Papers by Li Niu

Perform like an Engine: A Closed-Loop Neural-Symbolic Learning Framework for Knowledge Graph Inference (2022.coling-1)

Challenge:	a small learning gap exists between large and small language models . long CoT data and large model responses are not beneficial for small models - a problem that may be due to the small student model's ability to handle distribution shifts.
Approach:	They propose a mix distillation strategy that balances reasoning complexity by combining long and short CoT examples or reasoning from both larger and smaller models.
Outcome:	The proposed strategy outperforms training on large and small models on short CoT and small model CoT.

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Guanglin Niu, Bo Li, Yongfei Zhang, Shiliang Pu

PepRec: Progressive Enhancement of Prompting for Recommendation (2024.emnlp-main)

Challenge:	Existing knowledge graphs are incomplete and therefore lack interpretability.
Approach:	They propose a closed-loop neural-symbolic learning framework EngineKG to address the natural incompleteness of knowledge graphs.
Outcome:	The proposed model outperforms baselines on link prediction tasks on four real-world datasets.

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Yakun Yu, Shi-ang Qi, Baochun Li, Di Niu

Guided by Gut: Efficient Test-Time Scaling with Reinforced Intrinsic Confidence (2026.acl-long)

Challenge:	Large language models (LLMs) have been gaining in-depth performance in natural language processing domains.
Approach:	They propose a training-free prompting framework that captures knowledge from content-based filtering and collaborative filtering to boost recommendation performance with LLMs.
Outcome:	The proposed framework outperforms traditional deep learning recommendation models and prompt-based recommendation systems on two real-world datasets.

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Amirhosein Ghasemabadi, Keith G. Mills, Baochun Li, Di Niu

SAC: Neural Speech Codec with Semantic-Acoustic Dual-Stream Quantization (2026.acl-long)

Challenge:	Guided by Gut (GG) is an efficient self-guided TTS framework for Large Language Models (LLMs) that performs step-by-step reasoning at a low cost without any reward models or verifiers.
Approach:	They propose a self-guided TTS framework that enables LLMs to perform step-by-step reasoning at a low cost without any reward models or verifiers.
Outcome:	Empirical evaluations show that GG performs better than TTS with PRMs while reducing GPU memory usage by up to 10.

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Wenxi Chen, Ruiqi Yan, Yushen Chen, Zhikang Niu, Ziyang Ma, Xiquan Li, Yuzhe Liang, null Wenhanlin, Shunshun Yin, Ming Tao, Xinsheng Wang, Xie Chen

UHGEval: Benchmarking the Hallucination of Chinese Large Language Models via Unconstrained Generation (2024.acl-long)

Challenge:	Existing speech codecs struggle to balance high-quality reconstruction with semantically rich representations, limiting their effectiveness in both generative and understanding tasks.
Approach:	They propose a neural speech codec with semantic-acoustic dual-stream quantization that disentangles semantic and acousian modeling into two dedicated streams.
Outcome:	The proposed codec outperforms state-of-the-art speech tokenizers in auto-propagating text-to-speech models.

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Xun Liang, Shichao Song, Simin Niu, Zhiyu Li, Feiyu Xiong, Bo Tang, Yezhaohui Wang, Dawei He, Cheng Peng, Zhonghao Wang, Haiying Deng

Instruction Fusion: Advancing Prompt Evolution through Hybridization (2024.acl-long)

Challenge:	Large language models (LLMs) produce hallucinated text, compromising their practical utility in professional contexts.
Approach:	They have developed an unconstrained hallucination generation evaluation benchmark that contains hallucines generated by large language models with minimal restrictions.
Outcome:	The proposed benchmarks are based on a Chinese-language dataset that is lacking in the field.

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Weidong Guo, Jiuding Yang, Kaitong Yang, Xiangyang Li, Zhuwei Rao, Yu Xu, Di Niu

Text Generation

Challenge:	Existing methods for fine-tuning Large Language Models (LLMs) encounter performance limitations, impeding further enhancements in code generation tasks.
Approach:	They propose to combine two distinct prompts through a hybridization process to enhance the evolution of training prompts for code LLMs.
Outcome:	The proposed method significantly improves the performance of Code LLMs across five code generation benchmarks, namely HumanEval, HumanEva+, MBPP, mbap+ and MultiPL-E.

MeanAudio: Fast and Faithful Text-to-Audio Generation with Mean Flows (2026.acl-long)

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Xiquan Li, Junxi Liu, Yuzhe Liang, Zhikang Niu, Wenxi Chen, Xie Chen

TaCIE: Enhancing Instruction Comprehension in Large Language Models through Task-Centred Instruction Evolution (2025.coling-main)

Challenge:	Recent advances in Text-to-Audio Generation (TTA) systems suffer from slow inference speed, authors report . authors demonstrate that MeanAudia achieves state-of-the-art performance in single-step audio generation .
Approach:	They propose a text-to-audio generator capable of rendering realistic sound with only one function evaluation.
Outcome:	The proposed system achieves state-of-the-art performance in single-step audio generation.

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Jiuding Yang, Shengyao Lu, Weidong Guo, Xiangyang Li, Kaitong Yang, Yu Xu, Di Niu

PLATO-Ad: A Unified Advertisement Text Generation Framework with Multi-Task Prompt Learning (2022.emnlp-industry)

Challenge:	Existing methods for fine-tuning Large Language Models (LLMs) encounter performance limitations, impeding further enhancements in code generation tasks.
Approach:	They propose to combine two distinct prompts through a hybridization process to enhance the evolution of training prompts for code LLMs.
Outcome:	The proposed method significantly improves the performance of Code LLMs across five code generation benchmarks.

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Zeyang Lei, Chao Zhang, Xinchao Xu, Wenquan Wu, Zheng-yu Niu, Hua Wu, Haifeng Wang, Yi Yang, Shuanglong Li

HiPrune: Hierarchical Attention for Efficient Token Pruning in Vision-Language Models (2026.findings-acl)

Challenge:	Online advertisement text generation models have achieved remarkable success in generating high-quality text ads, but some challenges remain, such as low-resource scenarios and training efficiency for multiple ad tasks.
Approach:	They propose a unified text ad generation framework with multi-task prompt learning to tackle low-resource ade generation problem and a multi-step prompt learning mechanism to efficiently solve multiple aed generation tasks.
Outcome:	The proposed framework outperforms the state-of-the-art on offline and online metrics.

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Jizhihui Liu, Guangdao Zhu, Feiyi Du, Niu Lian, Jun Li, Bin Chen, Weili Guan, Yaowei Wang

MatRank: Text Re-ranking by Latent Preference Matrix (2022.findings-emnlp)

Challenge:	Existing methods for visual token pruning lack insight into the intrinsic property of the vision encoder . et al., 2017: 99.3% of task accuracy with only 1/3 of the tokens.
Approach:	They propose a model-agnostic token pruning method that trains without training . they propose 'HiPrune' method which prunes visual tokens according to their attention .
Outcome:	The proposed method achieves 99.3% of task accuracy with only 1/3 of the tokens . it reduces inference FLOPs by 58.7% and maintains 99.99% accuracy with 2/9 tokens.

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Jinwen Luo, Jiuding Yang, Weidong Guo, Chenglin Li, Di Niu, Yu Xu

Information Retrieval

Challenge:	Existing methods for text ranking have improved performance, but there are still challenges.
Approach:	They propose a method that learns to re-rank the text retrieved for a given query by learning to predict the most relevant passage based on a latent preference matrix.
Outcome:	The proposed method outperforms all prior methods on datasets with extensive results.

MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)

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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He

Green & Sustainable NLP

Challenge:	Document images are characterized by higher resolutions, denser content, and more complex structural layouts.
Approach:	They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition.
Outcome:	The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead.

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

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

Green & Sustainable NLP

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

REPT: Bridging Language Models and Machine Reading Comprehension via Retrieval-Based Pre-training (2021.findings-acl)

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Fangkai Jiao, Yangyang Guo, Yilin Niu, Feng Ji, Feng-Lin Li, Liqiang Nie

UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning (2021.acl-long)

Challenge:	Pre-trained language models have achieved great success on Machine Reading Comprehension (MRC) however, the poor support in evidence extraction hinders them from further advancing MRC.
Approach:	They propose a REtrieval-based pre-training approach that strengthens evidence extraction during pre-training by inherited downstream MRC tasks.
Outcome:	The proposed approach strengthens evidence extraction during pre-training, which is further inherited by downstream tasks.

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Wei Li, Can Gao, Guocheng Niu, Xinyan Xiao, Hao Liu, Jiachen Liu, Hua Wu, Haifeng Wang

Incremental Transformer with Deliberation Decoder for Document Grounded Conversations (P19-1)

Challenge:	Existing pre-training methods focus on single-modal tasks or multi-modal ones . large-scale pre- training has drawn much attention in both the community of Compute Vision (CV) and Natural Language Processing (NLP).
Approach:	They propose a UNIfied-MOdal pre-training architecture which can adapt to both single-modal and multi-modal understanding and generation tasks.
Outcome:	The proposed model can learn more generalizable representations with rich non-paired single-modal data.

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Zekang Li, Cheng Niu, Fandong Meng, Yang Feng, Qian Li, Jie Zhou

Dialogue Systems & Conversational Agents

Challenge:	Existing dialogue systems do not exploit document knowledge effectively enough.
Approach:	They propose a Transformer-based architecture for document grounded conversations that incorporates document knowledge into a two-pass decoder to improve context coherence and knowledge correctness.
Outcome:	The proposed model outperforms baselines on context coherence and knowledge relevance on a real-world document grounded dataset.

LongReward: Improving Long-context Large Language Models with AI Feedback (2025.acl-long)

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Jiajie Zhang, Zhongni Hou, Xin Lv, Shulin Cao, Zhenyu Hou, Yilin Niu, Lei Hou, Yuxiao Dong, Ling Feng, Juanzi Li

AVG-LLaVA: An Efficient Large Multimodal Model with Adaptive Visual Granularity (2025.findings-acl)

Challenge:	In recent years, significant advancements have been achieved in the development of long-context large language models (LLMs).
Approach:	They propose a method that utilizes an off-the-shelf LLM to provide rewards for long-context model responses from four human-valued dimensions: helpfulness, logicality, faithfulness, and completeness.
Outcome:	The proposed method improves models’ long-context performance and enhances their ability to follow short instructions.

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Zhibin Lan, Liqiang Niu, Fandong Meng, Wenbo Li, Jie Zhou, Jinsong Su

Benchmarking Vision-Language Models on Chinese Ancient Documents: From OCR to Knowledge Reasoning (2026.findings-acl)

Challenge:	Existing large multimodal models typically divide high-resolution images into multiple local images and a global image, leading to a large number of visual tokens.
Approach:	They propose an LMM that can adaptively select the appropriate visual granularity based on the input image and instruction.
Outcome:	The proposed model significantly reduces visual tokens and speeds up inference on 11 benchmarks.

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Haiyang Yu, Yuchuan Wu, Fan Shi, Jinghui Lu, Ke Niu, Xiaodong Ge, Minghan Zhuo, Jingqun Tang, Bin Li

SLAM-Omni: Timbre-Controllable Voice Interaction System with Single-Stage Training (2025.findings-acl)

Challenge:	Existing document benchmarks focus on English printed texts or simplified Chinese . current vision-language models struggle with visual complexity and poor adaptability .
Approach:	They propose a benchmark to evaluate Chinese ancient documents' visual/linguistic complexity . ancient documents are valuable cultural heritage, but they face challenges in digitization and understanding .
Outcome:	the first benchmark for Chinese ancient documents evaluates VLMs from OCR to knowledge reasoning . ancient documents carry thousands of years of Chinese history and culture . traditional methods only scan images, while current models struggle with visual complexity .

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Wenxi Chen, Ziyang Ma, Ruiqi Yan, Yuzhe Liang, Xiquan Li, Ruiyang Xu, Zhikang Niu, Yanqiao Zhu, Yifan Yang, Zhanxun Liu, Kai Yu, Yuxuan Hu, Jinyu Li, Yan Lu, Shujie Liu, Xie Chen

Speech & Audio in NLP

Challenge:	a new spoken dialogue system with single-stage training is demonstrating its low latency and high quality . SLAM-Omni achieves zero-shot timbre control by modeling spoken language with semantic tokens .
Approach:	They propose a timbre-controllable, end-to-end voice interaction system with single-stage training.
Outcome:	The proposed system outperforms previous models on 4 GPUs with limited data.

Tiny Budgets, Big Gains: Parameter Placement Strategy in Parameter Super-Efficient Fine-Tuning (2025.emnlp-main)

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Jinman Zhao, Xueyan Zhang, Jiaru Li, Jingcheng Niu, Yulan Hu, Erxue Min, Gerald Penn

Green & Sustainable NLP

Challenge:	Existing methods such as LoRA and VeRA use memory-efficient methods to fine-tune large language models.
Approach:	They propose a method that uses only 1–5% of the standard LoRA parameters and achieves state-of-the-art performance across a wide range of tasks.
Outcome:	The proposed method achieves state-of-the-art performance across a wide range of tasks using only 1–5% of the standard LoRA parameters.

Privacy Risks of Intermediate Representations: Attribute Inference in Distributed LLM Inference (2026.findings-acl)

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Yang Lyu, Jin Cao, Yang Xiao, Zhe Sun, Ben Niu, Fenghua Li, Hui LI

RelEdit: Evaluating Conceptual Knowledge Editing in Language Models via Relational Reasoning (2025.findings-acl)

Challenge:	Distributed LLMs avoid raw inputs by transmitting intermediate hidden states, a practice widely assumed to preserve privacy.
Approach:	They propose a distributed inference framework that transmits intermediate hidden states to avoid sending raw inputs by exposing sensitive user attributes.
Outcome:	The proposed approach achieves Top-1 accuracy of 0.997 on CMS, 0.980 on Skytrax, and 0.986 on ECHR.

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Yifan Niu, Miao Peng, Nuo Chen, Yatao Bian, Tingyang Xu, Jia Li

Tree-KG: An Expandable Knowledge Graph Construction Framework for Knowledge-intensive Domains (2025.acl-long)

Challenge:	Existing knowledge editing methods struggle to reason about related conceptual knowledge effectively, despite a lack of model-level relational reasoning.
Approach:	They propose a benchmark to assess concept-level and instance-level relational reasoning abilities of edited models.
Outcome:	The proposed model obtains the best scores on the memory-based in-context editing baseline, MICE, suggesting a promising direction for model editing.

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Songjie Niu, Kaisen Yang, Rui Zhao, Yichao Liu, Zonglin Li, Hongning Wang, Wenguang Chen

Structured Data in NLP

Challenge:	Knowledge graphs are a useful tool for organizing complex data in knowledge-intensive domains.
Approach:	They propose an expandable framework that combines structured domain texts with advanced semantic techniques to create a tree-like graph from textbooks.
Outcome:	The proposed framework surpasses competing methods in the text-Annotated dataset with high scores on the Text-Annalytated data.

Learning Embeddings for Rare Words Leveraging Internet Search Engine and Spatial Location Relationships (2021.starsem-1)

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Xiaotao Li, Shujuan You, Yawen Niu, Wai Chen

PersLEARN: Research Training through the Lens of Perspective Cultivation (2023.acl-demo)

Challenge:	Existing word embedding techniques depend heavily on the frequencies of words in the corpus, and fail to provide reliable representations for rare words.
Approach:	They propose an algorithm to learn embeddings for rare words based on an Internet search engine and the spatial location relationships.
Outcome:	The proposed algorithm can learn more accurate representations for a wider range of vocabulary.

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Yu-Zhe Shi, Shiqian Li, Xinyi Niu, Qiao Xu, Jiawen Liu, Yifan Xu, Shiyu Gu, Bingru He, Xinyang Li, Xinyu Zhao, Zijian Zhao, Yidong Lyu, Zhen Li, Sijia Liu, Lin Qiu, Jinhao Ji, Lecheng Ruan, Yuxi Ma, Wenjuan Han, Yixin Zhu

Challenge:	PersLEARN is a tool designed to facilitate the cultivation of scientific perspectives . junior researchers struggle to identify the perspectives reflected in the literature and struggle to develop their own viewpoints.
Approach:	They propose a tool to facilitate the cultivation of scientific perspectives by interacting with a prompt-based model and allowing students to develop their own perspectives explicitly.
Outcome:	The proposed tool outperforms baseline approaches across multiple domains of literature from different perspectives.

rSIM: Incentivizing Reasoning Capabilities of LLMs via Reinforced Strategy Injection (2026.acl-long)

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Sijia Chen, Baochun Li, Di Niu

AMEX: Android Multi-annotation Expo Dataset for Mobile GUI Agents (2025.findings-acl)

Challenge:	Existing literature on Reasoning Language Models (RLMs) focuses on the ability to integrate reasoning strategies into the chain-of-thought process, contributing to improved problem-solving accuracy.
Approach:	They propose a reinforced strategy injection mechanism that enables any LLM to become an RLM by employing a small planner to guide the LLM's CoT through the adaptive injection of reasoning strategies.
Outcome:	The proposed model outperforms existing models in mathematical, coding, and financial reasoning tasks and is generalizable.

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Yuxiang Chai, Siyuan Huang, Yazhe Niu, Han Xiao, Liang Liu, Guozhi Wang, Dingyu Zhang, Shuai Ren, Hongsheng Li

Challenge:	a new dataset is being developed to improve the capabilities of mobile GUI-control agents.
Approach:	They propose a dataset designed for generalist mobile GUI-control agents . they use screenshots from popular mobile applications to create a detailed GUI-annotated dataset .
Outcome:	The Android Multi-annotation EXpo (AMEX) is a large-scale dataset for generalist mobile GUI-control agents . it includes screenshots from popular mobile applications, which are annotated at multiple levels .

MdEval: Massively Multilingual Code Debugging (2026.findings-acl)

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Shukai Liu, Linzheng Chai, Jian Yang, Jiajun Shi, He Zhu, Liran Wang, Jin Ke, Wei Zhang, Hualei Zhu, Shuyue Guo, Tao Sun, Jiaheng Liu, Yunlong Duan, Yu Hao, Liqun Yang, Guanglin Niu, Ge Zhang, Zhoujun Li

MorphoBench: A Benchmark with Difficulty Adaptive to Model Reasoning (2026.findings-acl)

Challenge:	Existing benchmarks primarily focus on Python and are limited in terms of language diversity.
Approach:	They propose a multilingual debugging benchmark that includes 3.9K test samples of 20 programming languages and introduces the debug instruction corpora MdEval-Instruct by injecting bugs into the correct multilingual queries and solutions.
Outcome:	The proposed benchmark includes 3.9K test samples of 20 programming languages and covers the automated program repair task, bug localization task, and bug identification task.

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Xukai Wang, Xuanbo Liu, Mingrui Chen, Haitian Zhong, Xuanlin Yang, Bohan Zeng, Jinbo Hu, Hao Liang, Junbo Niu, Xuchen Li, Ruitao Wu, Ruichuan An, Yang Shi, Liu Liu, Qiang Liu, Zhouchen Lin, Xu-Yao Zhang, Wentao Zhang, Bin Dong

A Unified Joint Approach with Topological Context Learning and Rule Augmentation for Knowledge Graph Completion (2024.findings-acl)

Challenge:	Existing benchmarks designed to evaluate the reasoning capabilities of large models are limited in scope and lack flexibility to adapt difficulty according to evolving reasoning capacities of models.
Approach:	They propose a benchmark that incorporates multidisciplinary questions to evaluate the reasoning capabilities of large models and can adjust and update question difficulty based on the reasoning abilities of advanced models.
Outcome:	The proposed benchmark incorporates multidisciplinary questions to evaluate the reasoning capabilities of large models and can adjust and update question difficulty based on the reasoning abilities of advanced models.

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Jingtao Guo, Chunxia Zhang, Lingxi Li, Xiaojun Xue, Zhendong Niu

LICHEE: Improving Language Model Pre-training with Multi-grained Tokenization (2021.findings-acl)

Challenge:	Existing knowledge graph completion methods perform simple linear update on relation representation, and only local neighborhood information is aggregated, making it difficult to capture logic semantic between relations and global topological context information.
Approach:	They propose a joint approach with Topological Context learning and Rule Augmentation (TCRA) it uses a topological context learning mechanism and a relation rule context learning system .
Outcome:	The proposed approach performs better on three benchmark datasets and is widely used in knowledgeintensive applications.

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Weidong Guo, Mingjun Zhao, Lusheng Zhang, Di Niu, Jinwen Luo, Zhenhua Liu, Zhenyang Li, Jianbo Tang

Self-Evolution Learning for Mixup: Enhance Data Augmentation on Few-Shot Text Classification Tasks (2023.emnlp-main)

Challenge:	Pre-trained language models are trained based on single-grained tokenization, making it hard to learn the precise meaning of coarse-grain words and phrases.
Approach:	They propose a language model pretraining method that incorporates multi-grained information of input text into pre-trained language models.
Outcome:	The proposed method improves performance on CLUE and SuperGLUE in Chinese and English with little extra inference cost.

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Haoqi Zheng, Qihuang Zhong, Liang Ding, Zhiliang Tian, Xin Niu, Changjian Wang, Dongsheng Li, Dacheng Tao

Low-Resource NLP

Challenge:	Text classification tasks often encounter few-shot scenarios with limited labeled data, and addressing data scarcity is crucial.
Approach:	They propose a self-evolution learning (SE) based mixup approach for data augmentation in text classification which generates more adaptive and model-friendly pseudo samples for the model training.
Outcome:	The proposed approach can generate more adaptive and model-friendly pseudo samples for the model training.

AutoETER: Automated Entity Type Representation for Knowledge Graph Embedding (2020.findings-emnlp)

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Guanglin Niu, Bo Li, Yongfei Zhang, Shiliang Pu, Jingyang Li

Adversarial Preference Learning for Robust LLM Alignment (2025.findings-acl)

Challenge:	Existing knowledge graphs are incomplete whether they are constructed manually or automatically, limiting the effectiveness when exploited for downstream applications.
Approach:	They propose a KGE framework with an automatic type embedding mechanism which can be easily integrated into any existing KGE model.
Outcome:	The proposed model can model and infer all the relation patterns and complex relations compared to state-of-the-art models on four datasets.

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Yuanfu Wang, Pengyu Wang, Chenyang Xi, Bo Tang, Junyi Zhu, Wenqiang Wei, Chen Chen, Chao Yang, Jingfeng Zhang, Chaochao Lu, Yijun Niu, Keming Mao, Zhiyu Li, Feiyu Xiong, Jie Hu, Mingchuan Yang

A Contextual Hierarchical Attention Network with Adaptive Objective for Dialogue State Tracking (2020.acl-main)

Challenge:	Modern language models rely on Reinforcement Learning from Human Feedback (RLHF) to encourage safe behaviors, but they remain vulnerable to adversarial attacks due to three key limitations: (1) the inefficiency and high cost of human annotation; (2) the vast diversity of potential adversarials; and (3) the risk of feedback bias and reward hacking.
Approach:	They propose an iterative adversarial training method that incorporates three key innovations to address these challenges.
Outcome:	Experiments on Mistral-7B-Instruct-v0.3 show that the proposed method significantly enhances robustness and reduces harmful outputs from 5.88% to 0.43%.

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Yong Shan, Zekang Li, Jinchao Zhang, Fandong Meng, Yang Feng, Cheng Niu, Jie Zhou

Dialogue Systems & Conversational Agents

Challenge:	Existing methods for dialogue state tracking ignore the slot imbalance problem and treat all slots indiscriminately, which limits the learning of hard slots.
Approach:	They propose to employ a contextual hierarchical attention network to enhance the DST by learning contextual representations.
Outcome:	The proposed approach achieves 52.68% and 58.55% joint accuracy on multiWOZ 2.0 and MultiWOZ 2.1 datasets and significantly improves performance (+1.24% and +5.98%)

Can We Steer Reasoning Direction by Thinking Intervention? (2025.findings-emnlp)

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Xingsheng Zhang, Luxi Xing, Chen Zhang, Yanbing Liu, Yifan Deng, Yunpeng Li, Yue Hu, Chenxu Niu

BadScientist: Can a Research Agent Write Convincing but Unsound Papers that Fool LLM Reviewers? (2026.acl-long)

Challenge:	Large Reason Models suffer from overthinking and erroneous reasoning problems due to the lack of fine-grained control over their reasoning behaviors.
Approach:	They propose a paradigm to enable fine-grained control over LRMs’ reasoning behaviors by aligning reasoning trajectories with specific cognitive patterns.
Outcome:	The proposed paradigm achieves integration intervention throughout model reasoning processes.

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Fengqing Jiang, Yichen Feng, Yuetai Li, Luyao Niu, Basel Alomair, Radha Poovendran

Challenge:	Existing evidence suggests that LLMs are not able to detect scientifically unsound work from malicious or poorly designed research agents.
Approach:	They develop a framework that evaluates whether fabrication-oriented paper generation agents can deceive multi-model LLM review systems.
Outcome:	The proposed framework shows that fabricated papers achieve acceptance rates up to 18% . the framework shows only marginal improvements, with detection accuracy barely exceeding random chance.

MoC: Mixtures of Text Chunking Learners for Retrieval-Augmented Generation System (2025.acl-long)

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Jihao Zhao, Zhiyuan Ji, Zhaoxin Fan, Hanyu Wang, Simin Niu, Bo Tang, Feiyu Xiong, Zhiyu Li

Chunking

Challenge:	Existing methods for text chunking are limited by text chunks and lack of domain-specific knowledge.
Approach:	They propose a dual-metric evaluation method to quantify text chunking quality . they aim to generate a structured list of chunking regular expressions .
Outcome:	The proposed method enables direct quantification of chunking quality . it substantiates the need to integrate LLMs into chunking process .

FCGCL: Fine- and Coarse-Granularity Contrastive Learning for Speech Translation (2022.findings-emnlp)

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Hao Zhang, Nianwen Si, Yaqi Chen, Zhen Li, Tong Niu, Xukui Yang, Dan Qu

A Challenge Dataset and Effective Models for Conversational Stance Detection (2024.lrec-main)

Challenge:	Existing methods to perform implicit knowledge transfer from machine translation to ST model are difficult because of the task complexity and data scarcity.
Approach:	They recommend a method which conducts explicit knowledge transfer from MT to ST model by fine and coarse granularity contrastive learning.
Outcome:	The proposed method improves the performance of the end-to-end speech translation model on all 8 languages.

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Fuqiang Niu, Min Yang, Ang Li, Baoquan Zhang, Xiaojiang Peng, Bowen Zhang

Sentiment Analysis

Challenge:	stance detection studies focus on evaluating stances within individual instances, hindering progress of conversational stance analysis.
Approach:	They propose a multi-turn conversation stance detection dataset that encompasses multiple targets for conversational stance detector.
Outcome:	The proposed dataset encompasses multiple targets for conversational stance detection.

Temporal Sampling for Forgotten Reasoning in LLMs (2026.acl-long)

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Yuetai Li, Zhangchen Xu, Fengqing Jiang, Bhaskar Ramasubramanian, Luyao Niu, Bill Yuchen Lin, Xiang Yue, Radha Poovendran

VisFinEval: A Scenario-Driven Chinese Multimodal Benchmark for Holistic Financial Understanding (2025.emnlp-main)

Challenge:	a new metric measures the percentage of questions that were answered incorrectly during fine-tuning .
Approach:	They propose a decoding strategy that draws outputs from multiple checkpoints along the training trajectory.
Outcome:	The proposed method improves reasoning performance and consistency across benchmarks.

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Zhaowei Liu, Xin Guo, Haotian Xia, Lingfeng Zeng, Fangqi Lou, Jinyi Niu, Mengping Li, Qi Qi, Jiahuan Li, Wei Zhang, Yinglong Wang, Weige Cai, Weining Shen, Liwen Zhang

SafeChain: Safety of Language Models with Long Chain-of-Thought Reasoning Capabilities (2025.findings-acl)

Challenge:	Existing benchmarks focus on text comprehension, but MLLMs lack the ability to integrate visual data over financial visuals.
Approach:	They evaluate 21 state-of-the-art multimodal large language models in a zero-shot setting . they use an annotated question–answer pair from eight common financial image modalities .
Outcome:	The new benchmark outperforms existing models but trailed financial experts by 14 percentage points.

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Fengqing Jiang, Zhangchen Xu, Yuetai Li, Luyao Niu, Zhen Xiang, Bo Li, Bill Yuchen Lin, Radha Poovendran

CFlowPsyD: An Analysis-Enhanced Dataset for Asynchronous Psychological Counseling through Self-Optimizing Multi-Agent Framework (2026.findings-acl)

Challenge:	Emerging large reasoning models (LRMs) leverage long chain-of-thought (CoT) reasoning to enhance their reasoning capabilities.
Approach:	They conduct a systematic study of LRM safety using human annotations to assess their safety.
Outcome:	The proposed safety measures are compared to state-of-the-art models on strong and wildjailbreak datasets.

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Donghao Li, Yifan Deng, Jinta Weng, Xingsheng Zhang, Chenxu Niu, Jingyuan Tian, Heyan Huang, Yue Hu

Dialogue Systems & Conversational Agents

Challenge:	Asynchronous psychological counseling (APC) is a crucial mental health service modality that transcends temporal and spatial constraints.
Approach:	They propose a self-optimizing multi-agent framework for counseling dialogue generation, CFlowPsy, which utilizes real anonymized counseling cases as seed data to synthesize diverse problem-solving-oriented APC conversations through large language models.
Outcome:	The proposed framework synthesizes diverse problem-solving-oriented APC conversations through large language models.

Hierarchical Region Learning for Nested Named Entity Recognition (2020.findings-emnlp)

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Xinwei Long, Shuzi Niu, Yucheng Li

Information Extraction & Text Mining

Challenge:	Existing methods to recognize entities recursively from innermost to outermost are based on brute force and two-stage paradigms, often leading to cascaded errors.
Approach:	They propose a hierarchical region learning framework to automatically generate a tree hierarchy of candidate regions with nearly linear complexity and incorporate structure information into the region representation for better classification.
Outcome:	Experiments on benchmark datasets ACE-2005, GENIA and JNLPBA show that the proposed framework performs better than state-of-the-art models.

UNIMO-2: End-to-End Unified Vision-Language Grounded Learning (2022.findings-acl)

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Wei Li, Can Gao, Guocheng Niu, Xinyan Xiao, Hao Liu, Jiachen Liu, Hua Wu, Haifeng Wang

Graph Counselor: Adaptive Graph Exploration via Multi-Agent Synergy to Enhance LLM Reasoning (2025.acl-long)

Challenge:	Existing methods for vision-language pre-training can only learn from aligned image-caption data and rely heavily on expensive regional features.
Approach:	They propose an end-to-end unified-modal pre-training framework for joint learning . they propose to conduct grounded learning on both images and texts via a sharing grounded space .
Outcome:	The proposed model improves visual and visual semantic alignment on images and texts.

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Junqi Gao, Xiang Zou, Ying Ai, Dong Li, Yichen Niu, Biqing Qi, Jianxing Liu

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

Challenge:	Existing methods for enhancing LLM reliability suffer from inefficient information aggregation and rigid reasoning schemes.
Approach:	They propose a method that explicitly models external knowledge integration capabilities by explicitly modeling knowledge relationships.
Outcome:	The proposed method outperforms existing methods in multiple graph reasoning tasks.

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

Speech & Audio in NLP

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

Boundary Matters: Leveraging Structured Text Plots for Long Text Outline Generation (2025.findings-emnlp)

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Yuanchi Ma, Jiamou Liu, Hui He, Libo Zhang, Haoyuan Li, Zhendong Niu

Challenge:	Existing methods for generating readable outlines are inability to segment long texts .
Approach:	They propose an unsupervised framework to guide large language model outline generation . framework ensures each structured plot encapsulates complete causality by accurately identifying plot boundaries.
Outcome:	The proposed framework ensures that each structured plot encapsulates complete causality by accurately identifying plot boundaries.

QAEncoder: Towards Aligned Representation Learning in Question Answering Systems (2025.acl-long)

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Zhengren Wang, Qinhan Yu, Shida Wei, Zhiyu Li, Feiyu Xiong, Xiaoxing Wang, Simin Niu, Hao Liang, Wentao Zhang

MelTrim: Coarse-to-Fine Data Pruning for Speech Classification (2026.findings-acl)

Challenge:	Modern QA systems entail retrieval-augmented generation (RAG) for accurate and trustworthy responses, but the inherent gap between user queries and relevant documents hinders precise matching.
Approach:	They propose a retrieval-augmented generation (RAG)-based approach to bridge this gap by attaching document fingerprints to the embedding to estimate the expectation of potential queries.
Outcome:	Experiments across diverse datasets, languages, and embedding models confirm the proposed solution is simple-yet-effective with zero additional index storage, retrieval latency, training costs, or catastrophic forgetting and hallucination issues.

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Shaobo Wang, Tianle Niu, Xuan Ouyang, Xintong Li, Zhengkun Ge, Yue Min, Xiaoqian Liu, Hankun Wang, Linfeng Zhang

Information Extraction & Text Mining

Challenge:	Unlike image or text classification, speech classification tasks are particularly challenging due to the difficulty in capturing the acoustic, semantic, and contextual representations.
Approach:	They propose a dataset pruning method that coarsely filters redundant samples using DBSCAN clustering on Mel-Frequency Cepstral Coefficients (MFCC) features.
Outcome:	The proposed method achieves 49.5% improvement in WA on the MEAD dataset and 41.9% reduction in EER on speaker identification tasks.

ProMedTS: A Self-Supervised, Prompt-Guided Multimodal Approach for Integrating Medical Text and Time Series (2025.findings-acl)

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Shuai Niu, Jing Ma, Hongzhan Lin, Liang Bai, Zhihua Wang, V. W., Richard Yi Da Xu, Guo Li, Xian Yang

URO-Bench: Towards Comprehensive Evaluation for End-to-End Spoken Dialogue Models (2025.findings-emnlp)

Challenge:	Large language models excel at processing unstructured data, but integrating time series data with text remains a challenge.
Approach:	They propose a self-supervised multimodal framework that uses prompt-guided learning to unify heterogeneous data types.
Outcome:	The proposed framework outperforms state-of-the-art approaches on disease diagnosis tasks using real-world datasets.

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Ruiqi Yan, Xiquan Li, Wenxi Chen, Zhikang Niu, Chen Yang, Ziyang Ma, Kai Yu, Xie Chen

Speech & Audio in NLP

Challenge:	a lack of comprehensive evaluations for SDMs in speech-to-speech (S2S) scenarios is a major challenge for end-to end spoken dialogue models.
Approach:	They propose to provide an extensive evaluation framework for end-to-end spoken dialogue models (SDMs) that includes both cognitive dimensions and paralinguistic cues .
Outcome:	The proposed benchmark is divided into two difficulty levels: basic track and pro track, each comprising 20 test sets, evaluating the spoken dialogue model’s abilities in Understanding, Reasoning, and O**ral conversation.

GuessArena: Guess Who I Am? A Self-Adaptive Framework for Evaluating LLMs in Domain-Specific Knowledge and Reasoning (2025.acl-long)

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Qingchen Yu, Zifan Zheng, Ding Chen, Simin Niu, Bo Tang, Feiyu Xiong, Zhiyu Li

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

Challenge:	Existing evaluation methods for large language models rely on static benchmarks and standardized evaluation protocols.
Approach:	They propose an adaptive evaluation framework that integrates dynamic domain knowledge modeling with progressive reasoning assessment to improve evaluation fidelity.
Outcome:	Empirical results show that the framework distinguishes LLMs in terms of domain knowledge coverage and reasoning chain completeness.

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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 .

CleanGen: Mitigating Backdoor Attacks for Generation Tasks in Large Language Models (2024.emnlp-main)

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Yuetai Li, Zhangchen Xu, Fengqing Jiang, Luyao Niu, Dinuka Sahabandu, Bhaskar Ramasubramanian, Radha Poovendran

Robustness in NLP

Challenge:	Generative large language models (LLMs) have remarkable performance in generation tasks, but datasets used to train or fine-tune these models are often not disclosed to users.
Approach:	They develop an inference time defense called CleanGen to mitigate backdoor attacks for generation tasks in large language models.
Outcome:	The proposed inference time defense achieves lower attack success rates (ASR) compared to baseline defenses for all five backdoor attacks.

CARE-STaR: Constraint-aware Self-taught Reasoner (2025.findings-acl)

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Zhiliang Li, Bo Tang, Yijun Niu, Beihong Jin, Qiwen Shi, Yuchen Feng, Zhiyu Li, Jie Hu, Mingchuan Yang, Feiyu Xiong

M3T: A New Benchmark Dataset for Multi-Modal Document-Level Machine Translation (2024.naacl-short)

Challenge:	Recent research on instruction following has demonstrated that LLMs can handle complex instructions.
Approach:	They propose to assign constraints to different levels of constraints in instructions . they use chain-of-thought and self-taught reasoner methods to identify constraints .
Outcome:	The proposed method outperforms supervised fine-tuning (SFT) on three instruction-following benchmarks.

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Benjamin Hsu, Xiaoyu Liu, Huayang Li, Yoshinari Fujinuma, Maria Nadejde, Xing Niu, Ron Litman, Yair Kittenplon, Raghavendra Pappagari

Answer-Supervised Question Reformulation for Enhancing Conversational Machine Comprehension (D19-58)

Challenge:	Document translation is a challenge for machine translation systems that focus on textual content at the sentence level, ignoring global context and visual layout structure.
Approach:	They propose a benchmark dataset to evaluate document-level NMT systems . they use visual cues to preserve reading order and contiguous blocks of text .
Outcome:	The proposed benchmarks assess document-level NMT systems on the comprehensive task of translating semi-structured documents.

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Qian Li, Hui Su, Cheng Niu, Daling Wang, Zekang Li, Shi Feng, Yifei Zhang

Natural Language Interfaces

Challenge:	Existing question reformulation models are based on supervised question labels without considering feedback information from answers.
Approach:	They propose a question reformulation model that integrates conversational history information with reinforcement learning.
Outcome:	The proposed model is more effective in conversational machine comprehension with reinforcement learning.

SafeRAG: Benchmarking Security in Retrieval-Augmented Generation of Large Language Model (2025.acl-long)

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Xun Liang, Simin Niu, Zhiyu Li, Sensen Zhang, Hanyu Wang, Feiyu Xiong, Zhaoxin Fan, Bo Tang, Jihao Zhao, Jiawei Yang, Shichao Song, Mengwei Wang

Robustness in NLP

Challenge:	Existing approaches to integrating external knowledge into large language models (LLMs) however, the incorporation of external knowledge increases the vulnerability of LLMs .
Approach:	They propose a benchmark to evaluate the RAG security using a dataset . they classify attack tasks into silver noise, inter-context conflict, soft ad, and white Denial-of-Service .
Outcome:	The proposed benchmark evaluates the security of RAG against 14 representative RAG components.

Better Late Than Never: Model-Agnostic Hallucination Post-Processing Framework Towards Clinical Text Summarization (2024.findings-acl)

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Songda Li, Yunqi Zhang, Chunyuan Deng, Yake Niu, Hui Zhao

Text Generation

Challenge:	Existing methods for generating concise and coherent summaries may include unintended text with hallucinations, causing computational costs.
Approach:	They propose a model-agnostic framework to post-process medical hallucinations . MEDAL integrates with any medical summarization model, requiring no additional computational overhead .
Outcome:	MEDAL can post-process medical hallucinations without additional computational overhead.

CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion (2022.acl-long)

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Guanglin Niu, Bo Li, Yongfei Zhang, Shiliang Pu

UniCreative: Unifying Long-form Logic and Short-form Sparkle via Reference-Free Reinforcement Learning (2026.findings-acl)

Challenge:	Existing knowledge graph embedding techniques rely on fact-view data to predict missing links between entities, limiting their performance.
Approach:	They propose a commonsense-aware knowledge embedding framework which generates commonsensense from factual triples with entity concepts for a KGC task.
Outcome:	The proposed framework could produce high-quality negative triples and joint commonsense and fact-view link prediction.

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Xiaolong Wei, Zerun Zhu, Simin Niu, Xingyu Zhang, Peiying Yu, Changxuan Xiao, Yuchen Li, Jicheng Yang, Zhejun Zhao, Chong Meng, Long Xia, Daiting Shi

Challenge:	Existing alignment paradigms for creative writing use static reward signals and supervised data.
Approach:	They propose a constraint-aware reward model that synthesizes query-specific criteria to provide fine-grained preference judgments.
Outcome:	The proposed framework aligns models with human preferences across content quality and structural paradigms without supervised fine-tuning and ground-truth references.

Unlocking the Black Box of Latent Reasoning: An Interpretability-Guided Approach to Intervention (2026.acl-long)

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Shuochen Chang, Tong Bai, Xiaofeng Zhang, Qianli Ma, Qingyang Liu, Zhaohe Liao, Yibo Miao, Li Niu