Papers by Jing Huang

Adaptive Policy with Wait-k Model for Simultaneous Translation (2023.emnlp-main)

Challenge:	Existing retrieval methods neglect the execution sequence structures inherent in procedural documents.
Approach:	They propose a retrieval model which integrates procedural graphs with document representations.
Outcome:	The proposed model integrates procedural graphs with document representations to improve document retrieval.

Copied to clipboard

Libo Zhao, Kai Fan, Wei Luo, Wu Jing, Shushu Wang, Ziqian Zeng, Zhongqiang Huang

Machine Translation

Challenge:	Existing approaches to simultaneous machine translation require a robust read/write policy . a standalone multi-path wait-k model performs competitively with adaptive policies .
Approach:	They propose a more flexible approach by decoupling the adaptive policy model from the translation model.
Outcome:	The proposed approach outperforms baseline approaches in translation tasks.

Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs (P19-1)

Copied to clipboard

Ming Tu, Guangtao Wang, Jing Huang, Yun Tang, Xiaodong He, Bowen Zhou

Code-Switched Text Synthesis in Unseen Language Pairs (2023.findings-acl)

Challenge:	Existing models to tackle multi-hop reading comprehension (RC) are focusing on a single document or paragraph, but they lack the ability to do reasoning across multiple documents.
Approach:	They propose a heterogeneous document-entity graph with different types of nodes and edges to solve multi-hop RC problem.
Outcome:	The proposed model can do reasoning over the proposed graph with nodes representation initialized with co-attention and self-attention based context encoders.

Copied to clipboard

I-Hung Hsu, Avik Ray, Shubham Garg, Nanyun Peng, Jing Huang

Multilinguality

Challenge:	Existing efforts on text synthesis for code-switching require training on code-witched texts in the target language pairs.
Approach:	They propose a model that synthesizes code-switched texts for language pairs absent from training data by adding an additional code-sharing module to a pre-trained machine translation model.
Outcome:	The proposed model synthesizes code-switched texts for language pairs lacking from training data.

ExPUNations: Augmenting Puns with Keywords and Explanations (2022.emnlp-main)

Copied to clipboard

Jiao Sun, Anjali Narayan-Chen, Shereen Oraby, Alessandra Cervone, Tagyoung Chung, Jing Huang, Yang Liu, Nanyun Peng

Explainability & Interpretability in NLP

Challenge:	Puns add the challenge of fusing commonsense and world knowledge with the ability to interpret lexical-semantic ambiguity.
Approach:	They propose to augment existing datasets with detailed crowdsourced annotations of puns, keywords and fine-grained funniness ratings to challenge current models' ability to understand and generate humor.
Outcome:	The proposed tasks include explanation generation to aid with pun classification and keyword-conditioned pun generation to challenge state-of-the-art models' ability to understand and generate humor.

pyvene: A Library for Understanding and Improving PyTorch Models via Interventions (2024.naacl-demo)

Copied to clipboard

Zhengxuan Wu, Atticus Geiger, Aryaman Arora, Jing Huang, Zheng Wang, Noah Goodman, Christopher Manning, Christopher Potts

Explainability & Interpretability in NLP

Challenge:	Existing libraries are often project-based, but pyvene provides a unified and extensible framework for performing interventions on neural models and sharing the intervened upon models with others.
Approach:	They propose an open-source Python library that supports customizable interventions on a range of different PyTorch modules.
Outcome:	The proposed framework provides a unified and extensible framework for performing interventions on neural models and sharing the intervened upon models with others.

Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models (2026.findings-acl)

Copied to clipboard

Hengyuan Zhang, Zhihao Zhang, Ercong Nie, Mingyang Wang, Zunhai Su, Yiwei Wang, Qianli Wang, Shuzhou Yuan, Xufeng Duan, Qibo Xue, Zeping Yu, Chenming Shang, Xiao Liang, Jing Xiong, Hui Shen, Chaofan Tao, Zhengwu Liu, Senjie Jin, Zhiheng Xi, Dongdong Zhang, Sophia Ananiadou, Tao Gui, Ruobing Xie, Hayden Kwok-Hay So, Hinrich Schuetze, Xuanjing Huang, Qi Zhang, Ngai Wong

Explainability & Interpretability in NLP

Challenge:	Existing literature on mechanistic interpretation (MI) treats it as an observational science, leaving practical applications underexplored.
Approach:	They propose a survey structured around the pipeline to identify and improve MI models.
Outcome:	The proposed framework enables tangible improvements in Alignment, Capability, and Efficiency.

An Empirical Study of LLM-as-a-Judge for LLM Evaluation: Fine-tuned Judge Model is not a General Substitute for GPT-4 (2025.findings-acl)

Copied to clipboard

Hui Huang, Xingyuan Bu, Hongli Zhou, Yingqi Qu, Jing Liu, Muyun Yang, Bing Xu, Tiejun Zhao

R2F: A General Retrieval, Reading and Fusion Framework for Document-level Natural Language Inference (2022.emnlp-main)

Challenge:	Recent studies have fine-tuned judge models based on open-source LLMs to evaluate the quality of other LLM.
Approach:	They propose to use open-source LLMs to evaluate Large Language Models (LLMs) their empirical results show that the models underperform GPT-4 in several dimensions .
Outcome:	The proposed models outperform GPT-4 on several dimensions including generalizability, fairness and adaptability.

Copied to clipboard

Hao Wang, Yixin Cao, Yangguang Li, Zhen Huang, Kun Wang, Jing Shao

RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representations (2024.acl-long)

Challenge:	Document-level natural language inference (DOCNLI) is a new task in natural language processing.
Approach:	They propose a document-level natural language inference framework that fuses sentence-level tasks into a set of sentence-based tasks.
Outcome:	The proposed framework improves interpretability and performance with evidence.

Copied to clipboard

Jing Huang, Zhengxuan Wu, Christopher Potts, Mor Geva, Atticus Geiger

AgentV-RL: Scaling Reward Modeling with Agentic Verifier (2026.findings-acl)

Challenge:	Existing methods to disentangle individual neurons from multiple high-level concepts are not yet benchmarked.
Approach:	They propose a method of Multi-task Distributed Alignment Search that allows to find distributed representations satisfying multiple causal criteria.
Outcome:	The proposed method achieves state-of-the-art on the target language model with Llama2-7B .

Copied to clipboard

Jiazheng Zhang, Ziche Fu, Zhiheng Xi, Wenqing Jing, Mingxu Chai, Wei He, Guoqiang Zhang, Chenghao Fan, Chenxin An, Wenxiang Chen, Zhicheng Liu, Haojie Pan, Dingwei Zhu, Tao Gui, Qi Zhang, Xuanjing Huang

Better Simultaneous Translation with Monotonic Knowledge Distillation (2023.acl-long)

Challenge:	Existing approaches to improve LLM reasoning are limited in complex domains and lack external grounding makes verifiers unreliable on computation-intensive tasks.
Approach:	They propose a framework that transforms reward modeling into a multi-turn, tool-augmented deliberative process.
Outcome:	The proposed framework surpasses state-of-the-art ORMs by 25.2% under parallel and sequential TTS.

Copied to clipboard

Shushu Wang, Jing Wu, Kai Fan, Wei Luo, Jun Xiao, Zhongqiang Huang

LLMs are Biased Evaluators But Not Biased for Fact-Centric Retrieval Augmented Generation (2025.findings-acl)

Challenge:	Existing methods to train offline MT models require generating target tokens before source sentence is fully consumed.
Approach:	They propose a method that leverages traditional translation models as teachers to generate monotonic yet accurate reference translations for sequence-level knowledge distillation.
Outcome:	The proposed approach improves on strong baselines and on a monotonic version of the WMT15 De-En test set.

Copied to clipboard

Yen-Shan Chen, Jing Jin, Peng-Ting Kuo, Chao-Wei Huang, Yun-Nung Chen

Reframe Your Life Story: Interactive Narrative Therapist and Innovative Moment Assessment with Large Language Models (2025.emnlp-main)

Challenge:	Recent studies have shown that large language models (LLMs) exhibit significant biases in evaluation tasks, especially in preferentially rating and favoring self-generated content.
Approach:	They propose to simulate two critical phases of retrieval-augmented generation (RAG) frameworks where keyword extraction and factual accuracy take precedence over stylistic elements.
Outcome:	The proposed model emulates two critical phases of the retrieval-augmented generation framework.

Copied to clipboard

Yi Feng, Jiaqi Wang, Wenxuan Zhang, Zhuang Chen, Shen Yutong, Xiyao Xiao, Minlie Huang, Liping Jing, Jian Yu

Modeling Evolution of Message Interaction for Rumor Resolution (2020.coling-main)

Challenge:	Existing approaches to mental health support lack realism and capture therapeutic progression over time.
Approach:	They propose a framework that simulates expert narrative therapists by planning therapeutic stages, guiding reflection levels, and generating contextually appropriate responses through retrieval-augmentation.
Outcome:	The proposed framework outperforms standard methods in quality and depth on 260 simulated clients and 230 human participants.

Copied to clipboard

Lei Chen, Zhongyu Wei, Jing Li, Baohua Zhou, Qi Zhang, Xuanjing Huang

Challenge:	Existing methods for rumor resolution ignore local interactions during the message diffusion which is important for the identification of rumors.
Approach:	They propose to model confrontation and reciprocity between message pairs via discrete variational autoencoders which effectively reflects the diversified opinion interactivity.
Outcome:	Experiments on a PHEME dataset show that the proposed model achieves higher accuracy than existing methods.

AdamMeme: Adaptively Probe the Reasoning Capacity of Multimodal Large Language Models on Harmfulness (2025.acl-long)

Copied to clipboard

Zixin Chen, Hongzhan Lin, Kaixin Li, Ziyang Luo, Zhen Ye, Guang Chen, Zhiyong Huang, Jing Ma

Towards Understanding Gender Bias in Relation Extraction (2020.acl-main)

Challenge:	Existing models that assess mLLMs on harmful meme understanding are inaccurate and lack accuracy.
Approach:	They propose a framework that adaptively probes the reasoning capabilities of mLLMs . their framework systematically reveals the varying performance of different target mllms a .
Outcome:	The proposed framework systematically reveals the performance of different target mLLMs.

Copied to clipboard

Andrew Gaut, Tony Sun, Shirlyn Tang, Yuxin Huang, Jing Qian, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, William Yang Wang

Information Extraction & Text Mining

Challenge:	Existing bias mitigation techniques have a negative effect on NRE, a study finds .
Approach:	They create a dataset to analyze gender bias in relation extraction systems . they find that existing bias mitigation techniques have a negative effect on NRE .
Outcome:	The proposed dataset analyzes gender bias in relation extraction systems using a 10% human annotated test set.

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

Copied to clipboard

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

Consistent Client Simulation for Motivational Interviewing-based Counseling (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.

Copied to clipboard

Yizhe Yang, Palakorn Achananuparp, Heyan Huang, Jing Jiang, Nicholas Gabriel Lim, Cameron Tan Shi Ern, Phey Ling Kit, Jenny Giam Xiuhui, John Pinto, Ee-Peng Lim

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

Challenge:	Existing approaches to simulate human clients in mental health counseling are limited and cost prohibitive.
Approach:	They propose a framework that supports consistent client simulation for mental health counseling by tracking the mental state of a simulated client, controlling its state transitions, and generating for each state behaviors consistent with the client’s motivation, beliefs, preferred plan to change, and and receptivity.
Outcome:	The proposed framework can simulate human clients for mental health counseling tasks and achieve higher consistency than previous methods.

Copied to clipboard

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

Act-Adaptive Margin: Dynamically Calibrating Reward Models for Subjective Ambiguity (2026.acl-long)

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

Copied to clipboard

Feiteng Fang, Dingwei Chen, Xiang Huang, Ting-En Lin, Yuchuan Wu, Xiong Liu, Jing Ye, Ziqiang Liu, Haonan Zhang, Liang Zhu, Hamid Alinejad-Rokny, Min Yang, Yongbin Li

Challenge:	Existing approaches to reward modeling in reinforcement learning tasks are limited when dealing with ambiguous preferences.
Approach:	They propose to use AAM to dynamically calibrate preference margins using the Bradley-Terry model's internal parameter knowledge to improve reward modeling in subjective tasks.
Outcome:	The proposed approach improves reward modeling by dynamically calibrating preference margins using the model’s internal parameter knowledge.

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

Copied to clipboard

Yingqian Cui, Pengfei He, Jingying Zeng, Hui Liu, Xianfeng Tang, Zhenwei Dai, Yan Han, Chen Luo, Jing Huang, Zhen Li, Suhang Wang, Yue Xing, Jiliang Tang, Qi He

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

Challenge:	Chain-of-Thought (CoT) reasoning has improved the performance of large language models (LLMs) however, the detailed reasoning process in CoT often incurs long generation times and high computational costs due to the inclusion of unnecessary steps.
Approach:	They propose a method to identify critical reasoning steps using perplexity as a measure of their importance.
Outcome:	The proposed method achieves a better balance between reasoning accuracy and efficiency of CoT.

Copied to clipboard

MIThinker: A Plug-and-Play Policy-Optimized Thinker For Motivational Interviewing Counseling (2026.findings-acl)

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.

Copied to clipboard

Yizhe Yang, Palakorn Achananuparp, Heyan Huang, Jing Jiang, Ee-Peng Lim

TRIGO: Benchmarking Formal Mathematical Proof Reduction for Generative Language Models (2023.emnlp-main)

Challenge:	Existing reasoning large language models (LLMs) generate responses without explicitly aligning thoughts with counseling techniques, limiting their effectiveness.
Approach:	They propose a lightweight thinking model that generates therapeutic thoughts to guide MI counseling agents in strategy selection and response generation.
Outcome:	The proposed model achieves theory-of-mind assessment comparable to state-of the-art systems with an order of magnitude less computation.

Copied to clipboard

Jing Xiong, Jianhao Shen, Ye Yuan, Haiming Wang, Yichun Yin, Zhengying Liu, Lin Li, Zhijiang Guo, Qingxing Cao, Yinya Huang, Chuanyang Zheng, Xiaodan Liang, Ming Zhang, Qun Liu

Trajectory2Task: Training Robust Tool-Calling Agents with Synthesized Yet Verifiable Data for Complex User Intents (2026.acl-long)

Challenge:	Automated theorem proving (ATP) benchmarks focus on symbolic inference but rarely involve understanding complex number combination reasoning.
Approach:	They propose a benchmark that requires a model to reduce a trigonometric expression with step-by-step proof and evaluates a generative LM’s reasoning ability on formulas and ability to manipulate, group, and factor number terms.
Outcome:	The proposed benchmark evaluates a generative LM’s reasoning ability on formulas and ability to manipulate, group, and factor number terms.

Copied to clipboard

Ziyi Wang, Yuxuan Lu, Yimeng Zhang, Pei Chen, Ziwei Dong, Jing Huang, Jiri Gesi, Xianfeng Tang, Chen Luo, Qun Liu, Yisi Sang, Hanqing Lu, Manling Li, Jin Lai, Dakuo Wang

EDDA: An Encoder-Decoder Data Augmentation Framework for Zero-Shot Stance Detection (2024.lrec-main)

Challenge:	Tool-calling agents are increasingly deployed in real-world customer-facing workflows . but most studies on tool-callers focus on idealized settings with general, fixed, and well-specified tasks.
Approach:	They propose a tool-calling agent-based data pipeline that converts trajectories into user-facing tasks with controlled intent adaptations.
Outcome:	The proposed pipeline can be used to study tool use under three scenarios.

Copied to clipboard

Daijun Ding, Li Dong, Zhichao Huang, Guangning Xu, Xu Huang, Bo Liu, Liwen Jing, Bowen Zhang

CodeHacker: Automated Test Case Generation for Detecting Vulnerabilities in Competitive Programming Solutions (2026.acl-long)

Challenge:	Existing methods for enhancing text or data are limited by lack of logical connections between generated texts and training data.
Approach:	They propose an encoder-decoder data augmentation framework that combines large language models and chain-of-thought prompting to summarize texts into target-specific if-then rationales, establishing logical relationships.
Outcome:	The proposed framework significantly improves over state-of-the-art methods on benchmark datasets while enabling interpretable rationale-based learning.

Copied to clipboard

Jingwei Shi, Xinxiang Yin, Jing Huang, Shengyu Tao, Jinman Zhao

Robustness in NLP

Challenge:	Existing benchmarks for Large Language Models often lack coverage for subtle corner cases . a substantial amount of effort has been applied to address this challenge .
Approach:	They propose a framework that generates adversarial test cases that expose latent vulnerabilities in code submissions.
Outcome:	The proposed framework improves the True Negative Rate (TNR) of existing datasets and generates superior adversarial cases on liveCodeBench.

Exploring and Adapting Chinese GPT to Pinyin Input Method (2022.acl-long)

Copied to clipboard

Minghuan Tan, Yong Dai, Duyu Tang, Zhangyin Feng, Guoping Huang, Jing Jiang, Jiwei Li, Shuming Shi

VLSBench: Unveiling Visual Leakage in Multimodal Safety (2025.acl-long)

Challenge:	a frozen GPT can generate state-of-the-art performance on perfect pinyin, but performance drops when input includes abbreviated pinyan, which links to even larger number of Chinese characters.
Approach:	They propose to use Chinese GPT to generate fluent sentences using abbreviated pinyin.
Outcome:	The proposed approach improves on abbreviated pinyin across all domains.

Copied to clipboard

Xuhao Hu, Dongrui Liu, Hao Li, Xuanjing Huang, Jing Shao

DISCO Balances the Scales: Adaptive Domain- and Difficulty-Aware Reinforcement Learning on Imbalanced Data (2025.findings-emnlp)

Challenge:	Existing studies show that textual unlearning does not achieve comparable safety performance with image-text alignment.
Approach:	They propose to use textual unlearning to align MLLMs with image-text pairs to explain this problem . they construct a visual leakless safety bench with 2.2k image- text pairs to test this problem.
Outcome:	The proposed model can refuse image-text pairs according to textual queries, leading to unreliable safety evaluations.

Copied to clipboard

Yuhang Zhou, Jing Zhu, Shengyi Qian, Zhuokai Zhao, Xiyao Wang, Xiaoyu Liu, Ming Li, Paiheng Xu, Wei Ai, Furong Huang

Analyzing the Intensity of Complaints on Social Media (2022.findings-naacl)

Challenge:	Large Language Models (LLMs) are increasingly aligned with human preferences through Reinforcement Learning from Human Feedback (RLHF).
Approach:	a new study proposes a domain-informed self-consistency policy optimization extension to GRPO that addresses inter-group imbalance.
Outcome:	a new extension of GRPO addresses inter-group imbalance with two key innovations . the proposed method outperforms existing GR PO variants by 5% on Qwen3 models .

Copied to clipboard

Ming Fang, Shi Zong, Jing Li, Xinyu Dai, Shujian Huang, Jiajun Chen

Cross-Lingual Transfer

Challenge:	Prior studies on identifying the existence or the type of complaints focus on building automatic classification models for identifying complaints.
Approach:	They propose to measure the intensity of complaints from text using Best-Worst Scaling method to estimate the popularity of posts on social media.
Outcome:	The proposed model can estimate the popularity of complaints on social media with best-worst scaling (BWS) method.

Speaker Verification in Agent-generated Conversations (2024.acl-long)

Copied to clipboard

Yizhe Yang, Palakorn Achananuparp, Heyan Huang, Jing Jiang, Ee-Peng Lim

Dialogue Systems & Conversational Agents

Challenge:	Recent advances in large language models have increased the capabilities of conversational AI to solve challenging dialogue problems.
Approach:	They propose a task to verify whether two sets of utterances originate from the same speaker.
Outcome:	The proposed task aims to verify whether two sets of utterances originate from the same speaker.

Graph Ensemble Learning over Multiple Dependency Trees for Aspect-level Sentiment Classification (2021.naacl-main)

Copied to clipboard

Xiaochen Hou, Peng Qi, Guangtao Wang, Rex Ying, Jing Huang, Xiaodong He, Bowen Zhou

Sentiment Analysis

Challenge:	Recent work on aspect-level sentiment classification has shown that syntactic information is effective in capturing long-range syntaktic relations that are obscure from the surface form.
Approach:	They propose a graph ensemble technique that integrates syntactic structures with GNNs to better leverage syntaktic information in the face of parsing errors.
Outcome:	The proposed model outperforms models with single dependency tree and beats other models without adding model parameters.

Value Compass Benchmarks: A Comprehensive, Generative and Self-Evolving Platform for LLMs’ Value Evaluation (2025.acl-demo)

Copied to clipboard

Jing Yao, Xiaoyuan Yi, Shitong Duan, Jindong Wang, Yuzhuo Bai, Muhua Huang, Yang Ou, Scarlett Li, Peng Zhang, Tun Lu, Zhicheng Dou, Maosong Sun, James Evans, Xing Xie

Challenge:	Current evaluation methods for large language models face two key challenges: 1. evaluation validity and 2. Result interpretation reduce the pluralistic and incommensurable values to one-dimensional scores.
Approach:	They propose a platform for comprehensive value diagnosis of large language models (LLMs) that provides a generative evaluation paradigm that automatically creates real-world test items co-evolving with ever-advancing LLMs.
Outcome:	The proposed platform provides a framework for comprehensive value diagnosis of large language models (LLMs) with fine-grained scores and case studies across 27 value dimensions for 33 leading LLMs, customized comparisons, and visualized analysis of LLM’s alignment with cultural values.

PHMOSpell: Phonological and Morphological Knowledge Guided Chinese Spelling Check (2021.acl-long)

Copied to clipboard

Li Huang, Junjie Li, Weiwei Jiang, Zhiyu Zhang, Minchuan Chen, Shaojun Wang, Jing Xiao

Syntactic Text Processing

Challenge:	False gram and phonological errors make Chinese spelling check difficult . a novel end-to-end trainable model outperforms existing methods .
Approach:	They propose a trainable Chinese spelling check model that integrates phonological and visual information into a pre-trained language model.
Outcome:	The proposed model outperforms existing state-of-the-art models on three benchmarks.

Improving Event Coreference Resolution by Learning Argument Compatibility from Unlabeled Data (N19-1)

Copied to clipboard

Yin Jou Huang, Jing Lu, Sadao Kurohashi, Vincent Ng

Information Extraction & Text Mining

Challenge:	Argument compatibility is a linguistic condition that is often used in event coreference resolution systems.
Approach:	They propose a transfer learning framework that uses unlabeled data to learn argument compatibility of event mentions.
Outcome:	The proposed model improves the performance of the overall event coreference model on the English dataset.

AlignedCoT: Prompting Large Language Models via Native-Speaking Demonstrations (2024.findings-emnlp)

Copied to clipboard

Zhicheng Yang, Yinya Huang, Jing Xiong, Liang Feng, Xiaodan Liang, Yiwei Wang, Jing Tang

ProRank: Prompt Warmup via Reinforcement Learning for Small Language Models Reranking (2026.findings-acl)

Challenge:	Existing LLMs are delicate and elusive in prompt words and styles.
Approach:	They propose an LLM-acquainted prompting technique that includes proficient "native-speaking" they propose to use in-context learning to prompt LLMs to perform high-performance reasoning .
Outcome:	The proposed technique achieves step-wise prompts in zero-shot scenarios while maintaining the prompt quality.

Copied to clipboard

Xianming LI, Aamir Shakir, Rui Huang, Julius Lipp, Benjamin Clavié, Jing Li

PlanRAG-Audio: Planning and Retrieval Augmented Generation for Long-form Audio Understanding (2026.findings-acl)

Challenge:	Recent Large Language Models (LLMs) have demonstrated remarkable performance in document reranking tasks.
Approach:	They propose a two-stage training approach for document reranking using reinforcement learning and fine-grained score learning.
Outcome:	The proposed approach outperforms open-source and proprietary reranking models on BEIR benchmark.

Copied to clipboard

Masao Someki, Chien-yu Huang, Siddhant Arora, Samuele Cornell, Markus Müller, Nathan Susanj, Rupak Vignesh Swaminathan, Grant Strimel, Jing Liu, Shinji Watanabe

Improve Speech Translation Through Text Rewrite (2025.coling-industry)

Challenge:	Long-form audio understanding poses significant challenges due to the extreme length of audio sequences and the need to reason over heterogeneous acoustic cues distributed over time.
Approach:	They propose a retrieval-augmented generation framework for scalable long-form audio understanding . planRAG-Audio explicitly plans which modalities and temporal spans are required for a given query .
Outcome:	Experiments show that planRAG-Audio reduces the length of inputs for long-form audio models . the proposed framework can efficiently reason over long-term speech data .

Copied to clipboard

Jing Wu, Shushu Wang, Kai Fan, Wei Luo, Minpeng Liao, Zhongqiang Huang

Text Generation

Challenge:	Recent advances in speech translation (ST) research have focused on the unique characteristics of spontaneous speech, including accents and presentation quality.
Approach:	They propose to transform transcribed speech into a cleaner style more in line with the expectations of translation models built from written text.
Outcome:	Experiments on public and in-house translation models show that the proposed model can be effectively distilled into a standalone translation model.

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

Copied to clipboard

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

Neural Math Word Problem Solver with Reinforcement Learning (C18-1)

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

Copied to clipboard

Danqing Huang, Jing Liu, Chin-Yew Lin, Jian Yin

Discrete Argument Representation Learning for Interactive Argument Pair Identification (2021.naacl-main)

Challenge:	Existing models for solving math word problems rely on predefined rules or feature engineering.
Approach:	They propose to incorporate copy and alignment mechanism into the sequence-to-sequence model to address two shortcomings . they use model output as a feature and incorporate it into the feature-based model to explore the effectiveness .
Outcome:	The proposed model outperforms the state-of-the-art models on the problem solving task.

Copied to clipboard

Lu Ji, Zhongyu Wei, Jing Li, Qi Zhang, Xuanjing Huang

Representation Learning

Challenge:	Existing research on monological argumentation covers claims generation, argument structure prediction, and essay scoring.
Approach:	They propose to identify argument pairs from two posts with opposite stances to a certain topic.
Outcome:	The proposed framework outperforms competing models on a large-scale dataset . it also proves that it is useful for analyzing argument pairs from two posts .

Culturally Aware Natural Language Inference (2023.findings-emnlp)

Copied to clipboard

Jing Huang, Diyi Yang

OPeRA: A Dataset of Observation, Persona, Rationale, and Action for Evaluating LLMs on Human Online Shopping Behavior Simulation (2026.acl-long)

Challenge:	Cultural norms are behavioral rules and conventions shared within specific groups, connecting cultural symbols and values.
Approach:	They propose a task that operationalizes cultural variations in language understanding through a natural language inference task that surfaces cultural variations as label disagreement between annotators from different cultural groups.
Outcome:	The proposed model can be evaluated at which levels it is culturally aware.

Copied to clipboard

Ziyi Wang, Yuxuan Lu, Wenbo Li, Amirali Amini, Bo Sun, Yakov Bart, Weimin Lyu, Jiri Gesi, Tian Wang, Jing Huang, Yu Su, Upol Ehsan, Malihe Alikhani, Toby Jia-Jun Li, Lydia Chilton, Dakuo Wang

Challenge:	evaluating LLMs' ability to mimic real user behavior remains an open challenge due to the lack of high-quality, publicly available datasets that capture both the observable actions and the internal reasoning of an actual user.
Approach:	They propose a dataset of Observation, Persona, Rationale, and Action collected from real human participants during online shopping sessions.
Outcome:	The proposed dataset is the first to evaluate how well current LLMs can accurately simulate the next web action of a specific user.

PragmatiCQA: A Dataset for Pragmatic Question Answering in Conversations (2023.findings-acl)

Copied to clipboard

Peng Qi, Nina Du, Christopher Manning, Jing Huang

Natural Language Interfaces

Challenge:	Mars? - PragmatiCQA
Approach:	Mars? - The Paper .
Outcome:	The proposed dataset features 6873 QA pairs that explores pragmatic reasoning in conversations over a diverse set of topics.

Towards Low-Resource Harmful Meme Detection with LMM Agents (2024.emnlp-main)

Copied to clipboard

Jianzhao Huang, Hongzhan Lin, Liu Ziyan, Ziyang Luo, Guang Chen, Jing Ma

MLeVLM: Improve Multi-level Progressive Capabilities based on Multimodal Large Language Model for Medical Visual Question Answering (2024.findings-acl)

Challenge:	Existing methods for harmful meme detection are limited due to the dynamic nature of memes . eliciting knowledge-revising behavior within the LMM agent is a key factor in achieving this goal .
Approach:	They propose an agency-driven framework for low-resource harmful meme detection . they use annotated memes to leverage label information as auxiliary signals for model .
Outcome:	The proposed framework achieves superior performance than state-of-the-art methods on the low-resource harmful meme detection task.

Copied to clipboard

Dexuan Xu, Yanyuan Chen, Jieyi Wang, Yue Huang, Hanpin Wang, Zhi Jin, Hongxing Wang, Weihua Yue, Jing He, Hang Li, Yu Huang

CPO: Addressing Reward Ambiguity in Role-playing Dialogue via Comparative Policy Optimization (2025.findings-emnlp)

Challenge:	Existing MVQA models ignore multi-level progressive capabilities due to unspecific data and plain architecture.
Approach:	They propose a multi-level visual language model for medical visual question answering (MVQA) which covers multi- level questions and answers as well as reasoning processes from visual clues to semantic cognition.
Outcome:	The proposed model outperforms existing medical multimodal large language models on a multi-level instruction dataset and a feature alignment module.

Copied to clipboard

Jing Ye, Rui Wang, Yuchuan Wu, Victor Ma, Feiteng Fang, Fei Huang, Yongbin Li

Dialogue Systems & Conversational Agents

Challenge:	Comparative Policy Optimization (CPO) redefines the reward evaluation paradigm by shifting from sample-wise scoring to comparative group-wise score.
Approach:	They propose a method to optimize subjective tasks by shifting from sample-wise to comparative group-wise scoring.
Outcome:	The proposed framework shifts from sample-wise scoring to comparative group-wise score . it minimizes contextual bias and enables more robust and fair performance evaluation.

S2-MAD: Breaking the Token Barrier to Enhance Multi-Agent Debate Efficiency (2025.naacl-long)

Copied to clipboard

Yuting Zeng, Weizhe Huang, Lei Jiang, Tongxuan Liu, XiTai Jin, Chen Tianying Tiana, Jing Li, Xiaohua Xu

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

Challenge:	Large language models exhibit limitations when handling complex mathematical reasoning and logical inference tasks.
Approach:	They propose a sparsification strategy to reduce token costs within Multi-agent Debate (MAD) this strategy minimizes ineffective exchanges of information and unproductive discussions among agents .
Outcome:	The proposed approach reduces token costs by up to 94.5% while maintaining performance degradation below 2.0%.

Copied to clipboard

Boli Chen, Xin Huang, Lin Xiao, Liping Jing

ConEC: Earnings Call Dataset with Real-world Contexts for Benchmarking Contextual Speech Recognition (2024.lrec-main)

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

Copied to clipboard

Ruizhe Huang, Mahsa Yarmohammadi, Jan Trmal, Jing Liu, Desh Raj, Leibny Paola Garcia, Alexei V. Ivanov, Patrick Ehlen, Mingzhi Yu, Dan Povey, Sanjeev Khudanpur

Inducing Character-level Structure in Subword-based Language Models with Type-level Interchange Intervention Training (2023.findings-acl)

Challenge:	Existing work on contextual speech recognition (ASR) systems focuses on recognizing words that are not frequently seen in training data, such as rare words, but word error rate on rare words remains over 20%.
Approach:	They propose to use public-domain earnings calls and supplementary materials to evaluate contextual ASR approaches grounded on real-world applications.
Outcome:	The proposed frameworks are noisier than artificially synthesized contexts that contain the ground truth, yet still make great room for future improvement of contextual ASR technology.

Copied to clipboard

Jing Huang, Zhengxuan Wu, Kyle Mahowald, Christopher Potts

Prompting Vision-Language Models For Aspect-Controlled Generation of Referring Expressions (2024.findings-naacl)

Challenge:	Language models operating on subword units are challenging for character-level manipulations, authors say . authors develop a framework to learn robust character representations inside subword-based models .
Approach:	They propose a causal intervention framework to learn robust character representations inside subword-based language models.
Outcome:	The proposed model outperforms character-level models on more complex tasks . it improves robustness on unseen token sequences and leads to human-interpretable representations of characters.

Copied to clipboard

Danfeng Guo, Sanchit Agarwal, Arpit Gupta, Jiun-Yu Kao, Emre Barut, Tagyoung Chung, Jing Huang, Mohit Bansal

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

Challenge:	Referring Expression Generation (REG) is the task of generating a descriptive caption that uniquely identifies a given target in the scene.
Approach:	They propose an Aspect-Controlled REG task which requires generating a referring expression conditioned on the input aspect(s) by changing the input input such as color, location, action etc.
Outcome:	The proposed model beats all prior works in the CIDEr score and achieves comparable performance to training with 100% of real data.

Copied to clipboard

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

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

Orthogonal Relation Transforms with Graph Context Modeling for Knowledge Graph Embedding (2020.acl-main)

Copied to clipboard

Yun Tang, Jing Huang, Guangtao Wang, Xiaodong He, Bowen Zhou

Identifying Unlearned Data in LLMs via Membership Inference Attacks (2025.emnlp-main)

Challenge:	Existing knowledge graph embeddings have improved the knowledge graph link prediction task, but complex relations such as N-to-1, 1-to-N and N- to-N remain challenging to predict.
Approach:	They propose to extend RotatE from 2D complex domain to high dimensional space with orthogonal transforms to model relations.
Outcome:	The proposed method improves on N-to-1, 1-to-N and N- to-N cases while maintaining the capability for modeling symmetric/anti-symmetric, inverse and compositional relations.

Copied to clipboard

Advit Deepak, Megan Mou, Jing Huang, Diyi Yang

LLMs Deceive Unintentionally: Emergent Misalignment in Dishonesty from Misaligned Samples to Biased Human-AI Interactions (2026.findings-acl)

Challenge:	Existing work evaluates approximate unlearning under a retrieval paradigm, where adversaries attempt to extract residual knowledge given partial information of the unlearning target.
Approach:	They propose a framework to evaluate unlearning membership attacks using member inference techniques to exploit the forget set.
Outcome:	The proposed framework assesses whether unlearning leaves behind detectable artifacts that can be exploited to infer membership in the forget set.

Copied to clipboard

Xuhao Hu, Wang Peng, Xiaoya Lu, Dongrui Liu, Xuanjing Huang, Jing Shao

Demystifying Verbatim Memorization in Large Language Models (2024.emnlp-main)

Challenge:	Existing studies have shown that LLMs finetuned on incorrect completions can exhibit harmful behaviors, which is called emergent misalignment.
Approach:	They investigate whether LLMs finetuned on incorrect completions can exhibit harmful behaviors . they find that 1% of misalignment data is sufficient to decrease honest behavior .
Outcome:	The proposed model can be misaligned on errors within narrow domains to exhibit harmful behaviors . the proposed model is able to exhibit dishonest behavior with only 10% biased user population .

Copied to clipboard

Jing Huang, Diyi Yang, Christopher Potts

DREAM: Disentangling Risks to Enhance Safety Alignment in Multimodal Large Language Models (2025.naacl-long)

Challenge:	Existing studies have shown that Large Language Models (LLMs) memorize long sequences verbatim, with serious copyright and privacy implications.
Approach:	They develop a framework to study verbatim memorization in a controlled setting by continuing pre-training from Pythia checkpoints with injected sequences.
Outcome:	The proposed framework creates a control model M () and a treatment model M with injected sequences.

Copied to clipboard

Jianyu Liu, Hangyu Guo, Ranjie Duan, Xingyuan Bu, Yancheng He, Shilong Li, Hui Huang, Jiaheng Liu, Yucheng Wang, Chenchen Jing, Xingwei Qu, Xiao Zhang, Pei Wang, Yanan Wu, Jihao Gu, Yangguang Li, Jianke Zhu

Multi-Agent-as-Judge: Aligning LLM-Agent-Based Automated Evaluation with Multi-Dimensional Human Evaluation (2026.acl-long)

Challenge:	Multimodal Large Language Models (MLLMs) pose unique safety challenges due to their integration of visual and textual data.
Approach:	They propose a method to disentangle risks through step-by-step reasoning within multimodal inputs.
Outcome:	The proposed approach improves safety alignment in MLLMs by fine-tuning and iterative Reinforcement Learning from AI feedback.

Copied to clipboard

Jiaju Chen, Yuxuan Lu, Xiaojie Wang, Huimin Zeng, Jing Huang, Jiri Gesi, Ying Xu, Bingsheng Yao, Dakuo Wang

Challenge:	Existing "LLM-as-a-judge" evaluation frameworks are limited by persona descriptions and are not generalizable to other tasks.
Approach:	They propose a framework that can automatically construct multiple evaluator personas with distinct dimensions from relevant text documents and instantiate LLM agents with the persona.
Outcome:	The proposed framework can believably simulate human evaluators . it extracts stakeholders' diverse perspectives from the provided research papers and constructs personas for the agents .

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

Copied to clipboard

Chao Shang, Guangtao Wang, Peng Qi, Jing Huang

Self-Evaluation of Large Language Model based on Glass-box Features (2024.findings-emnlp)

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.

Copied to clipboard

Hui Huang, Yingqi Qu, Jing Liu, Muyun Yang, Bing Xu, Tiejun Zhao, Wenpeng Lu

Sentiment Interpretable Logic Tensor Network for Aspect-Term Sentiment Analysis (2022.coling-1)

Challenge:	Existing evaluation methods rely on external evaluators, focusing on training and prompting strategies, but model-aware glass-box features are overlooked.
Approach:	They propose to use model-aware glass-box features to evaluate an LLM's output.
Outcome:	The proposed model-aware features are reliable quality indicators for self-evaluation on public benchmarks.

Copied to clipboard

Bowen Zhang, Xu Huang, Zhichao Huang, Hu Huang, Baoquan Zhang, Xianghua Fu, Liwen Jing

Pre-trained Semantic Interaction based Inductive Graph Neural Networks for Text Classification (2025.coling-main)

Challenge:	Aspect-term sentiment analysis (ATSA) is a fine-grained task that aims to infer the sentiment towards the given aspect-terms.
Approach:	They propose a novel ATSA method that is interpretable and has high accuracy . they propose SILTN, which is a neurosymbolic formalism, to improve the accuracy based on syntax knowledge distillation.
Outcome:	The proposed method is interpretable because it is a neurosymbolic formalism and a computational model that supports learning and reasoning about data with a differentiable first-order logic language.

Copied to clipboard

Shiyu Wang, Gang Zhou, Jicang Lu, Jing Chen, Ningbo Huang

Information Extraction & Text Mining

Challenge:	Existing methods for text classification have vanishing or exploding gradients when dealing with long sequences, making it difficult to handle long-distance dependencies.
Approach:	They propose a graph neural network based on pre-trained semantic interaction called PaSIG . they construct a text-word heterogeneity graph and use context representation capability .
Outcome:	The proposed model outperforms existing methods on five datasets and achieves state-of-the-art performance.

Multi-Stage Balanced Distillation: Addressing Long-Tail Challenges in Sequence-Level Knowledge Distillation (2024.findings-emnlp)

Copied to clipboard

Yuhang Zhou, Jing Zhu, Paiheng Xu, Xiaoyu Liu, Xiyao Wang, Danai Koutra, Wei Ai, Furong Huang

Self-Adjust Softmax (2025.emnlp-main)

Challenge:	Knowledge distillation (KD) is a promising solution for large language models, but their deployment remains computationally expensive.
Approach:	They propose a framework which iteratively balances training data within a fixed computational budget and enables the transfer of knowledge from expensive teacher LLMs to smaller student models.
Outcome:	The proposed framework achieves state-of-the-art performance across diverse long-tailed datasets, enhancing both the efficiency and efficacy of the distilled models.

Copied to clipboard

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

Unsupervised Melody-to-Lyrics Generation (2023.acl-long)

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.

Copied to clipboard

Yufei Tian, Anjali Narayan-Chen, Shereen Oraby, Alessandra Cervone, Gunnar Sigurdsson, Chenyang Tao, Wenbo Zhao, Tagyoung Chung, Jing Huang, Nanyun Peng

Low-Resource NLP

Challenge:	Existing methods for automatic melody-to-lyric generation are limited due to the limited amount of melody-lyrical aligned data.
Approach:	They propose a method for automatic melody-to-lyric generation without training on any aligned melody-lyr data.
Outcome:	The proposed model generates high-quality lyrics that are singable, intelligible, and coherent than baseline models.

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

Copied to clipboard

Lin Xiao, Xin Huang, Boli Chen, Liping Jing

CAMI: A Counselor Agent Supporting Motivational Interviewing through State Inference and Topic Exploration (2025.acl-long)

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

Copied to clipboard

Yizhe Yang, Palakorn Achananuparp, Heyan Huang, Jing Jiang, Phey Ling Kit, Nicholas Gabriel Lim, Cameron Tan Shi Ern, Ee-Peng Lim

Challenge:	Motivational Interviewing (MI) is a client-centered counseling technique designed to address ambivalence and facilitate behavior change in clients.
Approach:	They propose to use a STAR framework to evoke change talk by using large language models to assess MI skill competency, client’s state inference accuracy, topic exploration proficiency, and overall counseling success.
Outcome:	The proposed agent outperforms several state-of-the-art methods and shows more realistic counselor-like behavior.

Variance-reduced First-order Meta-learning for Natural Language Processing Tasks (2021.naacl-main)

Copied to clipboard

Lingxiao Wang, Kevin Huang, Tengyu Ma, Quanquan Gu, Jing Huang

RatE: Relation-Adaptive Translating Embedding for Knowledge Graph Completion (2020.coling-main)

Challenge:	Existing studies show that meta-learning can overfit to some specific adaptation when we have heterogeneous tasks.
Approach:	They propose to reduce the variance of the gradient estimator used in task adaptation by adding a new variance reduction term to the gradient estimation.
Outcome:	Experiments on few-shot text classification and multi-domain dialog state tracking show that the proposed method outperforms existing methods.

Copied to clipboard

Hao Huang, Guodong Long, Tao Shen, Jing Jiang, Chengqi Zhang

MMCode: Benchmarking Multimodal Large Language Models for Code Generation with Visually Rich Programming Problems (2024.findings-emnlp)

Challenge:	Existing approaches for knowledge graph embedding have limitations in complex vector space . embeddability of one-to-many relations is not explicitly alleviated .
Approach:	They propose a relation-adaptive translating embedding function that can be extended to complex vector space.
Outcome:	The proposed translation function improves expressive power and alleviates embedding ambiguity problem.

Copied to clipboard

Kaixin Li, Yuchen Tian, Qisheng Hu, Ziyang Luo, Zhiyong Huang, Jing Ma

Chain of Thought Prompting Elicits Knowledge Augmentation (2023.findings-acl)

Challenge:	Programming often involves translating detailed and complex specifications into code . current state-of-the-art models struggle to solve these problems, a new study shows .
Approach:	They propose a multi-modal coding dataset to evaluate algorithmic problem-solving skills in visually rich contexts.
Outcome:	The proposed model lacks powerful vision-code models due to the extreme demand for reasoning abilities.

Copied to clipboard

Dingjun Wu, Jing Zhang, Xinmei Huang

Context-Situated Pun Generation (2022.emnlp-main)

Challenge:	Existing knowledge augmentation methods require retrieving knowledge from external knowledge sources or developing a reasoner to leverage the logical rules within the external knowledge source.
Approach:	They propose a Chain-of-Thought-based method that augments knowledge for deep learning by removing the need for additional knowledge retrieval or knowledge reasoning models.
Outcome:	The proposed method outperforms both pure CoT-based methods and the non-augmented method across the majority of 11 publicly available benchmarks for various reasoning tasks.

Copied to clipboard

Jiao Sun, Anjali Narayan-Chen, Shereen Oraby, Shuyang Gao, Tagyoung Chung, Jing Huang, Yang Liu, Nanyun Peng

Saliency-based Multi-View Mixed Language Training for Zero-shot Cross-lingual Classification (2021.findings-emnlp)

Challenge:	a new task for context-situated pun generation uses a given context to generate puns . human evaluation shows that 69% of top retrieved pun words can be used to generate context-based puns.
Approach:	They propose a task where puns are generated based on contextual keywords and pun words.
Outcome:	The proposed system generates successful puns 31% of the time given a plausible tuple of context words and pun pairs.

Copied to clipboard

Siyu Lai, Hui Huang, Dong Jing, Yufeng Chen, Jinan Xu, Jian Liu

Multilinguality

Challenge:	Recent multilingual pre-trained models have been demonstrated effective in many cross-lingual tasks.
Approach:	They propose a framework that leverages code-switched data with multi-view learning to fine-tune XLM-R.
Outcome:	The proposed model achieves state-of-the-art on zero-shot cross-lingual sentiment classification and dialogue state tracking tasks.

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

Copied to clipboard

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

An Expert is Worth One Token: Synergizing Multiple Expert LLMs as Generalist via Expert Token Routing (2024.acl-long)

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

Copied to clipboard

Ziwei Chai, Guoyin Wang, Jing Su, Tianjie Zhang, Xuanwen Huang, Xuwu Wang, Jingjing Xu, Jianbo Yuan, Hongxia Yang, Fei Wu, Yang Yang

Challenge:	Large language models (LLMs) have demonstrated remarkable capabilities across a wide spectrum of tasks, but performance and reliability in certain specialized domains still fall short of expectations.
Approach:	They propose a unified generalist framework that facilitates seamless integration of multiple expert LLMs.
Outcome:	The proposed framework outperforms existing multi-LLM collaboration paradigms across six diverse expert domains.

MASTER: A Multi-Agent System with LLM Specialized MCTS (2025.naacl-long)

Copied to clipboard

Bingzheng Gan, Yufan Zhao, Tianyi Zhang, Jing Huang, Li Yusu, Shu Xian Teo, Changwang Zhang, Wei Shi

Challenge:	Large Language Models (LLMs) are increasingly being explored for problem-solving tasks . their strategic planning capability is often viewed with skepticism due to their limited planning capabilities.
Approach:	They propose a framework that coordinates agent recruitment and communication through LLM specialized MCTS.
Outcome:	The proposed framework achieves 76% accuracy on HotpotQA and 80% on WebShop . it relies on extensive sampling simulations to approximate the true reward distribution .

Contrastive Learning with Adversarial Examples for Alleviating Pathology of Language Model (2023.acl-long)

Copied to clipboard

Pengwei Zhan, Jing Yang, Xiao Huang, Chunlei Jing, Jingying Li, Liming Wang

Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in Large Language Models (2025.naacl-long)

Challenge:	Existing interpretation methods fail to obtain faithful attributions on these models, thereby failing to reveal potential flaws and biases.
Approach:	They propose a Contrastive learning regularization method which calibrates the sentence representation of out-of-distribution examples and utilizes adversarial examples to introduce direction information in regularization.
Outcome:	The proposed method alleviates the model pathology while impacting generalization ability on in-distribution examples and thus helps interpretation methods obtain more faithful results.

Copied to clipboard

Tongxuan Liu, Wenjiang Xu, Weizhe Huang, Yuting Zeng, Jiaxing Wang, Xingyu Wang, Hailong Yang, Jing Li