Papers by Yuan Meng

FusionDTI: Fine-grained Binding Discovery with Token-level Fusion for Drug-Target Interaction (2025.findings-emnlp)

Challenge:	Quantization has shown promise for Large Language Models, but current methods require lengthy training to alleviate quantization loss.
Approach:	They propose to decouple weights and incorporate Low-Rank adapters to reduce weight sharing . they validate the approach on LLaMA2 families and Mistral on downstream evaluation .
Outcome:	The proposed approach shows high performance while reducing deployment time faced with multiple scenarios.

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Zhaohan Meng, Zaiqiao Meng, Ke Yuan, Iadh Ounis

Infinity-Parser: Layout-Aware Reinforcement Learning with High-quality Document Parsing Dataset (2026.findings-acl)

Challenge:	despite advances in DTI models, models often struggle to capture fine-grained interactions between drugs and proteins.
Approach:	They propose a novel drug-target interaction model that uses a token-level module to learn fine-grained information for drug-target interactions.
Outcome:	The proposed model learns fine-grained information for drug-target interaction . it mitigates sequence fragment invalidation and incorporates the structure-aware vocabulary of target proteins .

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Baode Wang, Biao Wu, Weizhen Li, Meng Fang, Zuming Huang, Jun Huang, Yanjie Liang, Haozhe Wang, Ling Chen, Wei Chu, Yuan Qi

Multimodality

Challenge:	Existing supervised fine-tuning methods struggle to generalize across document types, leading to poor performance.
Approach:	They propose layoutRL, a reinforcement learning framework that optimizes layout understanding through composite rewards integrating normalized edit distance, paragraph count accuracy, and reading order preservation.
Outcome:	The proposed model outperforms specialized document parsing systems and general-purpose vision-language models on a broad range of document types, languages, and structural complexities.

Search Augmented Instruction Learning (2023.findings-emnlp)

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Hongyin Luo, Tianhua Zhang, Yung-Sung Chuang, Yuan Gong, Yoon Kim, Xixin Wu, Helen Meng, James Glass

Responsible & Trustworthy NLP

Challenge:	Large language models (LLMs) have been significantly improved by instruction fine-tuning, but still lack transparency and the ability to utilize up-to-date knowledge and information.
Approach:	They propose a search-augmented instruction learning model which grounds the language generation and instruction following abilities on complex search results generated by in-house and external search engines.
Outcome:	The proposed model outperforms plain LLMs on zero-shot language tasks and can generate both natural and programming languages following natural language guidance and requests.

Word-level Textual Adversarial Attacking as Combinatorial Optimization (2020.acl-main)

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Yuan Zang, Fanchao Qi, Chenghao Yang, Zhiyuan Liu, Meng Zhang, Qun Liu, Maosong Sun

Robustness in NLP

Challenge:	Existing word-level attack models are far from perfect because of unsuitable search space reduction methods and inefficient optimization algorithms.
Approach:	They propose a novel adversarial adversarialist model that incorporates word substitution and particle swarm optimization to solve two problems separately.
Outcome:	The proposed model achieves much higher success rates and crafts more high-quality adversarial examples as compared to baseline methods.

Tackling Modality Heterogeneity with Multi-View Calibration Network for Multimodal Sentiment Detection (2023.acl-long)

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Yiwei Wei, Shaozu Yuan, Ruosong Yang, Lei Shen, Zhangmeizhi Li, Longbiao Wang, Meng Chen

Multimodality

Challenge:	Existing studies focus on fusing different features but ignore the challenge of modality heterogeneity.
Approach:	They propose a text-guided fusion module with novel Sparse-Attention to reduce the negative impacts of redundant visual elements and a sentiment-based congruity constraint task to calibrate the feature shift in the representation space.
Outcome:	The proposed model is competitive against existing methods and achieves state-of-the-art results on two public benchmark datasets.

See the World, Discover Knowledge: A Chinese Factuality Evaluation for Large Vision Language Models (2025.findings-acl)

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Jihao Gu, Yingyao Wang, Pi Bu, Chen Wang, Ziming Wang, Tengtao Song, Donglai Wei, Jiale Yuan, Yingxiu Zhao, Yancheng He, Shilong Li, Jiaheng Liu, Meng Cao, Jun Song, Yingshui Tan, Xiang Li, Wenbo Su, Xiaoyong Zhu, Bo Zheng

Multimodality

Challenge:	Existing models for large vision language models do not fully reflect their knowledge capacity and reliability, resulting in erroneous outputs that do not align with the image content or provide answers lacking knowledge evidence.
Approach:	They propose a Chinese-based benchmark for visual factuality across 8 major topics and 56 subtopics and a multi-hop question construction.
Outcome:	The proposed model decouples visual factuality into two parts: seeing the world and discovering knowledge.

Enhancing Input-Label Mapping in In-Context Learning with Contrastive Decoding (2025.acl-short)

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Keqin Peng, Liang Ding, Yuanxin Ouyang, Meng Fang, Yancheng Yuan, Dacheng Tao

An Empirical Study on Neural Keyphrase Generation (2021.naacl-main)

Challenge:	Prior research has found that large language models overlook input-label mapping information in ICL, relying more on their pre-trained knowledge.
Approach:	They propose a novel method that contrasts input-label mappings between positive and negative in-context examples to improve model performance.
Outcome:	The proposed method improves performance on 7 natural language understanding tasks without additional training.

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Rui Meng, Xingdi Yuan, Tong Wang, Sanqiang Zhao, Adam Trischler, Daqing He

Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality Estimation (2024.emnlp-main)

Challenge:	Recent years have seen a flourishing of neural keyphrase generation (KPG) works, including the release of several large-scale datasets and a host of new models to tackle them.
Approach:	They propose to compare the generalizability of KPG models with other models by analyzing the most crucial factors that may affect their generalizarability.
Outcome:	The proposed model can be used to predict keyphrases from a set of input sequences, and it can be compared with existing models.

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Yuan Ge, Yilun Liu, Chi Hu, Weibin Meng, Shimin Tao, Xiaofeng Zhao, Mahong Xia, Zhang Li, Boxing Chen, Hao Yang, Bei Li, Tong Xiao, JingBo Zhu

BizCompass: Benchmarking the Reasoning Capabilities of LLMs in Business Knowledge and Applications (2026.findings-acl)

Challenge:	Existing methods for instruction data selection have limitations such as relying on fragile external APIs, being affected by biases in GPT models, or reducing the diversity of the selected instruction dataset.
Approach:	They propose an industrial-friendly, expert-aligned and diversity-preserved instruction data selection method: Clustering and Ranking (CaR).
Outcome:	The proposed method outperforms Alpaca's existing methods by 32.1% in GPT-4 evaluations.

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Jianing Hao, Yuhe Wu, Yuanjian Xu, Shichang Meng, Shuai Yuan, Wei Zeng, Zixuan Wang, Guang Zhang

Reasoning

Challenge:	Existing benchmarks focus on narrow tasks and leave a fundamental question unanswered . Existing models only focus on specific tasks, requiring rigorous reasoning and knowledge .
Approach:	They propose a benchmark to connect theoretical foundations with practical business knowledge and applications.
Outcome:	The benchmark systematically evaluates both open-source and commercial LLMs . it reveals how theoretical knowledge translates into practical performance in business .

TEF: Causality-Aware Taxonomy Expansion via Front-Door Criterion (2025.coling-main)

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Yuan Meng, Songlin Zhai, Yuxin Zhang, Zhongjian Hu, Guilin Qi

Responsible & Trustworthy NLP

Challenge:	Existing research still faces spurious query-anchor matching due to unobserved factors.
Approach:	They propose a model that uses the front-door criteria to decompose the expansion process into a parser module and a connector to isolate confounding effects.
Outcome:	Extensive experiments on three benchmarks validate the effectiveness of the proposed model.

APB-V: Accelerating Long-Video Understanding via Sequence-Parallelism-aware Approximate Attention (2026.acl-long)

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Yuxiang Huang, Mingye Li, Xu Han, Chaojun Xiao, Weilin Zhao, Ao Sun, Ziqi Yuan, Hao Zhou, Fandong Meng, Zhiyuan Liu

From Mimesis to Metamorphosis: Evolving VLM Judges via In-Context Comparing and Knowledge Internalization (2026.findings-acl)

Challenge:	Existing methods for long-video inference use compression or sparse attention . existing methods restrict LMMs from handling longer, more complex videos .
Approach:	They propose a sequence-parallel framework with optimized attention that accelerates long-video inference across multiple GPUs.
Outcome:	The proposed framework delivers speedups of 12.72x, 1.70x, and 1.18x over FlashAttn, ZigZagRing, and APB without significant performance loss.

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Juntuo Wang, Yuming Qiao, Yifan Yang, Lunxi Yuan, Liang Luo, Dan Meng

Information Retrieval

Challenge:	Existing approaches to subjective assessment are inconsistent and inconsistent due to inconsistent scales and inherent preference biases.
Approach:	They propose a framework that operationalizes subjective assessment as comparative analysis and internalizes it via Language Buttons.
Outcome:	The proposed framework achieves state-of-the-art performance across multiple benchmarks and is scale-steerable.

Natural Language Embedded Programs for Hybrid Language Symbolic Reasoning (2024.findings-naacl)

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Tianhua Zhang, Jiaxin Ge, Hongyin Luo, Yung-Sung Chuang, Mingye Gao, Yuan Gong, Yoon Kim, Xixin Wu, Helen Meng, James Glass

Few-Shot Table Understanding: A Benchmark Dataset and Pre-Training Baseline (2022.coling-1)

Challenge:	Existing methods for surfacing symbolic reasoning capabilities are limited to narrow tasks . arithmetic computations are unnatural to perform in pure language space, and hence present difficulties for LLMs.
Approach:	They propose a natural language embedded program framework for solving symbolic reasoning tasks.
Outcome:	The proposed framework improves on strong baselines across math and symbolic reasoning, text classification, question answering, and instruction following tasks.

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Ruixue Liu, Shaozu Yuan, Aijun Dai, Lei Shen, Tiangang Zhu, Meng Chen, Xiaodong He

Modality-Aware Neuron Pruning for Unlearning in Multimodal Large Language Models (2025.acl-long)

Challenge:	Pre-trained language models have demonstrated their effectiveness for few-shot table understanding, but few-shoot table understanding is rarely explored due to the deficiency of public table pre-training corpus and well-defined downstream benchmark tasks.
Approach:	They establish a benchmark dataset and use it to explore few-shot table understanding in Chinese.
Outcome:	The proposed model improves the few-shot table understanding in Chinese.

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Zheyuan Liu, Guangyao Dou, Xiangchi Yuan, Chunhui Zhang, Zhaoxuan Tan, Meng Jiang

Protecting Privacy in Multimodal Large Language Models with MLLMU-Bench (2025.naacl-long)

Challenge:	Large Language Models and Multimodal Large Language Modells can memorize sensitive information, raising ethical and privacy concerns.
Approach:	They propose a novel unlearning framework that selectively clips neurons based on their relative importance to the targeted forget data.
Outcome:	The proposed framework selectively clips neurons based on their relative importance to the targeted forget data, curated for different modalities.

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Zheyuan Liu, Guangyao Dou, Mengzhao Jia, Zhaoxuan Tan, Qingkai Zeng, Yongle Yuan, Meng Jiang

Metaphor Reasoning is Meta-reasoning (2026.acl-long)

Challenge:	Large Language Models (LLMs) and Multimodal Large Language models (MLLMs) trained on vast web corpora can memorize and disclose individuals’ confidential and private data, raising legal and ethical concerns.
Approach:	They propose a benchmark to assess unlearning algorithms from multiple perspectives and provide a baseline for existing generative models.
Outcome:	The proposed benchmark consists of 500 fictitious profiles and 153 profiles for public celebrities, evaluated from both multimodal (image+text) and unimodal (text) perspectives.

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Qianyu He, Junting Lu, Yikai Zhang, Siyu Yuan, Xiaojun Meng, Jiansheng Wei, Jiaqing Liang, Yanghua Xiao

Reasoning

Challenge:	Existing work on metaphor reasoning's impact on reasoning abilities is limited.
Approach:	They propose a system for synthesizing metaphorical riddles that satisfy five quality dimensions: diverse, balanced, reasoning-oriented, challenging, and verifiable.
Outcome:	The proposed system improves reasoning abilities across six domains using only thousands of metaphorical riddles.

On the Vulnerability of Text Sanitization (2025.naacl-long)

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Meng Tong, Kejiang Chen, Xiaojian Yuan, Jiayang Liu, Weiming Zhang, Nenghai Yu, Jie Zhang

Responsible & Trustworthy NLP

Challenge:	Existing reconstruction attacks on text sanitization are not able to accurately assess their effectiveness.
Approach:	They propose to use ASR to measure the effectiveness of reconstruction attacks to evaluate sanitization performance.
Outcome:	The proposed reconstruction attacks achieve a 46.4% improvement in ASR over the state-of-the-art baseline with a privacy budget of =4.0 on the SST-2 dataset.

Parameter-Aware Contrastive Knowledge Editing: Tracing and Rectifying based on Critical Transmission Paths (2025.acl-long)

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Songlin Zhai, Yuan Meng, Yuxin Zhang, Guilin Qi

Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents (2021.acl-short)

Challenge:	Large language models (LLMs) encode vast amounts of knowledge in their parameters, but the acquired knowledge can be incorrect or outdated over time, necessitating rectification after pre-training.
Approach:	They propose a method that captures key information flows that influence model predictions . they propose 'critical transmission paths' to improve model editing .
Outcome:	The proposed method improves on two prominent datasets and three widely used LLMs.

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Rui Meng, Khushboo Thaker, Lei Zhang, Yue Dong, Xingdi Yuan, Tong Wang, Daqing He

SOAR: Supervision from Observation for Agentic Reinforcement Learning (2026.acl-long)

Challenge:	Faceted summarization provides briefings of a document from different perspectives.
Approach:	They propose a faceted summarization benchmark built on Emerald journal articles . they propose faceted models that bring structure into faceted documents .
Outcome:	The proposed benchmark is based on Emerald journal articles and covers a diverse range of domains.

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Meng Li, Lei Li, Xiting Wang, Yi Yuan, Zheng Wei, null Brucebian, Zang Li

Reasoning

Challenge:	Prior work assigns supervision based on outcome rewards or external reward models, but ignores environment observations, a critical source of learning.
Approach:	They propose a supervision-based agentic reinforcement learning system that integrates environment observations as an explicit supervision signal.
Outcome:	The proposed model improves performance on reasoning and deep research tasks while reducing erroneous and inefficient tool usage.

Towards Intrinsic Interpretability of Large Language Models: A Survey of Design Principles and Architectures (2026.acl-long)

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Yutong Gao, Qinglin Meng, Yuan Zhou, Liangming Pan

Explainability & Interpretability in NLP

Challenge:	Existing studies on explainable AI focus on post-hoc explanation methods that interpret trained models through external approximations.
Approach:	They propose to categorize existing approaches into five design paradigms: functional transparency, concept alignment, representational decomposability, explicit modularization, and latent sparsity induction.
Outcome:	The proposed approaches are categorized into five design paradigms: functional transparency, concept alignment, representational decomposability, explicit modularization, and latent sparsity induction.

Can Pretrained Language Models (Yet) Reason Deductively? (2023.eacl-main)

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Zhangdie Yuan, Songbo Hu, Ivan Vulić, Anna Korhonen, Zaiqiao Meng

One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases (2020.acl-main)

Challenge:	Acquiring factual knowledge with Pretrained Language Models (PLMs) has attracted increasing attention, showing promising performance in many knowledge-intensive tasks.
Approach:	They conduct a comprehensive evaluation of the learnable deductive reasoning capability of pretrained language models and compare their performance against simple adversarial surface form edits.
Outcome:	The models are able to generalise learned logic rules and perform inconsistently against simple adversarial surface form edits, but catastrophically forget the previously learnt knowledge.

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Xingdi Yuan, Tong Wang, Rui Meng, Khushboo Thaker, Peter Brusilovsky, Daqing He, Adam Trischler

DialogVCS: Robust Natural Language Understanding in Dialogue System Upgrade (2024.naacl-long)

Challenge:	Existing models for keyphrase generation do not provide a desideratum for the number of keyphrases in texts.
Approach:	They propose a recurrent generative model that generates multiple keyphrases as delimiter-separated sequences.
Outcome:	The proposed model outperforms baseline models on all datasets.

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Zefan Cai, Xin Zheng, Tianyu Liu, Haoran Meng, Jiaqi Han, Gang Yuan, Binghuai Lin, Baobao Chang, Yunbo Cao

Dialogue Systems & Conversational Agents

Challenge:	Existing models for natural language understanding are based on a well-defined intent 1 ontology.
Approach:	They propose to retrain the natural language understanding model as new data from real users are merged into existing data.
Outcome:	The proposed model shows that the semantically entangled intents can be recognized with an automatic workflow.

RAP: Efficient Text-Video Retrieval with Sparse-and-Correlated Adapter (2024.findings-acl)

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Meng Cao, Haoran Tang, Jinfa Huang, Peng Jin, Can Zhang, Ruyang Liu, Long Chen, Xiaodan Liang, Li Yuan, Ge Li

Green & Sustainable NLP

Challenge:	Text-Video Retrieval (TVR) aims to align relevant video content with natural language queries.
Approach:	They propose to conduct efficient text-video Retrieval with a salient-and-correlated AdaPter . they propose a low-rank modulation module to refine per-image features from frozen CLIP backbone .
Outcome:	Experiments on four TVR datasets show that the proposed method performs better than other methods.

General-to-Specific Transfer Labeling for Domain Adaptable Keyphrase Generation (2023.findings-acl)

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Rui Meng, Tong Wang, Xingdi Yuan, Yingbo Zhou, Daqing He