Papers by Li Ni

Sign-Language Datasets at Scale: A Comprehensive Survey on Resources, Benchmarks, and Annotation Standards (2026.acl-long)

Challenge:	Towards the KV cache efficiency, we propose a new objective that lifts the threshold constraints for robust KV compression.
Approach:	They propose a method that adjusts KV cache budgets while preserving full-cache performance.
Outcome:	The proposed method can reduce memory consumption while preserving full-cache performance.

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Yiming Ni, Zhi-Qi Cheng, Jiayu Li, Wei Cheng

Challenge:	Existing benchmarks fail to reflect real-world communication needs and are limited in their coverage.
Approach:	They present a comprehensive index of sign-language datasets, covering 120 resources across 35 sign languages.
Outcome:	The proposed index covers 120 resources across 35 sign languages.

AgentCourt: Simulating Court with Adversarial Evolvable Lawyer Agents (2025.findings-acl)

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Guhong Chen, Liyang Fan, Zihan Gong, Nan Xie, Zixuan Li, Ziqiang Liu, Chengming Li, Qiang Qu, Hamid Alinejad-Rokny, Shiwen Ni, Min Yang

Robustness in NLP

Challenge:	Existing legal language models struggle with dynamic courtroom interactions, resulting in overfitting to standardized legal tasks.
Approach:	They propose a new adversarial evolutionary approach for agents that performs dynamic knowledge learning and evolution through structured adversarials in a simulated courtroom program.
Outcome:	The proposed approach outperforms existing LLM-based models in three critical dimensions: cognitive agility, professional knowledge, and logical rigor.

Unified Demonstration Retriever for In-Context Learning (2023.acl-long)

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Xiaonan Li, Kai Lv, Hang Yan, Tianyang Lin, Wei Zhu, Yuan Ni, Guotong Xie, Xiaoling Wang, Xipeng Qiu

A Survey on LLM-based Conversational User Simulation (2026.eacl-long)

Challenge:	In-context learning is a new learning paradigm where a language model conditions on a few input-output pairs (demonstrations) and a test input, and directly outputs the prediction.
Approach:	They propose a single model to retrieve demonstrations for a wide range of tasks by combining training signals from various tasks into a unified list-wise ranking formulation by language model’s feedback.
Outcome:	The proposed model outperforms baselines on 30+ tasks across 13 task families and multiple data domains.

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Bo Ni, Yu Wang, Leyao Wang, Branislav Kveton, Franck Dernoncourt, Yu Xia, Hongjie Chen, Reuben Luera, Samyadeep Basu, Subhojyoti Mukherjee, Puneet Mathur, Nesreen K. Ahmed, Junda Wu, Li Li, Huixin Zhang, Ruiyi Zhang, Tong Yu, Sungchul Kim, Jiuxiang Gu, Zhengzhong Tu, Alexa Siu, Zichao Wang, Seunghyun Yoon, Nedim Lipka, Namyong Park, Zihao Lin, Trung Bui, Yue Zhao, Tyler Derr, Ryan A. Rossi

Dialogue Systems & Conversational Agents

Challenge:	Recent advances in large language models (LLMs) have enabled high-fidelity generation of synthetic user conversation.
Approach:	They propose a taxonomy covering user granularity and simulation objectives . they analyze core techniques and evaluation methodologies to help them understand the latest developments .
Outcome:	The proposed model enables high-fidelity generation of synthetic user conversation.

Data Interpreter: An LLM Agent for Data Science (2025.findings-acl)

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Sirui Hong, Yizhang Lin, Bang Liu, Bangbang Liu, Binhao Wu, Ceyao Zhang, Danyang Li, Jiaqi Chen, Jiayi Zhang, Jinlin Wang, Li Zhang, Lingyao Zhang, Min Yang, Mingchen Zhuge, Taicheng Guo, Tuo Zhou, Wei Tao, Robert Tang, Xiangtao Lu, Xiawu Zheng, Xinbing Liang, Yaying Fei, Yuheng Cheng, Yongxin Ni, Zhibin Gou, Zongze Xu, Yuyu Luo, Chenglin Wu

Listen, Decipher and Sign: Toward Unsupervised Speech-to-Sign Language Recognition (2023.findings-acl)

Challenge:	Large Language Models (LLMs) excel in various domains but face challenges when applied to data science workflows due to their complex, multi-stage nature.
Approach:	They propose a hierarchical graph-based agent that represents complexity and a progressive strategy for step-by-step verification, refinement, and consistent context management.
Outcome:	The proposed agent surpasses state-of-the-art baselines on the MATH dataset and performs better on InfiAgent-DABench.

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Liming Wang, Junrui Ni, Heting Gao, Jialu Li, Kai Chieh Chang, Xulin Fan, Junkai Wu, Mark Hasegawa-Johnson, Chang Yoo

Multi-Source Multi-Type Knowledge Exploration and Exploitation for Dialogue Generation (2023.emnlp-main)

Challenge:	Existing supervised sign language recognition systems rely on well-annotated data . instead, an unsupervised speech-to-sign language recognition system learns to translate between spoken and sign languages by observing only non-parallel speech and sign-language corpora.
Approach:	They propose an unsupervised speech-to-sign language recognition system that can translate between spoken and sign languages by observing only non-parallel speech and sign-language corpora.
Outcome:	The proposed approach outperforms baseline models on sign language corpora by 50% . the proposed approach is available at https://github.com/cactuswiththoughts/UnsupSpeech2Sign.git .

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Xuanfan Ni, Hongliang Dai, Zhaochun Ren, Piji Li

Dialogue Systems & Conversational Agents

Challenge:	Existing models focus on identifying specific types of dialogue knowledge and utilizing corresponding datasets for training, but lack generalization capabilities and computational resources.
Approach:	They propose a framework that explores multi-source multi-type knowledge from LLMs by leveraging diverse datasets and exploits it for response generation.
Outcome:	The proposed framework exploits multi-source multi-type knowledge from LLMs to generate coherent, informative, and fluent responses.

Expanding before Inferring: Enhancing Factuality in Large Language Models through Premature Layers Interpolation (2025.emnlp-main)

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Dingwei Chen, Ziqiang Liu, Feiteng Fang, Chak Tou Leong, Shiwen Ni, Ahmadreza Argha, Hamid Alinejad-Rokny, Min Yang, Chengming Li

Weakly Supervised Text Classification using Supervision Signals from a Language Model (2022.findings-naacl)

Challenge:	Existing approaches to generating factually inconsistent outputs are resource-intensive.
Approach:	They propose a plug-and-play intervention designed to enhance factuality by inserting premature layers formed through mathematical interpolation with adjacent layers.
Outcome:	The proposed intervention reduces hallucinations while outperforming baselines on four datasets.

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Ziqian Zeng, Weimin Ni, Tianqing Fang, Xiang Li, Xinran Zhao, Yangqiu Song

Low-Resource NLP

Challenge:	Existing weakly supervised text classification methods require a large number of annotated data and human annotations are expensive.
Approach:	They propose to query a masked language model with cloze style prompts to obtain supervision signals.
Outcome:	The proposed method outperforms baseline methods on three datasets by 2%, 4%, and 3%.

Pingan Smart Health and SJTU at COIN - Shared Task: utilizing Pre-trained Language Models and Common-sense Knowledge in Machine Reading Tasks (D19-60)

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Xiepeng Li, Zhexi Zhang, Wei Zhu, Zheng Li, Yuan Ni, Peng Gao, Junchi Yan, Guotong Xie

Polynomial Expansion Rank Adaptation: Enhancing Low-Rank Fine-Tuning with High-Order Interactions (2026.findings-acl)

Challenge:	Existing approaches to represent knowledge in the low-dimensional space are to leverage large-scale unsupervised text corpus to train fixed or contextual representations.
Approach:	They propose to leverage large-scale unsupervised text corpus to train fixed or contextual language representations and to express knowledge into a knowledge graph (KG) they incorporate distributional representations of a KG onto the representations from pre-trained language models, via simply concatenation or multi-head attention.
Outcome:	The proposed models outperform the other models on the COIN: COmmonsense INference in Natural Language Processing (COIN) Workshop datasets.

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Wenhao Zhang, Lin Mu, Li Ni, Peiquan Jin, Yiwen Zhang

Layer-wise Regularized Dropout for Neural Language Models (2024.lrec-main)

Challenge:	Low-rank adaptation (LoRA) is a widely used strategy for efficient fine-tuning of large language models, but its strictly linear structure limits expressive capacity.
Approach:	They propose a method that introduces structured polynomial expansion directly into the low-rank factor space.
Outcome:	The proposed method outperforms state-of-the-art methods across diverse benchmarks.

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Shiwen Ni, Min Yang, Ruifeng Xu, Chengming Li, Xiping Xiping Hu

SrDetection: A Self-Referential Framework for Data Leakage Detection in Code Large Language Models (2026.findings-acl)

Challenge:	Existing methods to regularize dropout are consistency training and dropout is a problem in many pre-trained neural language models.
Approach:	They propose a layer-wise regularized dropout technique which regularizes dropout at the output layer using consistency training.
Outcome:	The proposed model can be regarded as a "self-distillation" framework, in which each sub-model generated by dropout is the other's "teacher" model and "student" model.

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Shuaimin Li, Liyang Fan, Zeyang li, Zhuoyue Wan, Yufang Lin, Shiwen Ni, Feiteng Fang, Hamid Alinejad-Rokny, Yuanfeng Song, Kun Jing, Chen Jason Zhang, Min Yang

SOLAR: Serendipity Optimized Language Model Aligned for Recommendation (2025.findings-emnlp)

Challenge:	Existing methods for evaluating code large language models assume access to proprietary training corpora or use external reference sets with manually tuned, non-generalizable thresholds.
Approach:	They propose a framework for self-referential leakage detection for gray-box and black-box settings.
Outcome:	The proposed framework improves average F1 by 21.52 points in the gray-box setting and 14.46 points in black-box settings over strong baselines.

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Zichen Yuan, Lifan Sun, Yucen Zhuang, Yue Wang, Xinyuan Song, Tianqi Xu, Siyuan Li, Junchen Fu, Youhua Li, Sirui Hong, Jiaqi Chen, Joemon M. Jose, Yongxin Ni

Marco-Bench-MIF: On Multilingual Instruction-Following Capability of Large Language (2025.acl-long)

Challenge:	Large Language Models have shown strong potential in recommendation tasks . however, their application to serendipity-oriented recommendations remains challenging .
Approach:	They propose a domain-adaptive instruction tuning method that aligns Large Language Models with recommendation tasks.
Outcome:	The proposed framework bridges the domain gap between LLMs and recommendation tasks.

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Bo Zeng, Chenyang Lyu, Sinuo Liu, Mingyan Zeng, Minghao Wu, Xuanfan Ni, Tianqi Shi, Yu Zhao, Yefeng Liu, Chenyu Zhu, Ruizhe Li, Jiahui Geng, Qing Li, Yu Tong, Longyue Wang, Weihua Luo, Kaifu Zhang

Multilinguality

Challenge:	Existing datasets for instruction-following are monolingual and centered on English . existing data are unable to capture linguistic and cultural subtle differences .
Approach:	They propose an extension of IFEval to a localized multilingual version called Marco-Bench-MIF . their benchmark addresses linguistic constraints and cultural references via translation and verification .
Outcome:	The proposed extension of IFEval to a localized multilingual version covers 30 languages with varying levels of localization.

Beyond Quantity: Trajectory Diversity Scaling for Code Agents (2026.findings-acl)

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Guhong Chen, Chenghao Sun, Cheng Fu, Qiyao Wang, Zhihong Huang, ChaoPeng Wei, Guangxu Chen, Feiteng Fang, Ahmadreza Argha, Bing Zhao, Xander Xu, Qi Han, Hamid Alinejad-Rokny, Qiang Qu, Binhua Li, Shiwen Ni, Min Yang, HU Wei, Yongbin Li

Green & Sustainable NLP

Challenge:	Code large language models (LLMs) are becoming tool-interactive agents . quantity-centric scaling exhibits an early bottleneck that underutilizes trajectory data . et al.: a new approach to scale trajectory diversity improves tool-use generalization .
Approach:	They propose a Trajectory Diversity Scaling-based data synthesis framework for code agents that scales performance through diversity rather than raw volume.
Outcome:	Experiments on general tool-use benchmarks and code agent tasks show that TDScaling improves tool-user generalization and inherent coding proficiency.

Forgetting before Learning: Utilizing Parametric Arithmetic for Knowledge Updating in Large Language Models (2024.acl-long)

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Shiwen Ni, Dingwei Chen, Chengming Li, Xiping Hu, Ruifeng Xu, Min Yang

Tree-of-Code: A Self-Growing Tree Framework for End-to-End Code Generation and Execution in Complex Tasks (2025.findings-acl)

Challenge:	Existing methods of model editing and knowledge updating add additional network parameters, knowledge bases, knowledge base, and model parameters.
Approach:	They propose a new paradigm for fine-tuning called F-Learning that employs parametric arithmetic to facilitate the forgetting of old knowledge and learning of new knowledge.
Outcome:	The proposed model outperforms existing models on two datasets and is comparable to full fine-tuning and LoRA fine-uning.

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Ziyi Ni, Yifan Li, Ning Yang, Dou Shen, Pin Lyu, Daxiang Dong

Programming Languages in NLP

Challenge:	Effectively and efficiently handling complex realworld problems has become a key focus across industry and academia.
Approach:	They propose a tree-of-code framework that generates nodes through self-supervision and combines prompt and model exploration in a GT-free setting.
Outcome:	Experiments on two datasets with ten popular zero-shot LLMs show that Tree-of-Code boosts accuracy by nearly 20% over CodeAct with fewer than 1/4 turns.

Towards IP Intelligence: Benchmarking Large Language Models on Intellectual Property Knowledge and Practice (2026.findings-acl)

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Qiyao Wang, Guhong Chen, Hongbo Wang, Huaren Liu, Minghui Zhu, Zhifei Qin, Li Linwei, Yilin Yue, Shiqiang Wang, Jiayan Li, Wu Yihang, Ziqiang Liu, Longze Chen, Run Luo, Liyang Fan, Jiaming Li, Lei Zhang, Kan Xu, Hamid Alinejad-Rokny, Chengming Li, Shiwen Ni, Yuan Lin, Min Yang

SHARE: a System for Hierarchical Assistive Recipe Editing (2022.emnlp-main)

Challenge:	Existing datasets and benchmarks focus only on patents or cover limited aspects of the IP field, lacking alignment with real-world scenarios.
Approach:	They propose a bilingual IP task taxonomy and a large-scale bilingual benchmark to evaluate LLMs in real-world IP practice.
Outcome:	The proposed model achieves only 75.8% accuracy, indicating room for improvement . open-source IP and law-oriented models lag behind closed-source general-purpose models .

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Shuyang Li, Yufei Li, Jianmo Ni, Julian McAuley

Text Generation

Challenge:	Existing recipe websites do not provide options for users with dietary restrictions . a growing population follows some form of dietary restriction, with many people following it for a variety of reasons .
Approach:	They propose a system for hierarchical assistive recipe editing that performs simultaneous ingredient substitution before generating natural-language steps using the edited ingredients.
Outcome:	The proposed system can adapt a recipe to satisfy a user-specified dietary constraint.

ResearchBench: Benchmarking LLMs in Scientific Discovery via Inspiration-Based Task Decomposition (2026.findings-acl)

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Yujie Liu, Zonglin Yang, Tong Xie, Jinjie Ni, Ben Gao, Yuqiang Li, Shixiang Tang, Wanli Ouyang, Erik Cambria, Dongzhan Zhou

DependEval: Benchmarking LLMs for Repository Dependency Understanding (2025.findings-acl)

Challenge:	Large language models have shown potential in assisting scientific research, yet their ability to discover high-quality research hypotheses remains unexamined due to the lack of a dedicated benchmark.
Approach:	They propose a benchmark for evaluating large language models on a sufficient set of scientific discovery sub-tasks.
Outcome:	The proposed framework extracts critical components from papers across 12 disciplines with expert validation confirming its accuracy.

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Junjia Du, Yadi Liu, Hongcheng Guo, Jiawei Wang, Haojian Huang, Yunyi Ni, Zhoujun Li

Challenge:	a benchmark is designed to evaluate the repository-level dependency understanding of large language models (LLMs) based on 2683 repositories from real-world websites.
Approach:	They propose a benchmark to evaluate repository dependency understanding for large language models . DEPENDEVAL evaluates models on three core tasks across 8 programming languages .
Outcome:	The benchmark evaluates models on three core tasks across 8 programming languages from real-world repositories.

R-AT: Regularized Adversarial Training for Natural Language Understanding (2022.findings-emnlp)

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Shiwen Ni, Jiawen Li, Hung-Yu Kao

Robustness in NLP

Challenge:	Currently, adversarial training is a popular and powerful regularization method in the natural language domain.
Approach:	They propose to regularize adversarial training via dropout by perturbing word embeddings . they find that R-AT can improve many models by reducing adversariality .
Outcome:	The proposed method can reduce the inconsistency between training and testing of models with dropout.

Defending LLMs against Jailbreak Attacks via Template-Based ICL with a Defensive Suffix (2026.findings-acl)

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Ruiyang Ni, Changlong Li, Shuaibiao Han, Zhiyu Yi, Perley Xu, Wenjie Ruan

Meet The Truth: Leverage Objective Facts and Subjective Views for Interpretable Rumor Detection (2021.findings-acl)

Challenge:	State-of-the-art large language models (LLMs) are vulnerable to jailbreak attacks, such as GCG and AutoDAN.
Approach:	They propose to take the advances of online In-Context Learning and an offline defensive suffix and optimize them using an iterative algorithm and an online stochastic random search to identify the most effective ICL demonstrations.
Outcome:	The proposed method reduces attack success rate to nearly 0% while maintaining the model’s utility on benign tasks and incurring only negligible* computational overhead.

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Jiawen Li, Shiwen Ni, Hung-Yu Kao

SWE-QA-Pro: A Representative Benchmark and Scalable Training Recipe for Repository-Level Code Understanding (2026.findings-acl)

Challenge:	Existing rumor detection methods provide detection labels while ignoring their explanation.
Approach:	a novel model is proposed to automatically classify rumors using Wikipedia documents . the model combines objective facts and subjective views to verify rumours .
Outcome:	a new model outperforms existing models on real-world Twitter datasets . the proposed model combines objective facts and subjective views to verify rumor .

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Songcheng Cai, Zhiheng Lyu, Yuansheng Ni, Xiangchao Chen, Baichuan Zhou, Shenzhe Zhu, Yi Lu, Haozhe Wang, Chi Ruan, Benjamin Schneider, Weixu Zhang, Xiang Li, Andy Zheng, Yuyu Zhang, Ping Nie, Wenhu Chen

Learning Flexible Large Multimodal Models with Arbitrary Modality Combinations (2026.findings-acl)

Challenge:	Existing benchmarks for agentic repository-level code understanding overlook long tail topics and rely on memorized knowledge.
Approach:	They propose a repository-level agentic code understanding benchmark that uses long-tail repositories with executable environments to enforce topical balance.
Outcome:	Empirically, a Qwen3-8B model trained with the proposed benchmark outperforms GPT-4o by 2.3 points.

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Xinyu Zhao, Kangqi Ni, Jie Peng, Ang Li, Tianlong Chen

Can MLLMs Understand the Deep Implication Behind Chinese Images? (2025.acl-long)

Challenge:	Multimodal Large Language Models (MLLMs) have potential for cross-modal understanding . but extending MLLM to handle diverse modalities introduces two challenges .
Approach:	They propose a dual-stage compression mechanism to reduce the number of modality tokens per modality and condense it into a single, compact token sequence.
Outcome:	Experiments show that Flex-M3 outperforms its counterpart trained on only full-modality data.

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Chenhao Zhang, Xi Feng, Yuelin Bai, Xeron Du, Jinchang Hou, Kaixin Deng, Guangzeng Han, Qinrui Li, Bingli Wang, Jiaheng Liu, Xingwei Qu, Yifei Zhang, Qixuan Zhao, Yiming Liang, Ziqiang Liu, Feiteng Fang, Min Yang, Wenhao Huang, Chenghua Lin, Ge Zhang, Shiwen Ni

E-EVAL: A Comprehensive Chinese K-12 Education Evaluation Benchmark for Large Language Models (2024.findings-acl)

Challenge:	MLLMs perform poorly on traditional culture images, indicating limitations in understanding high-level semantics and lacking a deep knowledge base of Chinese traditional culture.
Approach:	They propose to use Chinese images to assess MLLMs' higher-order perception and understanding of Chinese visual content.
Outcome:	The proposed model incorporates images that represent Chinese traditional culture, such as famous Chinese traditional paintings, to ensure the authenticity of the Chinese context.

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Jinchang Hou, Chang Ao, Haihong Wu, Xiangtao Kong, Zhigang Zheng, Daijia Tang, Chengming Li, Xiping Hu, Ruifeng Xu, Shiwen Ni, Min Yang

TalkLoRA: Communication-Aware Mixture of Low-Rank Adaptation for Large Language Models (2026.acl-long)

Challenge:	despite the rapid development of Large Language Models, there is no dedicated benchmark for evaluating LLMs in Chinese K-12 education.
Approach:	They propose to develop a benchmark specifically tailored for Chinese K-12 education.
Outcome:	EVAL is the first evaluation benchmark specifically tailored for Chinese K-12 education.

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Lin Mu, Haiyang Wang, Li Ni, Lei Sang, Zhize Wu, Peiquan Jin, Yiwen Zhang

UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models (2022.emnlp-main)

Challenge:	Existing LoRA methods assume that experts operate independently, leading to unstable routing, expert dominance.
Approach:	They propose a communication-aware MoELoRA framework that relaxes this assumption by introducing expert-level communication prior to routing.
Outcome:	The proposed framework outperforms vanilla LoRA and MoELoRA on diverse language understanding tasks while maintaining expert dominance.

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Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zettlemoyer, Tao Yu

DICP: Deep In-Context Prompt for Event Causality Identification (2025.findings-emnlp)

Challenge:	Structured knowledge grounding (SKG) uses structured knowledge to complete user requests . since inputs and outputs of SKG tasks are heterogeneous, they have been studied separately .
Approach:	They propose a framework that unifies 21 SKG tasks into a text-to-text format . they use unifiedSKG to benchmark T5 with different sizes .
Outcome:	The proposed framework unifies 21 SKG tasks into a text-to-text format . it achieves state-of-the-art performance on almost all of the 21 tasks, the authors show .

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Lin Mu, Jun Shen, Li Ni, Lei Sang, Zhize Wu, Peiquan Jin, Yiwen Zhang

DenseLoRA: Dense Low-Rank Adaptation of Large Language Models (2025.acl-long)

Challenge:	Existing prompt-learning-based methods concatenate in-context examples only at the input layer, limiting the model’s ability to capture abstract semantic cues necessary for identifying complex causal relationships.
Approach:	They propose a model that injects in-context examples into the deeper layer of a pre-trained language model (PLM) this model leverages hierarchical semantic representations formed in deeper layers, thereby enhancing its capacity to learn high-level causal abstractions.
Outcome:	The proposed model improves on two widely used datasets and shows that it can learn high-level causal abstractions.

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Lin Mu, Xiaoyu Wang, Li Ni, Yang Li, Zhize Wu, Peiquan Jin, Yiwen Zhang

LMR-BENCH: Evaluating LLM Agent’s Ability on Reproducing Language Modeling Research (2025.emnlp-main)

Challenge:	Low-rank adaptation (LoRA) is an efficient approach for adapting large language models (LLMs) but many of the weights in these matrices are redundant, leading to inefficiencies in parameter utilization.
Approach:	They propose a low-rank adaptation approach that fine-tunes two low-ranked matrices and adapts them through a dense low-Rank matrix, improving parameter utilization and adaptation efficiency.
Outcome:	The proposed approach achieves 83.8% accuracy with only 0.01% of trainable parameters compared to LoRA's 80.8% with 0.70% of trainability parameters on LLaMA3-8B.

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Shuo Yan, Ruochen Li, Ziming Luo, Zimu Wang, Daoyang Li, Liqiang Jing, Kaiyu He, Peilin Wu, Juntong Ni, George Michalopoulos, Yue Zhang, Ziyang Zhang, Mian Zhang, Zhiyu Chen, Xinya Du

NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation (2023.acl-long)

Challenge:	Large language model (LLM) agents have demonstrated remarkable potential in advancing scientific discovery, but their capability in reproducing code from research papers remains underexplored.
Approach:	They propose to evaluate LLM agents' ability to reproduce scientific research papers by analyzing code reproduction tasks from 23 research papers published in top-tier NLP venues.
Outcome:	The proposed benchmark systematically evaluates the capability of large language model (LLM) agents on code reproduction from Language Modeling Research.

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Shengming Yin, Chenfei Wu, Huan Yang, Jianfeng Wang, Xiaodong Wang, Minheng Ni, Zhengyuan Yang, Linjie Li, Shuguang Liu, Fan Yang, Jianlong Fu, Ming Gong, Lijuan Wang, Zicheng Liu, Houqiang Li, Nan Duan

Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects (D19-1)

Challenge:	Existing work generates long videos segment by segment sequentially, which is inefficient.
Approach:	They propose a Diffusion over Difference architecture for eXtremely Long video generation.
Outcome:	The proposed architecture reduces the average inference time from 7.55min to 26s (94.26%) and generates high-quality long videos with both global and local coherence.

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Jianmo Ni, Jiacheng Li, Julian McAuley

Challenge:	Existing approaches to generating reviews struggle to generate justifications that are relevant to users’ decision-making process.
Approach:	They propose an ‘extractive’ approach to identify review segments which justify users’ intentions and use it to distantly label massive review corpora and construct large-scale personalized recommendation justification datasets.
Outcome:	The proposed model can generate convincing and diverse justifications from massive review corpora and distantly label massive review data.

Towards Mitigating Modality Bias in Vision-Language Models for Temporal Action Localization (2026.acl-long)

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Jiaqi Li, Guangming Wang, Shuntian Zheng, Minzhe Ni, Xiaoman Lu, Guanghui Ye, Yu Guan

Quantification of Large Language Model Distillation (2025.acl-long)

Challenge:	Existing vision-language models overemphasize linguistic priors, leading to modality bias.
Approach:	They propose a vision-language aggregation framework that mitigates modality bias in TAL by preserving vision as the dominant signal while adaptively exploiting language only when beneficial.
Outcome:	Experiments on THUMOS14 show that the proposed model outperforms state-of-the-art models by up to 3.2% mAP.

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Sunbowen Lee, Junting Zhou, Chang Ao, Kaige Li, Xeron Du, Sirui He, Haihong Wu, Tianci Liu, Jiaheng Liu, Hamid Alinejad-Rokny, Min Yang, Yitao Liang, Zhoufutu Wen, Shiwen Ni

GraphLoRA: Structure-Aware Low-Rank Adaptation for Large Language Model Recommendation (2026.findings-acl)

Challenge:	Existing studies have revealed the robustness degra-dation caused by data distillation.
Approach:	They propose a framework to evaluate and quantify model distillation . they aim to identify identity cognition contradictions and analyse multi-granularity response similarities across models to measure the extent of homogenization.
Outcome:	The proposed framework addresses two key aspects: (1) Identifying identity cognition contradictions to assess discrepancies in how models perceive and represent identity-related information; (2) Analyzing multi-granularity response similarities across models to measure the extent of homogenization.

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Lin Mu, Guoji Wang, Li Ni, Lei Sang, Zhize Wu, Peiquan Jin, Yiwen Zhang

MoZIP: A Multilingual Benchmark to Evaluate Large Language Models in Intellectual Property (2024.lrec-main)

Challenge:	Existing methods for translating collaborative information into textual prompts or injecting pre-trained embeddings into the LLM treat structural information as static input and fail to capture high-order relational dependencies.
Approach:	They propose a framework that generalizes low-rank adaptation from independent to structure-aware propagation by embedding a trainable graph message-passing network within the low-ranked adaptation pathway.
Outcome:	Experiments on multiple benchmarks show that GraphLoRA outperforms state-of-the-art recommendation methods and achieves superior generalization.

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Shiwen Ni, Minghuan Tan, Yuelin Bai, Fuqiang Niu, Min Yang, Bowen Zhang, Ruifeng Xu, Xiaojun Chen, Chengming Li, Xiping Hu

Natural Language Interfaces

Challenge:	Large language models (LLMs) have demonstrated impressive performance in various natural language processing tasks.
Approach:	They propose a benchmark for the evaluation of large language models in the IP domain . they also propose supervised multilingual large language model called MoZi .
Outcome:	The proposed model outperforms four well-known LLMs on the MoZIP benchmark . the most powerful ChatGPT does not reach the passing level .

Sound Signal Processing with Seq2Tree Network (L18-1)

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Weicheng Ma, Kai Cao, Zhaoheng Ni, Peter Chin, Xiang Li

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

Challenge:	Recent LSTM models have been used to model sequential data processing tasks because of their ability to preserve previous information weighted on distance.
Approach:	They propose to use a tree-structured tree-based neural network architecture to solve the problem of unbalanced connections between data units inside and outside semantic groups.
Outcome:	The proposed model outperforms the state-of-the-art Bidirectional LSTM model on a signal and noise separation task.

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Ningyu Zhang, Mosha Chen, Zhen Bi, Xiaozhuan Liang, Lei Li, Xin Shang, Kangping Yin, Chuanqi Tan, Jian Xu, Fei Huang, Luo Si, Yuan Ni, Guotong Xie, Zhifang Sui, Baobao Chang, Hui Zong, Zheng Yuan, Linfeng Li, Jun Yan, Hongying Zan, Kunli Zhang, Buzhou Tang, Qingcai Chen

Challenge:	a new benchmark for biomedical language understanding is being developed in Chinese . most benchmarks are limited to English, which makes it difficult to replicate success in other languages.
Approach:	They propose to use Chinese biomedical language understanding evaluation benchmarks to evaluate Chinese models.
Outcome:	The proposed benchmarks show that the current models perform worse than the human ceiling.

CoTJudger: A Graph-Driven Framework for Automatic Evaluation of Chain-of-Thought Efficiency and Redundancy in LRMs (2026.findings-acl)

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Siyi Li, Jiajun Shi, Shiwen Ni, Ge Zhang, Shuaimin Li, Shijian Wang, Zhoufutu Wen, Yizhi LI, Hamid Alinejad-Rokny, Jiaheng Liu, Min Yang, Wenhao Huang

Green & Sustainable NLP

Challenge:	Existing evaluations emphasize final accuracy or coarse token counts, and lack automated tools to separate essential logic from structural redundancy.
Approach:	They propose a graph-driven framework that quantifies reasoning efficiency by converting free-form CoTs into directed dependency graphs and extracting the Shortest Effective Path needed to reach a correct solution.
Outcome:	Evaluating 21 LRMs, the proposed framework quantifies reasoning efficiency by converting free-form CoTs into directed dependency graphs and extracting the Shortest Effective Path (SEP) needed to reach a correct solution.

Defenses Against Prompt Attacks Learn Surface Heuristics (2026.acl-long)

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Li Li, Chenxiao Yu, Zhiyu Ni, Hao Li, Charith Peris, Chaowei Xiao, Yue Zhao

LIME: Less Is More for MLLM Evaluation (2025.findings-acl)

Challenge:	Large language models (LLMs) are increasingly deployed in security-sensitive applications . recent defenses rely on supervised fine-tuning with benign and malicious labels . position bias arises when benign content placed later in a prompt is rejected at much higher rates .
Approach:	They analyze three recurring shortcut behaviors induced by supervised fine-tuning . position bias arises when benign content placed later in a prompt is rejected . token trigger bias occurs when strings common in attack data raise rejection probability .
Outcome:	The proposed model overrides intended logic when adversarial instructions appear . the proposed model has low rejection rates but narrow correlations in defense data .

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King Zhu, Qianbo Zang, Shian Jia, Siwei Wu, Feiteng Fang, Yizhi Li, Shuyue Guo, Tianyu Zheng, Jiawei Guo, Bo Li, Haoning Wu, Xingwei Qu, Jian Yang, Ruibo Liu, Xiang Yue, Jiaheng Liu, Chenghua Lin, Hamid Alinejad-Rokny, Min Yang, Shiwen Ni, Wenhao Huang, Ge Zhang