Papers by Rong Li

Joint Multi-modal Aspect-Sentiment Analysis with Auxiliary Cross-modal Relation Detection (2021.emnlp-main)

Challenge:	a toolkit for speech translation is available for free and provides step-by-step recipes for feature extraction, data preprocessing, distributed training, and evaluation.
Approach:	They propose to use NeurST to facilitate speech translation research for NLP researchers . they show experimental results for different benchmark datasets which can be regarded as reliable baselines .
Outcome:	The proposed framework provides reliable benchmarks for speech translation research.

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Xincheng Ju, Dong Zhang, Rong Xiao, Junhui Li, Shoushan Li, Min Zhang, Guodong Zhou

Sentiment Analysis

Challenge:	Existing studies on aspect-level sentiment analysis focus on extracting aspect terms and sentiment polarities separately.
Approach:	They propose a multi-modal joint learning approach with auxiliary cross-modal relation detection for multi-dimensional aspect-level sentiment analysis.
Outcome:	The proposed approach can obtain all aspect-level sentiment polarities dependent on the jointly extracted specific aspects.

Towards Controllable Speech Synthesis in the Era of Large Language Models: A Systematic Survey (2025.emnlp-main)

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Tianxin Xie, Yan Rong, Pengfei Zhang, Wenwu Wang, Li Liu

MAVEN: A Massive General Domain Event Detection Dataset (2020.emnlp-main)

Challenge:	Text-to-speech (TTS) has advanced from generating natural-sounding speech to enabling fine-grained control over speech attributes.
Approach:	They provide a review of controllable TTS methods from traditional control techniques to emerging approaches using natural language prompts.
Outcome:	The proposed methods are based on models, strategies, and features, and summarize challenges, datasets, and evaluations.

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Xiaozhi Wang, Ziqi Wang, Xu Han, Wangyi Jiang, Rong Han, Zhiyuan Liu, Juanzi Li, Peng Li, Yankai Lin, Jie Zhou

Information Extraction & Text Mining

Challenge:	Existing datasets exhibit data scarcity and limited coverage of general-domain events.
Approach:	They present a MAssive eVENt detection dataset which contains 4,480 Wikipedia documents and 168 event types.
Outcome:	The proposed dataset shows that existing methods cannot achieve promising results on the small datasets.

Enhancing Neural Models with Vulnerability via Adversarial Attack (2020.coling-main)

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Rong Zhang, Qifei Zhou, Bo An, Weiping Li, Tong Mo, Bo Wu

Robustness in NLP

Challenge:	Existing work on adversarial attack to improve performance of NLSM tasks has not been done.
Approach:	They propose a general two-stage training framework to enhance neural models with Vulnerability via adversarial attack.
Outcome:	The proposed framework improves neural models with Vulnerability via adversarial attack on NLSM datasets.

ContextBLIP: Doubly Contextual Alignment for Contrastive Image Retrieval from Linguistically Complex Descriptions (2024.findings-acl)

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Honglin Lin, Siyu Li, Guoshun Nan, Chaoyue Tang, Xueting Wang, Jingxin Xu, Rong Yankai, Zhouzhili Zhouzhili, Yutong Gao, Qimei Cui, Xiaofeng Tao

Visual Data in NLP

Challenge:	Existing approaches to image retrieval from contextual descriptions (IRCD) lag behind human performance in IRCD.
Approach:	They propose a method that relies on a doubly contextual alignment scheme for challenging IRCD.
Outcome:	The proposed method can yield comparable results with GPT-4V, despite fewer parameters.

Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents (2025.findings-emnlp)

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Long Li, Weiwen Xu, Jiayan Guo, Ruochen Zhao, Xingxuan Li, Yuqian Yuan, Boqiang Zhang, Yuming Jiang, Yifei Xin, Ronghao Dang, Yu Rong, Deli Zhao, Tian Feng, Lidong Bing

Challenge:	Existing methods for idea generation either trivially prompt LLMs or expose LLM to extensive literature without indicating useful information.
Approach:	They propose a chain-of-ideas agent that organizes literature in a chains structure . they propose evaluating idea-generation methods from different perspectives .
Outcome:	The proposed agent outperforms existing methods and matches human quality in idea generation.

Ciron: a New Benchmark Dataset for Chinese Irony Detection (2020.lrec-1)

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Rong Xiang, Xuefeng Gao, Yunfei Long, Anran Li, Emmanuele Chersoni, Qin Lu, Chu-Ren Huang

Sentiment Analysis

Challenge:	Automatic Chinese irony detection often lacks labeled benchmark datasets . despite its pervasive nature, irony is a trope whose actual meaning differs from what is literally enunciated.
Approach:	They propose to use a Chinese benchmark dataset for automatic Chinese irony detection to provide a benchmark for machine learning models.
Outcome:	The proposed dataset includes more than 8.7K posts, collected from Weibo, a micro blogging platform.

Counterfactual Adversarial Learning with Representation Interpolation (2021.findings-emnlp)

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Wei Wang, Boxin Wang, Ning Shi, Jinfeng Li, Bingyu Zhu, Xiangyu Liu, Rong Zhang

Robustness in NLP

Challenge:	Existing models with statistical bias are prone to memorized correlations . large pre-trained models such as BERT have revolutionized the model development paradigm in natural language processing .
Approach:	They propose a framework to tackle the problem from a causal perspective using a latent space interpolation approach.
Outcome:	Extensive experiments show that CAT achieves substantial performance improvement over SOTA across different downstream tasks, including sentence classification, natural language inference and question answering.

ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical Reasoning (2025.emnlp-main)

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Yu Sun, Xingyu Qian, Weiwen Xu, Hao Zhang, Chenghao Xiao, Long Li, Deli Zhao, Wenbing Huang, Tingyang Xu, Qifeng Bai, Yu Rong

Selecting Demonstrations for Many-Shot In-Context Learning via Gradient Matching (2025.findings-acl)

Challenge:	Existing medical reasoning datasets are limited in scale and typically rely on incomplete data.
Approach:	They propose to use ReasonMed to train medical reasoning models using a multi-agent generation, verification, and refinement pipeline.
Outcome:	The largest medical reasoning dataset to date surpasses the prior best sub-10B models by 4.17% and even exceeds LLaMA3.1-70B on PubMedQA by 4.60%.

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Jianfei Zhang, Bei Li, Jun Bai, Rumei Li, Yanmeng Wang, Chenghua Lin, Wenge Rong

Cross-modal Contrastive Learning for Speech Translation (2022.naacl-main)

Challenge:	In-Context Learning (ICL) empowers Large Language Models for rapid task adaptation without fine-tuning.
Approach:	They propose a method that aligns fine-tuning gradients between entire training set and selected examples to enable in-context learning and fine-uning.
Outcome:	The proposed method outperforms random selection on large LLMs from 4-shot to 128-shot scenarios across 9 datasets.

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Rong Ye, Mingxuan Wang, Lei Li

FinanceReasoning: Benchmarking Financial Numerical Reasoning More Credible, Comprehensive and Challenging (2025.acl-long)

Challenge:	Existing approaches for speech translation focus on using additional data from MT and automatic speech recognition (ASR).
Approach:	They propose a cross-modal contrastive learning method for end-to-end speech-totext translation.
Outcome:	The proposed method outperforms existing methods on a popular benchmark MuST-C.

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Zichen Tang, Haihong E, Ziyan Ma, Haoyang He, Jiacheng Liu, Zhongjun Yang, Zihua Rong, Rongjin Li, Kun Ji, Qing Huang, Xinyang Hu, Yang Liu, Qianhe Zheng

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

Challenge:	Compared to existing benchmarks, FinanceReasoning provides three key advancements: (1) credibility; (2) comprehensiveness; (3) numerical precision; (4) complexity; (5) complexity; and (6) complexity.
Approach:	They propose a benchmark to evaluate the reasoning capabilities of large reasoning models (LRMs) in financial numerical reasoning problems.
Outcome:	The proposed benchmark exceeds existing benchmarks in 67.8% of financial concepts and formulas and is credible, comprehensive, and challenging.

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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

PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development (2023.acl-demo)

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.

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Avi Sil, Jaydeep Sen, Bhavani Iyer, Martin Franz, Kshitij Fadnis, Mihaela Bornea, Sara Rosenthal, Scott McCarley, Rong Zhang, Vishwajeet Kumar, Yulong Li, Md Arafat Sultan, Riyaz Bhat, Juergen Bross, Radu Florian, Salim Roukos

Question Answering

Challenge:	Question Answering (QA) is a major area of research in Natural Language Processing (NLP)
Approach:	They propose a one-stop and open-source QA repository for question answering . it supports core QA functionalities like retrieval and reading comprehension . they say it will facilitate easy replication of state-of-the-art (SOTA) QA methods .
Outcome:	The proposed framework enables easy replication of state-of-the-art (SOTA) QA methods.

Multi-Agent Comedy Club: Investigating Community Discussion Effects on LLM Humor Generation (2026.findings-acl)

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Shiwei Hong, Lingyao Li, Ethan Z. Rong, Chenxinran Shen, Zhicong Lu

SumCSE: Summary as a transformation for Contrastive Learning (2024.findings-naacl)

Challenge:	Existing studies on the use of multi-turn interaction and feedback for LLM writing focus on prompts and localized feedback.
Approach:	They build a controlled multi-agent sandbox that instantiates a small standup comedy community and allows it to manipu-late whether public reception is generated, logged, and fed back into later rounds.
Outcome:	The proposed model improves craft/clarity and social response with occasional increases in aggressive humor.

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Raghuveer Thirukovalluru, Xiaolan Wang, Jun Chen, Shuyang Li, Jie Lei, Rong Jin, Bhuwan Dhingra

Disentangling Preference Representation and Text Generation for Efficient Individual Preference Alignment (2025.coling-main)

Challenge:	Sentence embedding models are typically trained using contrastive learning (CL) using human annotations directly or by repurposing other annotated datasets.
Approach:	They propose to use generative language models to generate CL data using annotated data.
Outcome:	The proposed method outperforms the previous best unsupervised method by 1.8 points and SimCSE, a strong supervised baseline by 0.3 points on the semantic text similarity (STS) benchmark.

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Jianfei Zhang, Jun Bai, Bei Li, Yanmeng Wang, Rumei Li, Chenghua Lin, Wenge Rong

EpLSA: Synergy of Expert-prefix Mixtures and Task-Oriented Latent Space Adaptation for Diverse Generative Reasoning (2024.lrec-main)

Challenge:	Human values are inherently diverse, making it insufficient to align LLMs solely with general preferences.
Approach:	They propose a flexible paradigm for individual preference alignment that disentangles preference representation from text generation in LLMs.
Outcome:	The proposed method produces aligned quality and better than PEFT-based methods while reducing training time for each new individual preference by 80% to 90%.

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Fujun Zhang, Xiangdong Su, Jiang Li, Rong Yan, Guanglai Gao

Pruning via Merging: Compressing LLMs via Manifold Alignment Based Layer Merging (2024.emnlp-main)

Challenge:	Existing models for diverse generative reasoning struggle to generate multiple unique and plausible results.
Approach:	They propose a model based on expert-prefix mixtures and task-oriented latent space adaptation for diverse generative reasoning.
Outcome:	The proposed model outperforms existing models on three types of generative reasoning tasks.

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Deyuan Liu, Zhanyue Qin, Hairu Wang, Zhao Yang, Zecheng Wang, Fangying Rong, Qingbin Liu, Yanchao Hao, Bo Li, Xi Chen, Cunhang Fan, Zhao Lv, Dianhui Chu, Zhiying Tu, Dianbo Sui

FocusLLM: Precise Understanding of Long Context by Dynamic Condensing (2025.acl-long)

Challenge:	Existing methods for parameter pruning fail to utilize the knowledge from pruned parameters.
Approach:	They propose a method that uses manifold learning and the Information Bottleneck measure to merge similar layers to preserve model performance.
Outcome:	The proposed method outperforms pruning methods on multiple datasets and LLMs with quantization and achieves substantial compression ratios.

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Zhenyu Li, Yike Zhang, Tengyu Pan, Yutao Sun, Zhichao Duan, Junjie Fang, Rong Han, Zixuan Wang, Jianyong Wang

Challenge:	Existing context condensing methods cannot accurately understand the full context, as there is a considerable amount of information loss in the condensed process.
Approach:	They propose a framework to extend the fixed context length of any decoder-only LLM by distilling crucial information from long sequences.
Outcome:	The proposed framework extends the fixed context length of any decoder-only LLM, allowing it to focus on relevant information from very long sequences.

How to Determine the Most Powerful Pre-trained Language Model without Brute Force Fine-tuning? An Empirical Survey (2023.findings-emnlp)

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Jun Bai, Xiaofeng Zhang, Chen Li, Hanhua Hong, Xi Xu, Chenghua Lin, Wenge Rong

Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching (2022.findings-naacl)

Challenge:	Transferability estimation has been a topic of great interest in computer vision fields . a lack of a comprehensive comparison between these estimation methods is a problem .
Approach:	They conduct a thorough survey of existing methods to find the most suitable model . they also outline difficulties of consideration of training details and applicability to text generation .
Outcome:	The proposed methods perform well with superiorities in effectiveness and efficiency.

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Kunbo Ding, Weijie Liu, Yuejian Fang, Zhe Zhao, Qi Ju, Xuefeng Yang, Rong Tian, Zhu Tao, Haoyan Liu, Han Guo, Xingyu Bai, Weiquan Mao, Yudong Li, Weigang Guo, Taiqiang Wu, Ningyuan Sun

Harnessing Large Language Models for Disaster Management: A Survey (2025.findings-acl)

Challenge:	Existing studies have shown that cross-lingual knowledge distillation can improve the performance of pre-trained models for cross-linguistic similarity matching tasks.
Approach:	They propose a multi-stage distillation framework for constructing a small-size but high-performance cross-lingual model using contrastive learning, bottleneck, and parameter recurrent strategies.
Outcome:	The proposed model can compress the size of XLM-R and MiniLM by more than 50% while the performance is only reduced by about 1%.

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Zhenyu Lei, Yushun Dong, Weiyu Li, Rong Ding, Qi R. Wang, Jundong Li

Joint Document-Level Event Extraction via Token-Token Bidirectional Event Completed Graph (2023.acl-long)

Challenge:	Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains, including their emerging role in mitigating threats to human life, infrastructure, and the environment during natural disasters.
Approach:	They propose a taxonomy that categorizes existing LLMs based on disaster phases and application scenarios to provide valuable insights for the research community and practitioners .
Outcome:	The proposed taxonomy categorizes existing LLMs based on disaster phases and application scenarios.

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Qizhi Wan, Changxuan Wan, Keli Xiao, Dexi Liu, Chenliang Li, Bolong Zheng, Xiping Liu, Rong Hu

Information Extraction & Text Mining

Challenge:	a joint exaction method can be used to extract document-level event records . it avoids inefficiency and error propagation issues in traditional pipeline methods .
Approach:	They propose a joint exaction method that can avoid inefficiency and error propagation issues . they propose eType-Role1-Roul2 as the edge type to reveal which tokens play argument roles .
Outcome:	The proposed method can avoid inefficiency and error propagation issues in traditional pipeline methods.

Incorporating Graph Attention Mechanism into Knowledge Graph Reasoning Based on Deep Reinforcement Learning (D19-1)

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Heng Wang, Shuangyin Li, Rong Pan, Mingzhi Mao

When Cantonese NLP Meets Pre-training: Progress and Challenges (2022.aacl-tutorials)

Challenge:	Existing methods for learning knowledge Graphs are incomplete and therefore need well-pretraining.
Approach:	They propose a deep reinforcement learning based model which incorporates LSTM and Graph Attention Mechanism as the memory components.
Outcome:	The proposed model can get rid of the pretraining process and achieve state-of-the-art performance compared with the other models.

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Rong Xiang, Hanzhuo Tan, Jing Li, Mingyu Wan, Kam-Fai Wong

ReasonRec: A Reasoning-Augmented Multimodal Agent for Unified Recommendation (2026.findings-acl)

Challenge:	Cantonese is an influential Chinese variant with a large population of speakers worldwide.
Approach:	This tutorial will review Cantonese's progress in linguistics and NLP . it will introduce transformer-based pre-training methods for a wide range of downstream tasks .
Outcome:	This tutorial will present the main challenges for Cantonese NLP in relation to Cantonesian language idiosyncrasies of colloquialism and multilingualism.

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Yihua Zhang, Mingfu Liang, Jiyan Yang, Rong Jin, Wen-Yen Chen, Yiping Han, Huayu Li, Buyun Zhang, Liang Luo, Luke Simon, Sijia Liu, Tianlong Chen, Xi Liu

Responsible & Trustworthy NLP

Challenge:	Recent advances in multimodal recommenders lack explicit reasoning and self-awareness of uncertainty.
Approach:	They propose a reasoning-augmented multimodal agent structured around a three-stage explicit reasoning pipeline.
Outcome:	The proposed agent improves ranking metrics and performance on four standard recommendation tasks across five real-world datasets.

HER: Human-like Reasoning and Reinforcement Learning for LLM Role-playing (2026.findings-acl)

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Chengyu Du, Xintao Wang, Aili Chen, Weiyuan Li, Rui Xu, Junteng Liu, Zishan Huang, Rong Tian, Zijun Sun, Yuhao Li, Liheng Feng, Deming Ding, Pengyu Zhao, Yanghua Xiao

Leveraging Estimated Transferability Over Human Intuition for Model Selection in Text Ranking (2024.emnlp-main)

Challenge:	Existing models for LLM role-playing lack high-quality datasets with explicit reasoning traces and reliable reward signals aligned with human preferences.
Approach:	They propose a unified framework for cognitive-level persona simulation that strictly distinguishes characters’ first-person thinking processes from LLMs’ third-person reasoning.
Outcome:	The proposed framework outperforms the Qwen3-32B baseline model and achieves a 30.26% and 14.97% performance on the minimax benchmarks.

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Jun Bai, Zhuofan Chen, Zhenzi Li, Hanhua Hong, Jianfei Zhang, Chen Li, Chenghua Lin, Wenge Rong

Information Extraction & Text Mining

Challenge:	Existing methods for text ranking are based on intuition, but their estimated transferability may not align well with the objectives of text ranking.
Approach:	They propose to compute expected rank as transferability, explicitly reflecting the model’s ranking capability.
Outcome:	The proposed method shows significant improvements over previous classification-oriented TE methods, human intuition, and ChatGPT with minor time consumption.

CEDAR: A Chinese Evaluation Dataset for Computational Argumentation (2026.acl-long)

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Tian Lan, Jiang Li, Rong Yan, Feilong Bao, Weihua Wang, Guanglai Gao, Xiangdong Su

An Examination of the Compositionality of Large Generative Vision-Language Models (2024.naacl-long)

Challenge:	Existing debate datasets neglect important labels for argument mining, generation, and evaluation.
Approach:	They propose a Chinese Evaluation Dataset for Computational Argumentation that includes key arguments and key rhetorical figures, debater roles, modal words, debate results and transcripts.
Outcome:	The proposed dataset covers 600 debates about 318 topics from Chinese debate competitions.

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Teli Ma, Rong Li, Junwei Liang

ProCQA: A Large-scale Community-based Programming Question Answering Dataset for Code Search (2024.lrec-main)

Challenge:	Recent studies have focused on the compositionality of vision-language models (VLMs) however, the performance of GVLMs in multimodal compositional reasoning remains under-explored.
Approach:	They propose a syntactical bias score to quantify GVLMs' syntaktical bias . they propose 'SADE' task to assess GVLs's robustness against inclination toward syntical correctness.
Outcome:	The proposed benchmarks are based on evaluation metrics and current benchmarks.

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Zehan Li, Jianfei Zhang, Chuantao Yin, Yuanxin Ouyang, Wenge Rong

RoChBert: Towards Robust BERT Fine-tuning for Chinese (2022.findings-emnlp)

Challenge:	Existing approaches to code question answering use bi-modal and unimodal pretraining to align text and code representations.
Approach:	They propose a modality-agnostic contrastive pre-training approach to improve alignment of text and code representations of current code language models.
Outcome:	The proposed model exhibits significant performance improvements across a wide range of code retrieval benchmarks.

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Zihan Zhang, Jinfeng Li, Ning Shi, Bo Yuan, Xiangyu Liu, Rong Zhang, Hui Xue, Donghong Sun, Chao Zhang

STEMM: Self-learning with Speech-text Manifold Mixup for Speech Translation (2022.acl-long)

Challenge:	Pre-trained language models (e.g., BERT) have been proved vulnerable to adversarial texts.
Approach:	They propose to fuse Chinese phonetic and glyph features into pre-trained models by using a more comprehensive adversarial graph.
Outcome:	The proposed framework outperforms existing methods in significant ways on a wide range of tasks while remaining accurate on benign texts.

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Qingkai Fang, Rong Ye, Lei Li, Yang Feng, Mingxuan Wang

WACO: Word-Aligned Contrastive Learning for Speech Translation (2023.acl-long)

Challenge:	Existing methods to learn speech representations for end-to-end speech-totext translation (ST) neglect the representation discrepancy across modalities.
Approach:	They propose a method to calibrate the representation discrepancy between modalities by mixing up the representation sequences of different modality inputs.
Outcome:	The proposed method alleviates the cross-modal representation discrepancy and improves on a strong baseline on eight translation directions.

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Siqi Ouyang, Rong Ye, Lei Li

JailMeter: An Evidence-Based Evaluation Framework for Jailbreak Attacks on Large Language Models (2026.findings-acl)

Challenge:	Existing ST methods perform poorly when only a limited amount of parallel data are available for training.
Approach:	They propose a Word-Aligned COntrastive learning method for low-resource speech-to-text translation that bridges word-level representations for both speech and text modalities via contrastive learning.
Outcome:	The proposed method outperforms the best baseline by 9+ BLEU points with only 1-hour parallel ST data.

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Qingjia Huang, Jingyu Zhang, Jianguo Wu, Yakai Li, Weijuan Zhang, Yankai Rong, Junyi Yao, Shengzhi Zhang, Xiaoqi Jia

Scaling Law for Multimodal Large Language Model Supervised Fine-Tuning (2026.acl-long)

Challenge:	Currently, evaluation criteria and methods used for jailbreak effectiveness are inconsistent.
Approach:	They propose a framework to measure jailbreak effectiveness using a model that filters out jailbreak noise while preserving the original malicious question.
Outcome:	The proposed framework outperforms existing evaluation methods on a challenging benchmark containing 330 human-labeled, non-rejected jailbreak instances.

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YiFan Zhang, Tao Yu, Feng Li, Chaoyou Fu, Yibo Hu, Kun Wang, Qingsong Wen, Zhang Zhang, Liang Wang, Rong Jin

Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct Decoding (2024.findings-acl)

Challenge:	supervised fine-tuning (SFT) is crucial for multimodal large language models, yet a comprehensive scaling law is lacking . et al.: scaling laws focus on model size, pre-training tokens, and MLLM SFT data volumes .
Approach:	They propose two scaling laws to guide optimal model-data configuration . they propose one applicable when training data volumes are well defined by researchers .
Outcome:	The proposed scaling laws provide valuable recommendations for optimal resource allocation . they show that the proposed laws are more accurate than existing models .

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Hanling Yi, Feng Lin, Hongbin Li, Ning Peiyang, Xiaotian Yu, Rong Xiao