, Yi-Wen Chao, Yu Jiang, Yuheng Lu, Guanrou Yang, Xuanchen Li, Hexin Liu, Chunyu Qiang, Cheng Gong, Yifan Yang, Tianchi Liu, Junyu Wang, Nana Hou, Meng Ge, Fuming You, Yang Wei, Zhongqian Sun, Hu Haifeng, Xiaobao Wang, Eng Siong Chng, Xie Chen, Longbiao Wang, Jianwu Dang

Speech & Audio in NLP

Challenge:	Existing methods for evaluating expressive speech focus on word accuracy, naturalness, signal quality, or emotional intensity at the utterance level.
Approach:	They propose a framework for Evaluating Expressive Appropriateness in speech that assesses whether a speech sample aligns with the underlying communicative intent implied by its discourse-level narrative context.
Outcome:	The proposed framework outperforms existing speech evaluation and analysis systems on a human-annotated test set.

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

Copied to clipboard

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.

LLM as a metric critic for low resource relation identification (2024.findings-emnlp)

Copied to clipboard

Zhe Yang, Yi Huang, Yaqin Chen, Xiaoting Wu, Junlan Feng, Chao Deng

Information Retrieval

Challenge:	Existing studies show that small language models (SLMs) overfit in low resource situations . however, the gap between pre-training and fine-tuning leads to performance decay .
Approach:	They propose to combine large language models and LLM for relation identification by co-evolution . they propose to use a masked language model prompt to generate a relation identification task .
Outcome:	The proposed model can handle low resource relation identification tasks with minimal overfitting . the proposed model provides essential background knowledge to assist training process .

Vision-Flan: Scaling Human-Labeled Tasks in Visual Instruction Tuning (2024.findings-acl)

Copied to clipboard

Zhiyang Xu, Chao Feng, Rulin Shao, Trevor Ashby, Ying Shen, Di Jin, Yu Cheng, Qifan Wang, Lifu Huang

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

Challenge:	Recent vision-language models (VLMs) have shown impressive capabilities as general visual assistants, but there are two challenges to their performance: (1) lacking task diversity in pretraining and visual instruction tuning; (2) annotation error and bias in GPT-4 synthesized instruction tuning data.
Approach:	They propose a two-stage instruction tuning framework that fine tunes VLMs firstly and further tuned on GPT-4 synthesized data.
Outcome:	The proposed framework outperforms the traditional single-stage visual instruction tuning framework and achieves state-of-the-art performance across a wide range of multi-modal evaluation benchmarks.

Copied to clipboard

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

Explainability & Interpretability in NLP

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

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

Copied to clipboard

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

LightRAG: Simple and Fast Retrieval-Augmented Generation (2025.findings-emnlp)

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.

Copied to clipboard

Zirui Guo, Lianghao Xia, Yanhua Yu, Tu Ao, Chao Huang

Green & Sustainable NLP

Challenge:	Existing RAG systems rely on flat data representations and inadequate contextual awareness . lightRAG framework incorporates graph structures into text indexing and retrieval processes .
Approach:	LightRAG is a framework that integrates graph structures into text indexing and retrieval processes.
Outcome:	The proposed framework incorporates graph structures into text indexing and retrieval processes.

“Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding (2021.findings-emnlp)

Copied to clipboard

Faeze Brahman, Meng Huang, Oyvind Tafjord, Chao Zhao, Mrinmaya Sachan, Snigdha Chaturvedi

Challenge:	Existing studies on character-centric understanding of narratives focus on understanding the characters in the narrative, but these studies are limited to understanding only certain aspects of characters.
Approach:	They propose a dataset of literary pieces and their summaries paired with descriptions of characters that appear in them that are used to facilitate character-centric narrative understanding.
Outcome:	The proposed dataset includes literary pieces and their summaries paired with descriptions of characters that appear in them.

Revisiting a Pain in the Neck: A Semantic Reasoning Benchmark for Language Models (2026.acl-long)

Copied to clipboard

Yang Liu, Hongming Li, Melissa Xiaohui Qin, Chao Huang, Qiankun Liu

Improving Time Sensitivity for Question Answering over Temporal Knowledge Graphs (2022.acl-long)

Challenge:	Semantic phrases (SP) are lexical combinations whose meanings or usages may not be fully derived from their individual components.
Approach:	They propose to consolidate existing multiword expression resources into a unified testbed to assess language models in semantic phrase processing tasks.
Outcome:	The evaluation suite covers idiomatic expressions, noun compounds, and verbal constructions.

Copied to clipboard

Chao Shang, Guangtao Wang, Peng Qi, Jing Huang

Semantic Text Processing

Challenge:	Temporal knowledge graphs record entity relations and when they occur in time . previous work fails to address time-related challenges such as time-order issues . paper proposes time-sensitive question answering framework to address these problems .
Approach:	They propose a time-sensitive question answering framework that uses temporal KGs to answer natural language questions.
Outcome:	The proposed framework outperforms the state-of-the-art on a new benchmark for question answering over temporal knowledge graphs.

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

Copied to clipboard

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 .

Migician: Revealing the Magic of Free-Form Multi-Image Grounding in Multimodal Large Language Models (2025.findings-acl)

Copied to clipboard

You Li, Heyu Huang, Chi Chen, Kaiyu Huang, Chao Huang, Zonghao Guo, Zhiyuan Liu, Jinan Xu, Yuhua Li, Ruixuan Li, Maosong Sun

OpenPhone: Mobile Agentic Foundation Models (2026.findings-acl)

Challenge:	Existing MLLMs still struggle to achieve precise grounding in multi-image scenarios.
Approach:	They propose a Chain-of-Thought framework that integrates single-image grounding with multi-image comprehension to address this challenge.
Outcome:	The proposed model outperforms existing models in multi-image grounding tasks by 24.94% and surpasses larger 70B models.

Copied to clipboard

Yangqin Jiang, Chao Huang

Green & Sustainable NLP

Challenge:	Mobile GUI agents face a critical dilemma: on-device models (4B or smaller) lack sufficient performance, while capable models are either too large for mobile deployment or prohibitively costly.
Approach:	They propose a mobile GUI agent system that leverages device-cloud collaboration to tap cost-efficiency of on-device models and high capability of cloud models.
Outcome:	The proposed system matches or nears larger models with reduced cloud costs on mobile platforms.

EasyRec: Simple yet Effective Language Models for Recommendation (2025.emnlp-main)

Copied to clipboard

Xubin Ren, Chao Huang

Self-Adjust Softmax (2025.emnlp-main)

Challenge:	Existing methods for learning from user-item interaction data rely on unique user and item IDs, which limits their performance in zero-shot learning scenarios.
Approach:	They propose an approach that integrates text-based semantic understanding with collaborative signals.
Outcome:	The proposed approach outperforms state-of-the-art models in zero-shot recommendation scenarios.

Copied to clipboard

Chuanyang Zheng, Yihang Gao, Guoxuan Chen, Han Shi, Jing Xiong, Xiaozhe Ren, Chao Huang, Zhenguo Li, Yu Li

Semantic Text Processing

Challenge:	Usually, tokens with larger attention scores are important for the final prediction.
Approach:	They propose to modify softmax(z) to z softmax and its normalized variant to improve the Transformer attention mechanism by making minor adjustments to the softmax function.
Outcome:	The proposed model provides enhanced gradient properties compared to the vanilla softmax function.

AnyGraph: Graph Foundation Model in the Wild (2026.findings-acl)

Copied to clipboard

Lianghao Xia, Chao Huang

Structured Data in NLP

Challenge:	Existing graph learning models struggle to extract generalizable insights from heterogeneous graph data, requiring extensive fine-tuning and limiting versatility across domains.
Approach:	They propose a graph foundation model that can handle key challenges such as Structure Heterogenity and Feature Heterogenicity.
Outcome:	The proposed model can handle key challenges such as structure heterogeneity, Feature heterogenity and fast adaptation across domains.

Aria-UI: Visual Grounding for GUI Instructions (2025.findings-acl)

Copied to clipboard

Yuhao Yang, Yue Wang, Dongxu Li, Ziyang Luo, Bei Chen, Chao Huang, Junnan Li

Revisiting Generative Commonsense Reasoning: A Pre-Ordering Approach (2022.findings-naacl)

Challenge:	Using a multimodal model, GUI agents can ground from language instructions to target elements . relying on HTML or AXTree inputs is a challenge for GUI agents .
Approach:	They propose a large multimodal model specifically designed for GUI grounding that adopts a pure vision approach instead of auxiliary inputs.
Outcome:	The proposed model outperforms vision-only and AXTree-reliant models on offline and online agents.

Copied to clipboard

Chao Zhao, Faeze Brahman, Tenghao Huang, Snigdha Chaturvedi