Papers by Rui Yang

OpenForecast: A Large-Scale Open-Ended Event Forecasting Dataset (2025.coling-main)

Challenge:	Existing approaches to multimodal representation learning focus on directional alignment and embedding magnitudes (L2-norm) however, these methods often fail to account for the intrinsic role of L2-norm in the contrastive process.
Approach:	They propose a plug-and-play framework that optimizes L2-norm alignment and Directional consistency jointly.
Outcome:	The proposed framework achieves consistent and significant performance gains over established baselines across 95 tasks using UniIR and VLM2Vec-V2 frameworks.

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Zhen Wang, Xi Zhou, Yating Yang, Bo Ma, Lei Wang, Rui Dong, Azmat Anwar

Challenge:	Existing closed-ended event forecasting methods are constrained by a limited answer space.
Approach:	They introduce OpenForecast, a large-scale open-ended dataset with three open-ending event forecasting tasks and an automatic LLM-based method for complex events.
Outcome:	The proposed method can be used to evaluate the ability of complex event forecasting of large language models.

Towards Modern Topic Models: A Survey of Taxonomies and Paradigm Shifts from Algorithm-Centric to LLM-Centered Topic Analysis (2026.findings-acl)

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Xuan Xu, Zhongliang Yang, Haolun Li, Rui Tian, Beilin Chu, J Song, Yu Li, Shaolin Tan, Linna Zhou

Towards Efficient Large Language Model Serving: A Survey on System-Aware KV Cache Optimization (2026.findings-acl)

Challenge:	Topic modeling (TM) is a classic unsupervised learning task in the field of natural language processing.
Approach:	They propose a new taxonomy that emphasizes the role of LLMs and the design of end-to-end workflows.
Outcome:	The proposed taxonomy emphasizes the role of LLMs and the design of end-to-end workflows.

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Jiantong Jiang, Peiyu Yang, Rui Zhang, Feng Liu

Enhancing Medical Dialogue Generation through Knowledge Refinement and Dynamic Prompt Adjustment (2025.findings-acl)

Challenge:	Large language models (LLMs) are a powerful tool for high-performance inference serving.
Approach:	They focus on system-aware KV infrastructure for serving LLMs . they analyze cross-behavior co-design affinity and behavior-objective links .
Outcome:	The proposed key-value (KV) cache is crucial for low-latency, high-throughput LLM inference serving.

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Hongda Sun, Jiaren Peng, Wenzhong Yang, Liang He, Bo Du, Rui Yan

TellWhisper: Tell Whisper Who Speaks When (2026.acl-long)

Challenge:	Medical dialogue systems (MDS) struggle to identify relevant medical knowledge and generate accurate responses.
Approach:	They propose a medical dialogue system that integrates knowledge refining and dynamic prompt adjustment to improve medical knowledge and accuracy.
Outcome:	The proposed system outperforms state-of-the-art systems in both generation quality and medical entity accuracy.

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Yifan Hu, Peiji Yang, Zhisheng Wang, Yicheng Zhong, Rui Liu

Rethinking Diverse Human Preference Learning through Principal Component Analysis (2025.findings-acl)

Challenge:	Existing approaches decouple temporal modeling and speaker modeling when addressing 'when' and 'who' . a new framework that couples temporal structure with speaker dynamics is proposed to address these limitations .
Approach:	They propose a framework that couples temporal and speaker identity within the speech encoder . they propose TS-RoPE, a time-speaker rotary positional encoding that partitions Query/Key channels into temporal, speaker subspaces and applies region-specific rotations to align "when" and "who" cues in selfattention.
Outcome:	The proposed framework couples temporal structure with speaker dynamics in speech encoder . it uses frame-level speaker activity to estimate speaker-activity estimates .

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Feng Luo, Rui Yang, Hao Sun, Chunyuan Deng, Jiarui Yao, Jingyan Shen, Huan Zhang, Hanjie Chen

Explainability & Interpretability in NLP

Challenge:	Decomposed Reward Models extract diverse human preferences from binary comparisons without fine-grained annotations.
Approach:	They propose a decomposed reward model that extracts diverse human preferences from binary comparisons without fine-grained annotations.
Outcome:	The proposed approach extracts diverse human preferences from binary comparisons without fine-grained annotations.

An Interpretable Neuro-Symbolic Reasoning Framework for Task-Oriented Dialogue Generation (2022.acl-long)

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Shiquan Yang, Rui Zhang, Sarah Erfani, Jey Han Lau

Chart-MRAG: Benchmarking Multimodal Retrieval Augmented Generation on Chart-based Documents (2026.acl-long)

Challenge:	Existing approaches to interpret task-oriented dialogue systems employ an implicit reasoning strategy that makes the model predictions uninterpretable to humans.
Approach:	They propose a neuro-symbolic approach that performs explicit reasoning that justifies model decisions by reasoning chains.
Outcome:	The proposed approach achieves better results and introduces an interpretable decision process.

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null Ymyang, Jiang Zhong, Li Jin, Xiao Sun, Jingwang Huang, null Gaojinpeng, Qing Liu, Yang Bai, Jingyuan Zhang, Rui Jiang, Qin Lei, Kaiwen Wei

Mining the Past with Dual Criteria: Integrating Three types of Historical Information for Context-aware Event Forecasting (2025.emnlp-main)

Challenge:	Existing benchmarks focus on simple image-text interactions, overlooking complex visual formats like charts.
Approach:	They propose a semi-automatic framework for generating evaluation samples through multi-modal keypoint extraction, knowledge graph construction, and qa pair synthesis.
Outcome:	The proposed framework generates 4,738 question-answering pairs across 8 domains from real-world documents.

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Rong Ma, Lei Wang, Yating Yang, Bo Ma, Rui Dong, Fengyi Yang, Ahtamjan Ahmat, Kaiwen Lu, Xinyue Wang

Structured Data in NLP

Challenge:	Existing methods focus on entities and structural dependencies but overlook implicitly relevant information.
Approach:	They propose a method that leverages event semantics for relevance modeling and incorporates a self-supervised semantic filter based on factual event associations to capture implicitly relevant historical information.
Outcome:	The proposed method outperforms existing methods on three public benchmark datasets and is highly effective on two structured temporal knowledge graph forecasting datasets.

Enhancing multi-modal Relation Extraction with Reinforcement Learning Guided Graph Diffusion Framework (2025.coling-main)

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Rui Yang, Rajiv Gupta

The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit (2025.acl-long)

Challenge:	Existing methods for cross-modal relation extraction focus on single-modal data, which limits their use in real-world situations.
Approach:	They propose a framework that leverages pre-trained models to encode multi-modal data into scene graphs and combine them into a cross-modal graph.
Outcome:	The proposed model outperforms existing methods on multi-modal relation extraction tasks.

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Huixue Zhou, Hengrui Gu, Zaifu Zhan, Xi Liu, Kaixiong Zhou, Yongkang Xiao, Mingfu Liang, Srinivas Prasad Govindan, Piyush Chawla, Jiyan Yang, Xiangfei Meng, Huayu Li, Buyun Zhang, Liang Luo, Wen-Yen Chen, Yiping Han, Bo Long, Rui Zhang, Tianlong Chen

Green & Sustainable NLP

Challenge:	Existing frameworks for Large Language Models (LLMs) for Click-Through Rate prediction require a careful balance between computational efficiency and predictive accuracy.
Approach:	They propose a framework that integrates Retrieval-Augmented Generation with a novel multi-head early exit architecture to address both challenges.
Outcome:	The proposed framework reduces retrieval time while maintaining high model performance.

CMR Scaling Law: Predicting Critical Mixture Ratios for Continual Pre-training of Language Models (2024.emnlp-main)

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Jiawei Gu, Zacc Yang, Chuanghao Ding, Rui Zhao, Fei Tan

Treasures Outside Contexts: Improving Event Detection via Global Statistics (2021.emnlp-main)

Challenge:	Large Language Models (LLMs) excel in diverse tasks but often underperform in specialized fields due to limited domain-specific or proprietary corpus.
Approach:	They propose a power-law relationship between loss, mixture ratio, and training tokens scale and formalize the trade-off between general and domain-specific capabilities.
Outcome:	The proposed model achieves the desired domain transfer while maintaining general ability and highest utilization of available resources.

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Rui Li, Wenlin Zhao, Cheng Yang, Sen Su

Sentence-Level Agreement for Neural Machine Translation (P19-1)

Challenge:	Existing neural-based ED models are confused by changeable contexts during testing . we propose a system that extracts statistical event features from word-event cooccurrence frequencies .
Approach:	They propose to integrate a set of statistical event features from word-event co-occurrence frequencies into the training set to cooperate with contextual features.
Outcome:	The proposed model outperforms ten strong baselines on ACE2005 and KBP2015 datasets.

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Mingming Yang, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita, Min Zhang, Tiejun Zhao

Amalgamating Knowledge from Two Teachers for Task-oriented Dialogue System with Adversarial Training (2020.emnlp-main)

Challenge:	Empirical results show that a sentence-level agreement module can significantly improve the performance of neural machine translation (NMT)
Approach:	They propose a sentence-level agreement module to minimize the difference between the representation of source and target sentences.
Outcome:	Empirical results show the proposed agreement module significantly improves translation performance.

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Wanwei He, Min Yang, Rui Yan, Chengming Li, Ying Shen, Ruifeng Xu

Robustness in NLP

Challenge:	Existing approaches to task-oriented dialogue systems require a large number of handcrafted features and labels.
Approach:	They propose a "Two-Teacher One-Student" learning framework for task-oriented dialogue . the framework amalgamates knowledge from two teacher networks and provides guidance .
Outcome:	The proposed framework outperforms baseline methods on two benchmark datasets . it can retrieve accurate KB entities and generate human-like responses simultaneously .

Meta-Reasoning: Semantics-Symbol Deconstruction for Large Language Models (2024.findings-acl)

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Yiming Wang, Zhuosheng Zhang, Pei Zhang, Baosong Yang, Rui Wang

AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments (2025.acl-long)

Challenge:	Existing methods rely on syntactically mapping natural languages to complete formal languages like Python and SQL.
Approach:	They propose to deconstruct reasoning-independent semantic information into generic symbolic representations, thereby efficiently capturing more generalized reasoning knowledge.
Outcome:	The proposed method improves in-context reasoning accuracy, learning efficiency, out-of-domain generalization, and output stability compared to the Chain-of thought technique.

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Zhiheng Xi, Yiwen Ding, Wenxiang Chen, Boyang Hong, Honglin Guo, Junzhe Wang, Xin Guo, Dingwen Yang, Chenyang Liao, Wei He, Songyang Gao, Lu Chen, Rui Zheng, Yicheng Zou, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang

Multi-Programming Language Sandbox for LLMs (2025.acl-demo)

Challenge:	Large language models (LLMs) are promising foundations to build generally-capable agents . however, the community lacks a unified interactive framework that covers diverse environments for comprehensive evaluation of agents.
Approach:	They propose a framework that features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction.
Outcome:	The proposed framework features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction.

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Shihan Dou, Jiazheng Zhang, Jianxiang Zang, Yunbo Tao, Weikang Zhou, Haoxiang Jia, Shichun Liu, Yuming Yang, Shenxi Wu, Zhiheng Xi, Muling Wu, Rui Zheng, Changze Lv, Limao Xiong, Shaoqing Zhang, Lin Zhang, Wenyu Zhan, Rongxiang Weng, Jingang Wang, Xunliang Cai, Yueming Wu, Ming Wen, Yixin Cao, Tao Gui, Xipeng Qiu, Qi Zhang, Xuanjing Huang

Beyond Single Frames: Can LMMs Comprehend Implicit Narratives in Comic Strip? (2025.findings-emnlp)

Challenge:	MPLSandbox is an out-of-the-box multi-programming language sandbox designed to provide unified and comprehensive feedback from compiler and analysis tools for Large Language Models (LLMs).
Approach:	They propose a multi-programming language sandbox that provides unified feedback from compilers and analysis tools for Large Language Models.
Outcome:	The proposed multi-language sandbox can provide comprehensive feedback from compilers and analysis tools for large language models (LLMs).

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Xiaochen Wang, Heming Xia, Jialin Song, Longyu Guan, Qingxiu Dong, Rui Li, Yixin Yang, Yifan Pu, Weiyao Luo, Yiru Wang, Xiangdi Meng, Wenjie Li, Zhifang Sui

Large Language Models as Source Planner for Personalized Knowledge-grounded Dialogues (2023.findings-emnlp)

Challenge:	Large Multimodal Models have demonstrated strong performance on vision-language benchmarks, yet current evaluations focus on single-image reasoning.
Approach:	STRIPCIPHER is a benchmark designed to evaluate model ability on understanding implicit narratives in silent comics.
Outcome:	STRIPCIPHER is a high-quality, human-annotated dataset featuring fine-grained annotations and comprehensive coverage of varying difficulty levels.

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Hongru Wang, Minda Hu, Yang Deng, Rui Wang, Fei Mi, Weichao Wang, Yasheng Wang, Wai-Chung Kwan, Irwin King, Kam-Fai Wong

Turn-PPO: Turn-Level Advantage Estimation with PPO for Improved Multi-Turn RL in Agentic LLMs (2026.findings-eacl)

Challenge:	Existing knowledge-grounded dialogue systems focus on a single knowledge source or ignore the dependency between multiple knowledge sources.
Approach:	They propose a framework that integrates multiple knowledge sources and dependencies between them.
Outcome:	The proposed framework can produce persona-consistent and knowledge-enhanced responses on a knowledge-grounded dialogue dataset.

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Junbo Li, Peng Zhou, Rui Meng, Meet P. Vadera, Lihong Li, Yang Li

Reasoning

Challenge:	Reinforcement learning (RL) has re-emerged as a natural approach for training interactive LLM agents in real-world environments.
Approach:	They propose a variant that operates on a turn-level MDP formulation, instead of the commonly used token-level one.
Outcome:	The proposed method is more robust than the widely used GRPO algorithm and more efficient than token-level MDPs.

LongCLI-Bench: A Preliminary Benchmark and Study for Long-horizon Agentic Programming in Command-Line Interfaces (2026.findings-acl)

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Yukang Feng, Jianwen Sun, Zelai Yang, Jiaxin Ai, Chuanhao Li, Zizhen Li, Fanrui Zhang, Kang He, Rui Ma, Jifan Lin, Jie Sun, Yang Xiao, Sizhuo Zhou, Wenxiao Wu, Yiming Liu, Pengfei Liu, Shenglin Zhang, Kaipeng Zhang

PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction (2021.acl-long)

Challenge:	Existing benchmarks for agentic programming in long-horizon command-line interface tasks are limited by short task horizons, data contamination from GitHub scraping, and a lack of fine-grained evaluation metrics.
Approach:	They propose a benchmark to evaluate agentic capabilities across long-horizon command-line interface tasks.
Outcome:	The proposed benchmarks cover four engineering categories: from scratch, feature addition, bug fixing, and refactoring.

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Hengyi Zheng, Rui Wen, Xi Chen, Yifan Yang, Yunyan Zhang, Ziheng Zhang, Ningyu Zhang, Bin Qin, Xu Ming, Yefeng Zheng

UniCOQE: Unified Comparative Opinion Quintuple Extraction As A Set (2023.findings-acl)

Challenge:	Recent methods for extracting entities and relations from unstructured texts suffer from limitations, such as redundancy of relation prediction and inefficiency.
Approach:	They propose a joint relational triple extraction framework based on Potential Relation and Global Correspondence (PRGC) they propose overlapping triples for relation prediction and relation-relational alignment .
Outcome:	The proposed framework achieves state-of-the-art performance on public benchmarks with higher efficiency and consistent performance gain on complex scenarios of overlapping triples.

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Zinong Yang, Feng Xu, Jianfei Yu, Rui Xia

SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-Domain Text-to-SQL Task (D18-1)

Challenge:	Existing methods decompose the COQE task into multiple subtasks and solve them in a pipeline manner, but ignore the intrinsic connection between subtask and the error propagation among stages.
Approach:	They propose a unified generative model that solves COQE in one shot by concatenating all the comparative tuples into a target output sequence.
Outcome:	The proposed model significantly outperforms the SOTA method on multiple benchmarks and ablation experiments.

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Tao Yu, Michihiro Yasunaga, Kai Yang, Rui Zhang, Dongxu Wang, Zifan Li, Dragomir Radev

Model Surgery: Modulating LLM’s Behavior Via Simple Parameter Editing (2025.naacl-long)

Challenge:	Existing studies in text-to-SQL do not require generating complex SQL queries with multiple clauses or sub-queries.
Approach:	They propose a syntax tree network to address the complex text-to-SQL generation task.
Outcome:	The proposed model outperforms the current state-of-the-art model by 9.5% on a large text-to-SQL corpus.

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Huanqian Wang, Yang Yue, Rui Lu, Jingxin Shi, Andrew Zhao, Shenzhi Wang, Shiji Song, Gao Huang

Challenge:	Current approaches for detoxification or preventing jailbreaking involve fine-tuning billions of parameters through gradient descent with substantial computational cost.
Approach:	They propose to use supervised fine-tuning and Reinforcement Learning from human feedback to modify LLMs' behavior by directly editing a small subset of parameters.
Outcome:	Experiments show that editing a small subset of parameters can modulate specific behaviors of LLMs, such as detoxification and resistance to jailbreak, with only inference-level computational resources.

Clustering-Aware Negative Sampling for Unsupervised Sentence Representation (2023.findings-acl)

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Jinghao Deng, Fanqi Wan, Tao Yang, Xiaojun Quan, Rui Wang

Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs (2023.findings-emnlp)

Challenge:	Using clustering-aware learning, in-batch negatives are often ignored in sentence representation learning.
Approach:	They propose a method that integrates cluster information into contrastive learning for unsupervised sentence representation learning.
Outcome:	The proposed method compares favorably with baselines on semantic textual similarity tasks.

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Hongru Wang, Rui Wang, Fei Mi, Yang Deng, Zezhong Wang, Bin Liang, Ruifeng Xu, Kam-Fai Wong

ProLongVid: A Simple but Strong Baseline for Long-context Video Instruction Tuning (2025.emnlp-main)

Challenge:	Existing LLMs generate responses based on the dialogue context, overlooking the underlying linguistic cues about the user status exhibited in the context.
Approach:	They propose a linguistic cue-based chain-of-thoughts method which enhances the LLMs inference with an intermediate reasoning step to find cues exhibited in the dialogue.
Outcome:	The proposed method outperforms standard prompting methods on in-depth dialogue questions and linguistic cues exhibited in the context.

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Rui Wang, Bohao Li, Xiyang Dai, Jianwei Yang, Yi-Ling Chen, Zhen Xing, Yifan Yang, Dongdong Chen, Xipeng Qiu, Zuxuan Wu, Yu-Gang Jiang

Large Language Model for Multi-Domain Translation: Benchmarking and Domain CoT Fine-tuning (2024.findings-emnlp)

Challenge:	Existing approaches to adapt image-focused models for video understanding have not been successful in analyzing long video sequences.
Approach:	They propose a video instruction dataset that outperforms existing video instruction data for fine-tuning MLLMs by incrementally increasing input context length.
Outcome:	The proposed model outperforms existing models on video benchmarks and outperformed proprietary models on VideoMME even with a compact 7B model.

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Tianxiang Hu, Pei Zhang, Baosong Yang, Jun Xie, Derek Wong, Rui Wang

GNN-encoder: Learning a Dual-encoder Architecture via Graph Neural Networks for Dense Passage Retrieval (2022.findings-emnlp)

Challenge:	Achieving consistent high-quality machine translation across diverse domains remains a challenge due to limited and imbalanced parallel training data available in various domains.
Approach:	They propose a domain Chain of Thought technique that uses the multi-domain intelligence of LLMs to improve translation performance.
Outcome:	The proposed method achieves significant improvements in translation accuracy and domain robustness over traditional fine-tuning on a small dataset of four domains.

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Jiduan Liu, Jiahao Liu, Yang Yang, Jingang Wang, Wei Wu, Dongyan Zhao, Rui Yan

Information Retrieval

Challenge:	Existing approaches to perform large-scale query-passage retrieval are term-based, but they lose interaction between query-pastage pairs.
Approach:	They propose to fuse query (passage) information into query representations via graph neural networks that are constructed by queries and their top retrieved passages.
Outcome:	The proposed model outperforms existing models on MSMARCO, Natural Questions and TriviaQA datasets and achieves the new state-of-the-art on these datasets.

Measuring Bargaining Abilities of LLMs: A Benchmark and A Buyer-Enhancement Method (2024.findings-acl)

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Tian Xia, Zhiwei He, Tong Ren, Yibo Miao, Zhuosheng Zhang, Yang Yang, Rui Wang

Challenge:	Using a novel approach, we can evaluate an agent’s bargaining abilities as an asymmetric incomplete information game.
Approach:	They propose an approach that integrates a deterministic Offer Generator and an LLM Narrator to create natural language sentences for generated offers.
Outcome:	The proposed approach improves the buyer’s deal rates from 26.67% to 88.88% and brings a ten times multiplication of profits on all baselines, even a model that has not been aligned.

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

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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.

MiCRo: Mixture Modeling and Context-aware Routing for Personalized Preference Learning (2025.emnlp-main)

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Jingyan Shen, Jiarui Yao, Rui Yang, Yifan Sun, Feng Luo, Rui Pan, Tong Zhang, Han Zhao

GraphCheck: Breaking Long-Term Text Barriers with Extracted Knowledge Graph-Powered Fact-Checking (2025.acl-long)

Challenge:	Existing reward models assume a global reward function, limiting personalization and pluralistic alignment.
Approach:	They propose a framework that leverages binary preference datasets to enhance personalized preference learning.
Outcome:	The proposed framework captures diverse human preferences without fine-grained annotations and significantly improves personalized preference learning on downstream tasks.

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Yingjian Chen, Haoran Liu, Yinhong Liu, Jinxiang Xie, Rui Yang, Han Yuan, Yanran Fu, Peng Yuan Zhou, Qingyu Chen, James Caverlee, Irene Li

Structured Data in NLP

Challenge:	Existing fact-checking methods that use large language models often generate subtle factual errors.
Approach:	They propose a fact-checking framework that uses extracted knowledge graphs to enhance text representation.
Outcome:	GraphCheck outperforms existing specialized fact-checkers on seven benchmarks spanning general and medical domains . Graph Neural Networks process extracted knowledge graphs as a soft prompt, enabling efficient fact- checking in a single inference call.

A Systematic Assessment of Language Models with Linguistic Minimal Pairs in Chinese (2026.tacl-1)

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Yikang Liu, Yeting Shen, Hongao Zhu, Lilong Xu, Zhiheng Qian, Siyuan Song, Kejia Zhang, Jialong Tang, Pei Zhang, Baosong Yang, Rui Wang, Hai Hu

Seeing but Not Thinking: Routing Distraction in Multimodal Mixture-of-Experts (2026.acl-long)

Challenge:	Using sub-linear length normalized log-probabilities (SLLN-LP), we find unequal lengths of sentences in minimal pairs difficult for LMs even up to 32B parameters.
Approach:	They propose to use ZhoBLiMP as a linguistic minimal pair benchmark for Chinese language models to mitigate biases.
Outcome:	The proposed metric mitigates biases in Chinese language models with over 100 paradigms . Anaphor, Quantifiers, and Ellipsis are difficult for LMs even up to 32B parameters .

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Haolei Xu, Haiwen Hong, Hongxing Li, Rui Zhou, Yang Zhang, Longtao Huang, Hui Xue, Yongliang Shen, Weiming Lu, Yueting Zhuang

Comprehensive Benchmarking of Long-Form Speech Generation in Diverse Scenarios (2026.findings-acl)

Challenge:	Existing multimodal Mixture-of-Experts models accurately perceive image content yet fail in subsequent reasoning . Seeing but not thinking phenomenon is a puzzling phenomenon .
Approach:	They propose a routing-guided intervention method that enhances domain expert activation.
Outcome:	The proposed method achieves consistent improvements on visual reasoning tasks.

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Changhao Pan, Rui Yang, Han Wang, Zhuan Zhou, Xuming He, Wenxiang Guo, Ziyue Jiang, Ruiqi Li, Yu Zhang, Chenyuhao Wen, Ke Lei, Xiang Yin, Jingyu Lu, Zhiyuan Zhu, Zhou Zhao

V-MAGE: A Game Evaluation Framework for Assessing Vision-Centric Capabilities in Multimodal Large Language Models (2026.findings-acl)

Challenge:	Existing evaluation benchmarks for long-form speech are limited to limited domains, creating a significant gap with the diverse downstream applications.
Approach:	They propose a benchmark that decomposes "long-form speech quality" into specific, disentangled dimensions.
Outcome:	The proposed benchmark decomposes “long-form speech quality” into specific, disentangled dimensions.

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Xiangxi Zheng, Linjie Li, Zhengyuan Yang, Ping Yu, Alex Jinpeng Wang, Rui Yan, Yuan Yao, Lijuan Wang

Curr-ReFT: Overcoming Training Bottlenecks in Small-scale Vision-Language Models via Curriculum Reinforcement Finetuning (2025.findings-emnlp)

Challenge:	Existing static image-text benchmarks are insufficient for evaluating multimodal large language models’ dynamic perception and interactive reasoning abilities.
Approach:	They propose a game-based evaluation framework to assess multimodal large language models’ visual reasoning in dynamic, continuous-space environments.
Outcome:	The proposed framework systematically assesses MLLMs’ visual reasoning in dynamic, continuous-space environments.

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Huilin Deng, Ding Zou, Xinghao Zhao, Rui Ma, Yanming Guo, Yang Cao, Yu Kang

Low-Resource Language Expansion and Translation Capacity Enhancement for LLM: A Study on the Uyghur (2025.coling-main)

Challenge:	State-of-the-art vision-language models require massive scaling that limits practical deployment.
Approach:	They propose to use supervised fine-tuning to train small-scale vision-language models but face out-of-domain collapse when trained with traditional supervised learning (SFT).
Outcome:	Experiments show that curr-reFT achieves state-of-the-art performance across visual tasks in both in- and out-of domain settings and benchmarks.

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Kaiwen Lu, Yating Yang, Fengyi Yang, Rui Dong, Bo Ma, Aihetamujiang Aihemaiti, Abibilla Atawulla, Lei Wang, Xi Zhou

InferPilot: Autonomous Inference Attacks Against ML Services With LLM-Based Agents (2026.findings-acl)

Challenge:	Extensive experiments have shown that our strategy effectively expands the low-resource languages supported by large language models and significantly enhances the model’s translation ability in Uyghur with less parallel data.
Approach:	They propose a direct preference optimization based on translation self-evolution to expand low-resource languages into large language models by using Uyghur as an example.
Outcome:	The proposed strategy expands low-resource languages supported by large language models and significantly enhances the model’s translation ability in Uyghur with less parallel data.

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Yixin Wu, Rui Wen, Chi Cui, Michael Backes, Yang Zhang

Robustness in NLP

Challenge:	Inference attacks are important for assessing model's robustness, but their implementation and parameters are challenging for non-experts.
Approach:	They propose an autonomous agent capable of conducting inference attacks without human intervention.
Outcome:	The proposed agent achieves a 100.0% task completion rate and near-expert attack performance with an average token cost of only 0.627 per run.

Interpretable Research Replication Prediction via Variational Contextual Consistency Sentence Masking (2022.findings-acl)

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Tianyi Luo, Rui Meng, Xin Wang, Yang Liu

Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards (2024.acl-long)

Challenge:	Existing methods for predicting research replication are insufficient especially for long research papers.
Approach:	They propose to build an interpretable neural model which can provide sentence-level explanations and apply weakly supervised approach to leverage large corpus of unlabeled datasets.
Outcome:	The proposed model can provide sentence-level explanations and leverage large unlabeled datasets to boost interpretability and improve prediction performance.

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Haoxiang Wang, Yong Lin, Wei Xiong, Rui Yang, Shizhe Diao, Shuang Qiu, Han Zhao, Tong Zhang

Challenge:	Reinforcement Learning from Human Feedback (RLHF) relies on scalar rewards to capture user preferences.
Approach:	They propose a framework that integrates multi-objective reward modeling to represent diverse preference profiles.
Outcome:	The proposed method improves performance across reward objectives and targets.

SparkRA: A Retrieval-Augmented Knowledge Service System Based on Spark Large Language Model (2024.emnlp-demo)

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Dayong Wu, Jiaqi Li, Baoxin Wang, Honghong Zhao, Siyuan Xue, Yanjie Yang, Zhijun Chang, Rui Zhang, Li Qian, Bo Wang, Shijin Wang, Zhixiong Zhang, Guoping Hu

Balancing Speciality and Versatility: a Coarse to Fine Framework for Supervised Fine-tuning Large Language Model (2024.findings-acl)

Challenge:	Large language models (LLMs) have shown remarkable achievements across various language tasks.
Approach:	They propose a scientific literature LLM and a knowledge service system based on it . they collect scientific literature and then pre-train it using autoregressive training .
Outcome:	The proposed system provides literature investigation, paper reading, and academic writing functions.

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Hengyuan Zhang, Yanru Wu, Dawei Li, Sak Yang, Rui Zhao, Yong Jiang, Fei Tan

Capturing Minds, Not Just Words: Enhancing Role-Playing Language Models with Personality-Indicative Data (2024.findings-emnlp)

Challenge:	Aligned Large Language Models exhibit remarkable versatility, capable of handling diverse real-world tasks.
Approach:	They propose a coarse to fine framework to fine-tune aligned Large Language Models to achieve a balance between speciality and versatility.
Outcome:	The proposed framework outperforms baseline methods across diverse tasks and model scales.

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Yiting Ran, Xintao Wang, Rui Xu, Xinfeng Yuan, Jiaqing Liang, Yanghua Xiao, Deqing Yang

BabelDOC: Better Layout-Preserving PDF Translation via Intermediate Representation (2026.acl-demo)

Challenge:	Existing role-playing models focus on character knowledge and tones, but lack personality-indicative data to capture characters' minds.
Approach:	They propose to enhance role-playing agents (RPAs) via personality-indicative data by asking psychological scales to capture broad aspects of personality traits in individuals.
Outcome:	The proposed model exhibits advanced role-playing capabilities for both general and personality-related evaluations.

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Yang Qi, Xiangyao Ma, Xiao Wang, Hao Wang, Rui Wang

Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task (D18-1)

Challenge:	Existing document translation pipelines face a tension between linguistic processing and layout preservation.
Approach:	They propose a framework for layout-preserving PDF translation that decouples visual layout metadata from semantic content.
Outcome:	The proposed framework improves layout fidelity, visual aesthetics, and terminology consistency over representative baselines while maintaining competitive translation precision.

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Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, Zilin Zhang, Dragomir Radev

Programming Languages in NLP

Challenge:	Existing datasets for semantic parsing are too small in terms of number of programs for training modern data-intensive models.
Approach:	They propose a large-scale complex and cross-domain semantic parsing task for a database . they use a dataset with 10,181 questions and 5,693 unique complex SQL queries .
Outcome:	The proposed task is different from previous tasks because it uses the same database and program . the best model achieves only 9.7% exact matching accuracy on a database split setting.

MACSum: Controllable Summarization with Mixed Attributes (2023.tacl-1)

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Yusen Zhang, Yang Liu, Ziyi Yang, Yuwei Fang, Yulong Chen, Dragomir Radev, Chenguang Zhu, Michael Zeng, Rui Zhang

Multi-perspective Preference Alignment of LLMs for Programming-Community Question Answering (2025.coling-main)

Challenge:	Existing work on controllable summarization with mixed attributes lacks designated annotations.
Approach:	They propose a human-annotated summarization benchmark for controllable summarizing with mixed attributes based on news and dialogue sources .
Outcome:	The proposed dataset contains human-annotated summarization datasets with mixed attributes . hard prompt models yield the best performance on most metrics and human evaluations . mixed-attribute control is still challenging for summarizing tasks .

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Hongyu Yang, Jiahui Hou, Liyang He, Rui Li

Curse of Knowledge: Your Guidance and Provided Knowledge are biasing LLM Judges in Complex Evaluation (2025.findings-emnlp)

Challenge:	Extensive experiments on a high-quality, real-world PCQA dataset validate its accuracy and preference.
Approach:	They propose a multi-perspective preference alignment for programming-community question answering to generate user-centric responses.
Outcome:	Experiments on a high-quality, real-world PCQA dataset validate the proposed model's accuracy and preference.

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Weiyuan Li, Xintao Wang, Siyu Yuan, Rui Xu, Jiangjie Chen, Qingqing Dong, Yanghua Xiao, Deqing Yang

Challenge:	a recent study has focused on simple settings, but their reliability in complex tasks remains understudied.
Approach:	They propose to use large language models as judges to evaluate reliability in complex tasks . they use a challenge benchmark to expose and quantify Auxiliary Information Induced Biases .
Outcome:	The proposed benchmark exposes and quantifies Auxiliary Information Induced Biases across 12 basic and 3 advanced scenarios.

Attention Optimization for Abstractive Document Summarization (D19-1)

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Min Gui, Junfeng Tian, Rui Wang, Zhenglu Yang

QAEval: Mixture of Evaluators for Question-Answering Task Evaluation (2025.acl-long)

Challenge:	Abstractive summarization models require attention to reproduce the most salient information.
Approach:	They propose to use local and global variances to augment the vanilla attention model to reproduce the most salient information and avoid repetitions.
Outcome:	The proposed attention refinement unit can reproduce the most salient information and avoid repetitions on CNN/Daily Mail dataset.

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Tan Yue, Rui Mao, Xuzhao Shi, Shuo Zhan, Zuhao Yang, Dongyan Zhao

Toward Optimal LLM Alignments Using Two-Player Games (2025.findings-emnlp)

Challenge:	Existing QA evaluation methods struggle with open-ended and unstructured responses.
Approach:	They propose a hybrid framework that combines rule-based reliability with LLM-based adaptability to overcome these challenges.
Outcome:	The proposed framework outperforms existing models like GPT-4o and Claude-3 in accuracy and cost.

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Rui Zheng, Hongyi Guo, Zhihan Liu, Xiaoying Zhang, Yuanshun Yao, Xiaojun Xu, Zhaoran Wang, Zhiheng Xi, Tao Gui, Qi Zhang, Xuanjing Huang, Yang Liu, Hang Li

Modeling Personalization in Continuous Space for Response Generation via Augmented Wasserstein Autoencoders (D19-1)

Challenge:	Alignment of large language models (LLM) is a process that ensures the model’s responses to user prompts align with human intentions and social values.
Approach:	They propose an alignment method based on a two-agent game consisting of an adversarial agent and a defensive agent.
Outcome:	The proposed method improves on a two-agent game with an adversarial agent and a defensive agent.

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Zhangming Chan, Juntao Li, Xiaopeng Yang, Xiuying Chen, Wenpeng Hu, Dongyan Zhao, Rui Yan

The Art of (Mis)alignment: How Fine-Tuning Methods Effectively Misalign and Realign LLMs in Post-Training (2026.findings-acl)

Challenge:	Existing work on variableal autoencoders and waterstein autoencoding models has shown significant progress in open-domain response generation.
Approach:	They propose to embed user-level and utterance-level information into two multimodal distributions and combine them into a mixed distribution.
Outcome:	The proposed model outperforms state-of-the-art models on a large-scale real-world dataset.

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Rui Zhang, Hongwei Li, Yun Shen, Xinyue Shen, Wenbo Jiang, Guowen Xu, Yang Liu, Michael Backes, Yang Zhang

ReAgent: Reversible Multi-Agent Reasoning for Knowledge-Enhanced Multi-Hop QA (2025.emnlp-main)

Challenge:	Misaligned large language models can magnify harm by exploiting them to undermine safety . et al., 2022b; Bai e.t., 2023): misalignment, realignment and model-specific resistance are important .
Approach:	They evaluate four methods to identify a mechanism asymmetry between attack and defense . they find that ORPO is most effective for misalignment, but DPO excels in realignment .
Outcome:	The proposed methods show a mechanism asymmetry between attack and defense . the proposed methods excel in realignment, but at the expense of model utility .

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Zhao Xinjie, Fan Gao, Xingyu Song, Yingjian Chen, Rui Yang, Yanran Fu, Yuyang Wang, Yusuke Iwasawa, Yutaka Matsuo, Irene Li

Peering Behind the Shield: Guardrail Identification in Large Language Models (2026.findings-acl)

Challenge:	Multi-hop question answering (QA) is a central challenge in natural language processing . early mistakes can cause errors and undermine the final result, authors say .
Approach:	They propose a reversible multi-agent reasoning framework that backtracks to earlier valid states when conflicts arise.
Outcome:	Empirical evaluation shows that the framework improves on forward-only benchmarks by 6% . the approach enables agents to backtrack to valid states when conflicts arise .

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Ziqing Yang, Yixin Wu, Rui Wen, Michael Backes, Yang Zhang

Beyond Inherent Cognition Biases in LLM-Based Event Forecasting: A Multi-Cognition Agentic Framework (2025.findings-emnlp)

Challenge:	Identifying guardrails in conversational AI agents is critical for identifying malicious content . identifying guardrail components in black-box AI agents poses security challenges .
Approach:	They propose a method that leverages guard-specific adversarial prompts to detect guardrails in black-box AI agents.
Outcome:	The proposed method achieves perfect classification accuracy in multiple scenarios.

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Zhen Wang, Xi Zhou, Yating Yang, Bo Ma, Lei Wang, Rui Dong, Azmat Anwar

Challenge:	Large Language Models exhibit human-like cognitive biases in event forecasting . a human-curated dataset reveals significant cognitive bias in LLMs .
Approach:	They propose a human-curated dataset to explore LLMs' cognitive biases . they leverage LLM participants to act as multi-cognition event participants .
Outcome:	The proposed framework alleviates cognitive biases in LLMs and offers diverse perspectives.

Mitigating Reversal Curse in Large Language Models via Semantic-aware Permutation Training (2024.findings-acl)

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Qingyan Guo, Rui Wang, Junliang Guo, Xu Tan, Jiang Bian, Yujiu Yang

From Query to Logic: Ontology-Driven Multi-Hop Reasoning in LLMs (2026.findings-acl)

Challenge:	Large language models (LLMs) have achieved impressive performance across diverse tasks, but suffer from the "reversal curse" this limitation poses a challenge to the advancement of artificial general intelligence (AGI)
Approach:	They propose to use training data to permute training sentences into entities and feed them into the model.
Outcome:	The proposed method improves the performance of large language models (LLMs) on reversed questions and improves existing models.

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Haonan Bian, Yutao Qi, Rui Yang, Yuanxi Che, Jiaqian Wang, Heming Xia, Ranran Zhen

MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) exhibit limitations in complex multi-hop question answering tasks that necessitate non-linear, structured reasoning.
Approach:	They propose an ontology-driven reasoning and chain framework that combines LLMs’ generative capabilities with the structural benefits of knowledge graphs.
Outcome:	Extensive experiments across a diverse set of models and standard MQA benchmarks demonstrate that the proposed framework achieves competitive performance while producing more interpretable reasoning chains.

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Weihao Xuan, Rui Yang, Heli Qi, Qingcheng Zeng, Yunze Xiao, Aosong Feng, Dairui Liu, Yun Xing, Junjue Wang, Fan Gao, Jinghui Lu, Yuang Jiang, Huitao Li, Xin Li, Kunyu Yu, Ruihai Dong, Shangding Gu, Yuekang Li, Xiaofei Xie, Felix Juefei-Xu, Foutse Khomh, Osamu Yoshie, Qingyu Chen, Douglas Teodoro, Nan Liu, Randy Goebel, Lei Ma, Edison Marrese-Taylor, Shijian Lu, Yusuke Iwasawa, Yutaka Matsuo, Irene Li

Enhancing Multilingual Reasoning via Steerable Model Merging (2026.findings-acl)

Challenge:	Existing large language model evaluation benchmarks focus on English, while current multilingual tasks lack parallel questions that specifically assess cross-lingual reasoning abilities.
Approach:	They propose a comprehensive benchmark covering 29 languages, built on an English benchmark.
Outcome:	The MMLU-ProX is a comprehensive benchmark covering 29 languages, built on an English benchmark.

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Zhuoran Li, Rui Xu, Jian Yang, Junnan Liu, Zhijun Chen, Qianren Mao, Hongcheng Guo, Jiaheng Liu, Likang Xiao, Ming LI, Xiaojie Wang

Reasoning

Challenge:	Model merging is an effective technique for composing the capabilities of a multilingual model and a reasoning model.
Approach:	They propose a model merging framework that modulates the contribution of each source model.
Outcome:	Experiments show that the proposed model merging framework outperforms strong baselines on multilingual reasoning benchmarks across 21 different languages.

Neighbors Are Not Strangers: Improving Non-Autoregressive Translation under Low-Frequency Lexical Constraints (2022.naacl-main)

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Chun Zeng, Jiangjie Chen, Tianyi Zhuang, Rui Xu, Hao Yang, Qin Ying, Shimin Tao, Yanghua Xiao

GraphDialog: Integrating Graph Knowledge into End-to-End Task-Oriented Dialogue Systems (2020.emnlp-main)

Challenge:	Existing approaches to lexically constrained neural machine translation suffer from high latency.
Approach:	They propose a plug-in algorithm for non-autoregressive translation for this problem . they propose ACT to familiarize the model with the source-side context of constraints .
Outcome:	The proposed model improves over the backbone constrained NAT model in constraint preservation and translation quality, especially for rare constraints.

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Shiquan Yang, Rui Zhang, Sarah Erfani

ChainEdit: Propagating Ripple Effects in LLM Knowledge Editing through Logical Rule-Guided Chains (2025.acl-long)

Challenge:	End-to-end task-oriented dialogue systems aim to generate system responses directly from plain text inputs.
Approach:	They propose a recurrent cell architecture which exploits the structural information in dialogue history . they propose recursive cell architecture to allow representation learning on graphs .
Outcome:	The proposed model improves on two different datasets on task-oriented dialogues.

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Zilu Dong, Xiangqing Shen, Zinong Yang, Rui Xia

Causality-aware Concept Extraction based on Knowledge-guided Prompting (2023.acl-long)

Challenge:	Existing knowledge editing methods for large language models struggle to maintain logical consistency when propagating ripple effects to associated facts.
Approach:	They propose a framework that synergizes knowledge graph-derived logical rules with LLM logical reasoning capabilities to enable systematic chain updates.
Outcome:	The proposed framework improves logical generalization and specificity while maintaining reliability and specificness.

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Siyu Yuan, Deqing Yang, Jinxi Liu, Shuyu Tian, Jiaqing Liang, Yanghua Xiao, Rui Xie

Translationese-index: Using Likelihood Ratios for Graded and Generalizable Measurement of Translationese (2025.emnlp-main)

Challenge:	Concepts in knowledge graphs (KGs) are far from complete in existing knowledge graph models.
Approach:	They propose to equip a PLM-based extractor with a knowledge-guided prompt to alleviate concept bias by removing spurious co-occurrence correlations from existing knowledge.
Outcome:	The proposed prompt can alleviate concept bias and improve the performance of existing models.

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Yikang Liu, Wanyang Zhang, Yiming Wang, Jialong Tang, Pei Zhang, Baosong Yang, Fei Huang, Rui Wang, Hai Hu

Evaluating Character Understanding of Large Language Models via Character Profiling from Fictional Works (2024.emnlp-main)

Challenge:	Translationese is a linguistic property that is often introduced in the translation process that is different from those of original texts.
Approach:	They propose to use synthesized translations and translations in the wild to evaluate T-index's generalizability in cross-domain settings and its validity against human judgments.
Outcome:	The proposed measure can generalize to unseen genres, authors, and language pairs.

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Xinfeng Yuan, Siyu Yuan, Yuhan Cui, Tianhe Lin, Xintao Wang, Rui Xu, Jiangjie Chen, Deqing Yang

Improving Multimodal Named Entity Recognition via Entity Span Detection with Unified Multimodal Transformer (2020.acl-main)

Challenge:	Recent advances in large language models (LLMs) have catalyzed numerous AI applications, among which role-playing agents (RPAs) are particularly popular.
Approach:	They propose to evaluate LLMs' character understanding capability via the character profiling task, i.e., summarizing character profiles from corresponding materials, a widely adopted yet understudied practice for RPA development.
Outcome:	The proposed model outperforms existing models and literature summarization methods and proves its ability to understand fictional characters in downstream tasks.

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Jianfei Yu, Jing Jiang, Li Yang, Rui Xia

Enhancing Machine Translation with Self-Supervised Preference Data (2025.acl-long)

Challenge:	Existing methods for named entity recognition ignore visual context bias . NER is a key component of many information extraction tasks .
Approach:	They propose to use a multimodal interaction module to generate word-aware visual representations and leverage purely text-based entity span detection as an auxiliary module to guide the final predictions.
Outcome:	The proposed approach achieves state-of-the-art on two benchmark datasets.

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Haoxiang Sun, Ruize Gao, Pei Zhang, Baosong Yang, Rui Wang

SCOPE: Preserving Modality-Specific Cues to Mitigate Modality Laziness in Multimodal Learning (2026.findings-acl)

Challenge:	Current approaches to constructing preference data rely on human annotations.
Approach:	They propose a framework which efficiently constructs translation preference data for iterative training.
Outcome:	The proposed framework improves translation preference data on large language models.

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Jingfan Yang, Rui Zhang, Liang Hong, Ke Yuan

Rethinking Word-Level Auto-Completion in Computer-Aided Translation (2023.emnlp-main)

Challenge:	Existing approaches to learning multimodal representations emphasize shared semantics and overlook modality-specific cues.
Approach:	They propose a framework for learning complete multimodal representations using shared and practical cues.
Outcome:	SCOPE outperforms SOTA benchmarks on four datasets and achieves 27.10% accuracy improvement.

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Xingyu Chen, Lemao Liu, Guoping Huang, Zhirui Zhang, Mingming Yang, Shuming Shi, Rui Wang

How do Visual Attributes Influence Web Agents? A Comprehensive Evaluation of User Interface Design Factors (2026.findings-acl)

Challenge:	Existing models for word-level auto-completion (WLAC) do not meet the criterion of good auto-completes.
Approach:	They propose a measurable criterion to address the question: what kind of words are good auto-completions? they propose an approach to enhance WLAC performance by promoting adherence to the cri-terion.
Outcome:	The proposed approach outperforms the top-performing system submitted to the WLAC shared tasks in WMT2022 while using significantly smaller model sizes.

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Kuai Yu, Naicheng Yu, Han Wang, Rui Yang, Huan Zhang

Revisiting Representation Degeneration Problem in Language Modeling (2020.findings-emnlp)

Challenge:	Existing studies on the effect of environmental variation on web agents have focused on robustness to adversarial attacks with less attention to agents’ preferences in benign scenarios.
Approach:	They propose a controlled evaluation pipeline to quantify how visual attributes influence web-agent decision-making by comparing variants and browsing interactions.
Outcome:	Extensive experiments on 8 variant families, 5 real-world websites and 4 representative web agents show that background color contrast, item size, position, and card clarity have a strong influence on agents’ actions, whereas font styling, text color, and item image clarity exhibit minor effects.

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Zhong Zhang, Chongming Gao, Cong Xu, Rui Miao, Qinli Yang, Junming Shao

Untie the Knots: An Efficient Data Augmentation Strategy for Long-Context Pre-Training in Language Models (2025.acl-long)

Challenge:	Language modeling is a fundamental task in natural language processing, applications include machine translation, image captioning and speech recognition.
Approach:	They propose a cosine regularization method to solve the representation degeneration problem by analyzing the limitations of the proposed method and then propose an alternative regularization technique to tackle the problem.
Outcome:	The proposed method is effective in language modeling and image captioning.

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Junfeng Tian, Da Zheng, Yang Chen, Rui Wang, Colin Zhang, Debing Zhang

Efficient Data Labeling by Hierarchical Crowdsourcing with Large Language Models (2025.coling-main)

Challenge:	Large language models (LLMs) have prioritized expanding the context window from which they can incorporate more information.
Approach:	They propose a data augmentation strategy to enable large language models to gain long-context capabilities without the need to modify existing data mixture.
Outcome:	The proposed model outperforms existing models on 20 billion tokens and achieves 75% and 84.5% accuracy on RULER at 128K context length.

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Haodi Zhang, Junyu Yang, Jinyin Nie, Peirou Liang, Kaishun Wu, Defu Lian, Rui Mao, Yuanfeng Song

Benchmarking the Fine-Grained Discriminability in Image-Text Retrieval via Controlled Contrastive Differences (2026.findings-acl)

Challenge:	Large language models (LLMs) have been gaining attention for their impressive performance in in-context dialogues.
Approach:	They propose a hierarchical framework that leverages multiple LLMs for efficient data labeling under budget constraints.
Outcome:	The proposed framework outperforms human labelers and GPT-4 in terms of accuracy and efficiency.

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Zhen Wang, Xi Zhou, Yating Yang, Bo Ma, Lei Wang, Rui Dong, Azmat Anwar, Siru Miao

ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development (2026.findings-acl)

Challenge:	Existing cross-modal image-text retrieval models often retrieve samples with inconsistent details.
Approach:	They propose two fine-grained image-text retrieval benchmarks that incorporate extensive contrastive samples with one controlled contrastive difference from its anchor.
Outcome:	Extensive experiments show that contrastive samples can significantly degrade retrieval performance.

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Jie Yang, Honglin Guo, Li Ji, Jiazheng Zhou, Rui Zheng, Zhikai Lei, Shuo Zhang, Zhiheng Xi, Shichun Liu, Yuxin Wang, Bo Wang, Yining Zheng, Tao Gui, Xipeng Qiu

RAM2C: A Liberal Arts Educational Chatbot based on Retrieval-augmented Multi-role Multi-expert Collaboration (2025.coling-main)

Challenge:	Large Language Models (LLMs) have redefined the role of AI in software engineering . current benchmarks focus on localized code generation, but neglect dynamic, full-process requirements of real-world engineering.
Approach:	They propose a benchmark to evaluate agentic backend coding within a realistic, executable workflow.
Outcome:	The ABC-Bench benchmark evaluates agentic backend coding within a realistic, executable workflow.

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Haoyu Huang, Tong Niu, Rui Yang, Luping Shi

SpiderFlow: Efficient Topology-Aware Scheduling for LLM Training Across Decentralized GPU Clusters (2026.acl-long)

Challenge:	Empirical evaluations indicate that RAM2C-empowered LLMs excel in Chinese reading teaching, offering more personalized, and ethically safe teaching response.
Approach:	They propose a framework to retrieve large language models into educational dialogues and organize them into multi-experts groups with distinct roles to generate the data.
Outcome:	Empirical evaluations show that RAM2C-empowered LLMs excel in Chinese reading teaching, offering more personalized, and ethically safe teaching response.

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Zihan Chang, Shuibing He, Bo Zhou, Sheng Xiao, Siling Yang, Rui Wang, Zhe Pan

PreQuant: A Task-agnostic Quantization Approach for Pre-trained Language Models (2023.findings-acl)

Challenge:	Existing approaches to training large language models lack topologyaware task scheduling mechanisms and model parallelization strategies.
Approach:	They propose a topology-aware scheduling system specifically designed for decentralized GPU clusters . they propose heuristic methods at the inter-cluster level with ILP-based optimization within clusters.
Outcome:	The proposed system reduces job completion time by 1.2-1.3 and improves throughput by 1.12-1.25 . it also reduces scheduling overhead by 20-90 on average compared to state-of-the-art scheduling systems.

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Zhuocheng Gong, Jiahao Liu, Qifan Wang, Yang Yang, Jingang Wang, Wei Wu, Yunsen Xian, Dongyan Zhao, Rui Yan

Evaluating Large Language Models on Wikipedia-Style Survey Generation (2024.findings-acl)

Challenge:	Quantization is a viable solution for pre-trained language models, but most existing methods are task-specific and require customized training and quantization with a large number of trainable parameters.
Approach:	They propose a "quantize before fine-tuning" framework that allows for quantization with a large number of trainable parameters on each individual task.
Outcome:	The proposed framework is compatible with quantization-aware training and post-training quantization and corrects quantization errors.

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Fan Gao, Hang Jiang, Rui Yang, Qingcheng Zeng, Jinghui Lu, Moritz Blum, Tianwei She, Yuang Jiang, Irene Li

Alternated Training with Synthetic and Authentic Data for Neural Machine Translation (2021.findings-acl)

Challenge:	Recent studies have shown that large language models can perform well in general tasks, but their effectiveness and limitations in domainspecific tasks remain unclear.
Approach:	They examine the proficiency of Large Language Models (LLMs) in generating succinct survey articles specific to the niche field of NLP in computer science.
Outcome:	The LLMs perform better in generating succinct survey articles specific to the niche field of NLP in computer science, compared to human-authored surveys, but they exhibit bias in evaluation.

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Rui Jiao, Zonghan Yang, Maosong Sun, Yang Liu

Multi-Task Neural Model for Agglutinative Language Translation (2020.acl-srw)

Challenge:	Existing approaches to synthesizing data in NMT focus on leveraging monolingual data in training.
Approach:	They propose alternated training with synthetic and authentic data to improve NMT models' performance.
Outcome:	The proposed approach improves Chinese-English and German-English translation tasks over strong baselines.

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Yirong Pan, Xiao Li, Yating Yang, Rui Dong

Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate (2024.emnlp-main)

Challenge:	Neural machine translation (NMT) has been gaining popularity in high-resource translation tasks, but struggles in low-ressource and morphologically-rich scenarios.
Approach:	They propose a multi-task neural model that jointly learns to perform bi-directional translation and agglutinative language stemming.
Outcome:	The proposed model can significantly improve translation performance on agglutinative languages by using a small amount of monolingual data.

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Tian Liang, Zhiwei He, Wenxiang Jiao, Xing Wang, Yan Wang, Rui Wang, Yujiu Yang, Shuming Shi, Zhaopeng Tu

Learning Relation Alignment for Calibrated Cross-modal Retrieval (2021.acl-long)

Challenge:	Modern large language models (LLMs) have shown remarkable performance on general language tasks but struggle on complex reasoning tasks.
Approach:	They propose a multi-agent debate framework that encourages divergent thinking in LLMs . they propose to break debate and use a judge to obtain a final solution .
Outcome:	The proposed framework encourages divergent thinking in large language models . it is able to generate novel thoughts even if initial stance is incorrect .

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Shuhuai Ren, Junyang Lin, Guangxiang Zhao, Rui Men, An Yang, Jingren Zhou, Xu Sun, Hongxia Yang

Finding the Pillars of Strength for Multi-Head Attention (2023.acl-long)

Challenge:	despite advances in multimodal pre-training, cross-modal retrieval remains challenging . lack of relation consistency impairs contextualized representation of image-text pairs .
Approach:	They propose a new metric to quantify the relation consistency by measuring the semantic distance between linguistic and visual relations.
Outcome:	The proposed method boosts the performance of prevailing models on Flickr30k and MS COCO datasets by a considerable margin.

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Jinjie Ni, Rui Mao, Zonglin Yang, Han Lei, Erik Cambria

AlignSum: Data Pyramid Hierarchical Fine-tuning for Aligning with Human Summarization Preference (2024.findings-emnlp)

Challenge:	Recent studies have revealed some issues of Multi-Head Attention (MHA) e.g., redundancy and over-parameterization.
Approach:	They propose to train attention heads with a self-supervised group constraint to focus on an essential but distinctive feature subset.
Outcome:	The proposed method achieves significant performance gains on three well-established tasks while significantly compressing parameters.

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Yang Han, Yiming Wang, Rui Wang, Lu Chen, Kai Yu

Intent Discovery with Frame-guided Semantic Regularization and Augmentation (2023.findings-acl)

Challenge:	Text summarization tasks employ Pre-trained Language Models (PLMs) to fit diverse datasets.
Approach:	They propose a human summarization preference alignment framework to align PLMs with human preferences.
Outcome:	The proposed framework narrows the gap between automatic and human evaluations by integrating three components.

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Yajing Sun, Rui Zhang, Jingyuan Yang, Wei Peng

BaseCal: Unsupervised Confidence Calibration via Base Model Signals (2026.acl-long)

Challenge:	Existing intent discovery methods focus on transferring prior knowledge of known intents to unknown ones.
Approach:	They propose to use frame knowledge as conceptual semantic guidance to bridge the gap between known intents representation learning and unknown intents clustering.
Outcome:	The proposed method outperforms solid baselines on two benchmark datasets.

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Hexiang Tan, Wanli Yang, Junwei Zhang, Xin Chen, Rui Tang, Du Su, Jingang Wang, Yuanzhuo Wang, Fei Sun, Xueqi Cheng

Relational Graph Attention Network for Aspect-based Sentiment Analysis (2020.acl-main)

Challenge:	Post-trained LLMs typically compromise reliability with severe overconfidence, resulting in inaccurate responses.
Approach:	They propose a solution that feeds PoLLMs into the base LLM to get confidence.
Outcome:	The proposed solution reduces expected calibration error (ECE) by 42.90% compared to the best unsupervised baselines.

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Kai Wang, Weizhou Shen, Yunyi Yang, Xiaojun Quan, Rui Wang

Sentiment Analysis

Challenge:	Aspect-based sentiment analysis aims to determine the sentiment polarity towards a specific aspect in online reviews.
Approach:	They propose a relational graph attention network to encode a tree structure for sentiment prediction.
Outcome:	The proposed approach improves the performance of the graph attention network (GAT) on the SemEval 2014 and Twitter datasets.

ASCM: An Answer Space Clustered Prompting Method without Answer Engineering (2022.findings-acl)

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Zhen Wang, Yating Yang, Zhou Xi, Bo Ma, Lei Wang, Rui Dong, Azmat Anwar

Low-Resource NLP

Challenge:	Pre-trained language models have shown a great impact on NLP tasks.
Approach:	They propose an answer space clustered prompting model and a synonym initialization method that automatically categorizes all answer tokens in a semantic-clustered embedding space.
Outcome:	Experiments show that the proposed method outperforms existing state-of-the-art methods in few-shot settings.

HumanLLM: Benchmarking and Improving LLM Anthropomorphism via Human Cognitive Patterns (2026.acl-long)

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Xintao Wang, Jian Yang, Weiyuan Li, Rui Xie, Jen-tse Huang, Jun Gao, Shuai Huang, Yueping Kang, Yuanli Guo, Hongwei Feng, Yanghua Xiao

Linguistics & Cognitive NLP

Challenge:	Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning and generation, serving as the foundation for advanced persona simulation and Role-Playing Language Agents (RPLAs).
Approach:	They propose a framework that treats psychological patterns as interacting causal forces and synthesizes 113 scenarios where 2-5 patterns reinforce, conflict, or modulate each other.
Outcome:	The proposed framework outperforms Qwen3-32B on multi-pattern dynamics despite 4 fewer parameters.

OMGM: Orchestrate Multiple Granularities and Modalities for Efficient Multimodal Retrieval (2025.acl-long)

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Wei Yang, Jingjing Fu, Rui Wang, Jinyu Wang, Lei Song, Jiang Bian