Papers by Yilun Zhang

A Survey of Reasoning-Intensive Retrieval: Progress and Challenges (2026.acl-long)

Challenge:	Existing benchmarks for logical reasoning in large language models lack language naturalness or limited complexity.
Approach:	They propose to use first-order logic annotations to evaluate logical reasoning capabilities of large language models.
Outcome:	The proposed dataset evaluates the FOL reasoning ability of supervised fine-tuning on medium-sized language models.

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Yiyang Wei, Tingyu Song, Siyue Zhang, Yilun Zhao

Can Large Language Models Understand Context? (2024.findings-eacl)

Challenge:	Reasoning-Intensive Retrieval (RIR) targets retrieval settings where relevance is mediated by latent inferential links between a query and supporting evidence, rather than semantic similarity.
Approach:	They propose a taxonomy that categorizes methods based on where and how reasoning is integrated into the retrieval pipeline.
Outcome:	The proposed method framework provides a detailed analysis of the current landscape and its trade-offs and practical applications.

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Yilun Zhu, Joel Moniz, Shruti Bhargava, Jiarui Lu, Dhivya Piraviperumal, Site Li, Yuan Zhang, Hong Yu, Bo-Hsiang Tseng

Rethinking Composed Image Retrieval Evaluation: A Fine-Grained Benchmark from Image Editing (2026.acl-long)

Challenge:	Existing evaluation methodologies for Large Language Models (LLMs) have been inadequate to evaluate their ability to understand contextual features.
Approach:	They propose a benchmark to assess large language models' ability to understand context by adapting existing datasets to suit their evaluation.
Outcome:	The proposed model performs better under the in-context learning pretraining scenario than state-of-the-art models.

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Tingyu Song, Yanzhao Zhang, Mingxin Li, Zhuoning Guo, Dingkun Long, Pengjun Xie, Siyue Zhang, Yilun Zhao, Shu Wu

Latent Inter-User Difference Modeling for LLM Personalization (2025.emnlp-main)

Challenge:	Composed Image Retrieval (CIR) is a complex task in multimodal understanding . current CIR benchmarks lack a robust evaluation pipeline and limited query categories .
Approach:	They construct a fine-grained CIR benchmark that allows for precise control over modification types and content.
Outcome:	The proposed benchmark covers 5,000 high-quality queries structured across five main categories and fifteen subcategories.

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Yilun Qiu, Tianhao Shi, Xiaoyan Zhao, Fengbin Zhu, Yang Zhang, Fuli Feng

Semantic Text Processing

Challenge:	Large language models (LLMs) are increasingly integrated into users’ daily lives, leading to a growing demand for personalized outputs.
Approach:	They propose a framework that models inter-user differences in the latent space instead of relying on language-based prompts.
Outcome:	The proposed framework outperforms baseline methods on personalized review generation.

MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning (2024.findings-acl)

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Xiangru Tang, Anni Zou, Zhuosheng Zhang, Ziming Li, Yilun Zhao, Xingyao Zhang, Arman Cohan, Mark Gerstein

FinDVer: Explainable Claim Verification over Long and Hybrid-content Financial Documents (2024.emnlp-main)

Challenge:	Large language models face unique challenges such as domain-specific terminologies and reasoning over specialized knowledge.
Approach:	They propose a multi-disciplinary collaboration framework that leverages LLM-based agents in a role-playing setting.
Outcome:	The proposed framework excels at mining and harnessing medical expertise within LLMs, as well as extending its reasoning abilities.

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Yilun Zhao, Yitao Long, Tintin Jiang, Chengye Wang, Weiyuan Chen, Hongjun Liu, Xiangru Tang, Yiming Zhang, Chen Zhao, Arman Cohan

Responsible & Trustworthy NLP

Challenge:	FinDVer is a benchmark to evaluate the explainable claim verification capabilities of LLMs . financial documents are typically long, intricate and dense, and they include both quantita and numerical reasoning.
Approach:	They propose a benchmark to evaluate the explainable claim verification capabilities of LLMs . they assess 25 LLM systems under long-context and RAG settings .
Outcome:	The proposed benchmark can be used to evaluate the explainable claim verification capabilities of LLMs in financial documents.

OpenT2T: An Open-Source Toolkit for Table-to-Text Generation (2024.emnlp-demo)

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Haowei Zhang, Shengyun Si, Yilun Zhao, Lujing Xie, Zhijian Xu, Lyuhao Chen, Linyong Nan, Pengcheng Wang, Xiangru Tang, Arman Cohan

Text Generation

Challenge:	Existing methods for table-to-text generation are limited and benchmarked on a limited number of datasets.
Approach:	They propose to use open-source tools to reproduce existing large language models for performance comparison and expedite the development of new models.
Outcome:	The proposed toolkit compares existing large language models on 9 table-to-text generation datasets and maintains a leaderboard to provide insights for future work.

Large Language Models Can Solve Real-World Planning Rigorously with Formal Verification Tools (2025.naacl-long)

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Yilun Hao, Yongchao Chen, Yang Zhang, Chuchu Fan

SciSketch: An Open-source Framework for Automated Schematic Diagram Generation in Scientific Papers (2025.emnlp-demos)

Challenge:	Large Language Models (LLMs) struggle to generate correct plans for multi-constraint planning problems . a recent study showed that large language models have significant potential in solving planning problems.
Approach:	They propose an LLM-based planning framework that formalizes and solves multi-constraint planning problems as constrained satisfiability problems.
Outcome:	The proposed framework achieves a success rate of 93.9% and is effective with diverse paraphrased prompts.

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Zihang Wang, Yilun Zhao, Kaiyan Zhang, Chen Zhao, Manasi Patwardhan, Arman Cohan

MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data (2022.acl-long)

Challenge:	SCISKETCH is an open-source framework that supports two automated workflows for schematic diagram generation using foundation models.
Approach:	They propose an open-source framework that supports two automated workflows for schematic diagram generation using foundation models.
Outcome:	The open-source framework outperforms several state-of-the-art foundation models in generating schematic diagrams for scientific papers.

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Yilun Zhao, Yunxiang Li, Chenying Li, Rui Zhang

ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples (2022.emnlp-main)

Challenge:	Existing benchmarks for numerical reasoning over hybrid data only include a single flat table in each document .
Approach:	They propose a new benchmark with QA pairs over multi hierarchical tabular and textual data.
Outcome:	The proposed model is more complex and challenging than existing benchmarks and is available on github . it uses facts retrieving to extract relevant facts from both tables and text and symbolic reasoning over retrieved facts.

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Yilun Zhao, Linyong Nan, Zhenting Qi, Rui Zhang, Dragomir Radev

KnowledgeFMath: A Knowledge-Intensive Math Reasoning Dataset in Finance Domains (2024.acl-long)

Challenge:	Existing models with table-specific architectures and pre-training methods perform well on understanding table structures but lack table reasoning skills.
Approach:	They propose to pre-train tables with table reasoning skills without complex architectures . they define 7 table reasoning skill, and then pre-teach them to generate tables .
Outcome:	The proposed model improves on four tasks and is available on github.

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Yilun Zhao, Hongjun Liu, Yitao Long, Rui Zhang, Chen Zhao, Arman Cohan

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

Challenge:	Existing benchmarks for large language models (LLMs) are only 56.6% accurate, leaving room for improvement.
Approach:	They propose a benchmark to evaluate LLMs' capabilities in solving knowledge-intensive math reasoning problems using a finance-domain knowledge bank and expert-annotated solution references.
Outcome:	The proposed system achieves only 56.6% accuracy, leaving room for improvement.

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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

Z1: Efficient Test-time Scaling with Code (2025.emnlp-industry)

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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Zhaojian Yu, Yinghao Wu, Yilun Zhao, Arman Cohan, Xiao-Ping Zhang

Responsible & Trustworthy NLP

Challenge:	Large Language Models (LLMs) can achieve enhanced complex problem-solving through test-time computing scaling, but this often entails longer contexts and numerous reasoning token costs.
Approach:	They propose an efficient test-time scaling method that trains LLMs on code-related reasoning trajectories and a novel Shifted Thinking Window to mitigate overthinking overhead.
Outcome:	The proposed method reduces overthinking overhead while maintaining performance.

TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Industry Systems (2024.emnlp-industry)

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Yilun Kong, Jingqing Ruan, YiHong Chen, Bin Zhang, Tianpeng Bao, Shi Shiwei, Du Qing, Xiaoru Hu, Hangyu Mao, Ziyue Li, Xingyu Zeng, Rui Zhao, Xueqian Wang

Semantic Text Processing

Challenge:	Large language models have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools.
Approach:	They propose a framework to enhance the task planning and tool usage abilities of LLMs in industrial systems.
Outcome:	The proposed framework enhances the task planning and tool usage abilities of LLM-based agents in industrial systems.

Measuring What Makes You Unique: Difference-Aware User Modeling for Enhancing LLM Personalization (2025.findings-acl)

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Yilun Qiu, Xiaoyan Zhao, Yang Zhang, Yimeng Bai, Wenjie Wang, Hong Cheng, Fuli Feng, Tat-Seng Chua

Challenge:	Extensive experiments on real-world datasets demonstrate that DPL significantly enhances LLM personalization.
Approach:	They propose a novel approach that emphasizes extracting inter-user differences to enhance LLM personalization.
Outcome:	The proposed approach extracts inter-user differences to enhance LLM personalization.

Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Future (2026.acl-long)

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Sihong Wu, Owen Jiang, Yilun Zhao, Tiansheng Hu, Yiling Ma, Kaiyan Zhang, Manasi Patwardhan, Arman Cohan

Diffusion vs. Autoregressive Language Models: A Text Embedding Perspective (2025.emnlp-main)

Challenge:	Recent advances in large language models (LLMs) motivated methods that assist or automate different stages of peer review pipeline.
Approach:	They synthesize techniques to enhance peer review generation and after-review tasks aligned to reviews.
Outcome:	The proposed methods improve the peer review process by fine-tuning strategies, agent-based systems, and emerging paradigms.

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Siyue Zhang, Yilun Zhao, Liyuan Geng, Arman Cohan, Anh Tuan Luu, Chen Zhao

Semantic Text Processing

Challenge:	Large language model (LLM)-based embedding models surpass BERT and T5 on general-purpose text embeddable tasks.
Approach:	They propose to adopt diffusion language models for text embeddings to overcome limitations in unidirectional attention used during autoregressive pre-training.
Outcome:	The proposed model outperforms the existing LLM-based embedding model on reasoning tasks by 20% and 2% on traditional embeddable benchmarks.

Part Represents Whole: Improving the Evaluation of Machine Translation System Using Entropy Enhanced Metrics (2022.findings-aacl)

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Yilun Liu, Shimin Tao, Chang Su, Min Zhang, Yanqing Zhao, Hao Yang

Machine Translation

Challenge:	Existing machine translation metrics have poor correlations with human assessments . entropy-based evaluations are often limited to a limited number of samples .
Approach:	They propose a fast and unsupervised approach to enhance machine translation metrics using entropy by introducing sentence-level difficulty.
Outcome:	The proposed method outperforms existing metrics on five sub-tracks in the WMT19 Metrics shared tasks.

RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial Perturbations (2023.acl-long)

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Yilun Zhao, Chen Zhao, Linyong Nan, Zhenting Qi, Wenlin Zhang, Xiangru Tang, Boyu Mi, Dragomir Radev

MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)

Challenge:	Existing Table QA models are vulnerable to task-specific perturbations, such as replacing key question entities or shuffling table columns.
Approach:	They propose to use large language models to generate adversarial examples to enhance training, which significantly improves the robustness of Table QA models.
Outcome:	The proposed model significantly improves on existing Table QA models against human-annotated adversarial perturbations.

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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie

PRompt Optimization in Multi-Step Tasks (PROMST): Integrating Human Feedback and Heuristic-based Sampling (2024.emnlp-main)

Challenge:	Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models.
Approach:	They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts.
Outcome:	The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings .

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Yongchao Chen, Jacob Arkin, Yilun Hao, Yang Zhang, Nicholas Roy, Chuchu Fan

Investigating Table-to-Text Generation Capabilities of Large Language Models in Real-World Information Seeking Scenarios (2023.emnlp-industry)

Challenge:	Prompt optimization aims to find the best prompt to a large language model (LLM) for a given task.
Approach:	They propose a method to optimize prompts for LLM-driven multi-step tasks using a human-designed feedback rule.
Outcome:	The proposed method outperforms human-engineered prompts and several other prompt optimization methods on 11 representative multi-step tasks.

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Yilun Zhao, Haowei Zhang, Shengyun Si, Linyong Nan, Xiangru Tang, Arman Cohan

Text Generation

Challenge:	Existing table-to-text generation techniques that transform complex tabular data into comprehensible narratives are lacking in real-world applications.
Approach:	They investigate the table-to-text capabilities of different LLMs using four datasets within two real-world information seeking scenarios.
Outcome:	The proposed models can generate table-to-text data in two real-world information seeking scenarios and perform better than existing models.

HumanEval Pro and MBPP Pro: Evaluating Large Language Models on Self-invoking Code Generation Task (2025.findings-acl)

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Zhaojian Yu, Yilun Zhao, Arman Cohan, Xiao-Ping Zhang

Programming Languages in NLP

Challenge:	Existing benchmarks for code generation tasks are inadequate, but performance declines on self-invoking tasks.
Approach:	They propose a general recipe for generating more challenging versions of existing benchmarks . they propose to use instruction-tuned models to evaluate LLMs on self-invoking code generation tasks .
Outcome:	The proposed model improves on humanEval and MBPP but on self-invoking code generation tasks.

On Evaluating the Integration of Reasoning and Action in LLM Agents with Database Question Answering (2024.findings-naacl)

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Linyong Nan, Ellen Zhang, Weijin Zou, Yilun Zhao, Wenfei Zhou, Arman Cohan

Natural Language Interfaces

Challenge:	a new study evaluates how Large Language Models interact with a SQL interpreter . the model is limited in context and is stochastic, making it less suited for tasks requiring high precision and extensive computations.
Approach:	They propose and evaluate two interaction strategies to evaluate how LLMs interact with a SQL interpreter.
Outcome:	The proposed framework improves the accuracy and reliability of the evaluations.

Rethinking Reasoning-Intensive Retrieval: Evaluating and Advancing Retrievers in Agentic Search Systems (2026.acl-long)

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Yilun Zhao, Jinbiao Wei, Tingyu Song, Siyue Zhang, Chen Zhao, Arman Cohan

Information Retrieval

Challenge:	Existing evaluation benchmarks for retrievers are narrow and evaluate them in isolation . existing evaluation benchmarking frameworks focus on evaluating retrievers in isolation, obscuring their value in real-world applications.
Approach:	They propose an evaluation framework that evaluates retrievers in agentic search systems . they provide expert-annotated reasoning aspects, positive documents, a reference response and evaluation rubrics .
Outcome:	The proposed framework assesses retrievers in agentic search systems.

Web Fraud Attacks Against LLM-Driven Multi-Agent Systems (2026.findings-acl)

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Dezhang Kong, Hujin Peng, Yilun Zhang, Lele Zhao, Zhenhua Xu, Shi Lin, Changting Lin, Meng Han

Robustness in NLP

Challenge:	Large Language Model (LLM)-driven multi-agent systems (MAS) are rapidly gaining popularity, and its inherent security risks are rapidly becoming a concern.
Approach:	They propose a novel attack manipulating unique structures of web links to deceive MAS by using homoglyph deception, sub-directory nesting, and parameter obfuscation.
Outcome:	The proposed attacks exploit unique structures of web links to deceive MAS . they exhibit significant destructive potential across different MAS architectures .

SENTRA: Selected-Next-Token Transformer for LLM Text Detection (2025.findings-emnlp)

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Mitchell Plyler, Yilun Zhang, Alexander Tuzhilin, Saoud Khalifah, Sen Tian

LimRank: Less is More for Reasoning-Intensive Information Reranking (2025.emnlp-main)

Challenge:	SENTRA is a general-purpose, supervised LLM text detector . it detects LLM-generated text that is not explicitly declared as such .
Approach:	They propose a general-purpose LLM text detector that detects unlabeled text . they use a transformer-based encoder that leverages selected-next-token sequences .
Outcome:	The proposed classifier outperforms baselines on 24 domains of text.

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Tingyu Song, Yilun Zhao, Siyue Zhang, Chen Zhao, Arman Cohan

Judging with Many Minds: Do More Perspectives Mean Less Prejudice? On Bias Amplification and Resistance in Multi-Agent Based LLM-as-Judge (2025.findings-emnlp)

Challenge:	Existing approaches to rerank information require large-scale fine-tuning, which is computationally expensive.
Approach:	They propose an open-source pipeline for generating diverse, challenging, and realistic reranking examples.
Outcome:	The proposed model performs competitively on two benchmarks, while being trained on less than 5% of the data typically used in prior work.

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Chiyu Ma, Enpei Zhang, Yilun Zhao, Wenjun Liu, Yaning Jia, Peijun Qing, Lin Shi, Arman Cohan, Yujun Yan, Soroush Vosoughi

Responsible & Trustworthy NLP

Challenge:	LLM-as-Judge frameworks provide scalable alternative to human evaluation . but the question of how intrinsic biases manifest in these settings remains unexplored .
Approach:	They conduct systematic analysis of four bias types in multi-agent LLM-as-Judge frameworks . they find debate framework amplifies biases sharply after initial debate .
Outcome:	The proposed frameworks amplify biases after debate and show they are stronger in meta-judge scenarios.

Taming Text-to-Image Synthesis for Novices: User-centric Prompt Generation via Multi-turn Guidance (2025.emnlp-main)

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Yilun Liu, Minggui He, Feiyu Yao, Yuhe Ji, Shimin Tao, Jingzhou Du, Justin Li, Jian Gao, Zhang Li, Hao Yang, Boxing Chen, Osamu Yoshie

Low-Resource NLP

Challenge:	Existing solutions for text-to-image synthesis are sensitive on textual prompts, posing a challenge for novice users.
Approach:	They propose a dialogue-based TIS prompt generation model that emphasizes user experience for novice users.
Outcome:	The proposed model emphasizes user experience for novice users . it improves user-centricity score while maintaining a competitive quality of synthesized images.

A Survey of Multimodal Mathematical Reasoning: From Perception, Alignment to Reasoning (2026.acl-long)

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Tianyu Yang, Sihong Wu, Yilun Zhao, Zhenwen Liang, Lisen Dai, Chen Zhao, Minhao Cheng, Arman Cohan, Xiangliang Zhang

Enhancing Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies (2023.findings-emnlp)

Challenge:	Multimodal mathematical Reasoning (MMR) has attracted increasing attention for its ability to solve mathematical problems involving both textual and visual modalities.
Approach:	They review the theoretical frameworks of multimodal reasoning and examine the challenges they face in visual math tasks.
Outcome:	The proposed models can solve problems involving both textual and visual modalities.

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Linyong Nan, Yilun Zhao, Weijin Zou, Narutatsu Ri, Jaesung Tae, Ellen Zhang, Arman Cohan, Dragomir Radev

Programming Languages in NLP

Challenge:	In-context learning (ICL) is a new approach to natural language processing tasks that rely on large language models to make predictions based on context . recent studies have shown that neural symbolic design is the preferred choice for question answering systems because of its limited working memory and unreliable long-term memory.
Approach:	They propose to extend in-context learning to question answering tasks that utilize structured knowledge sources and to explore various prompt design strategies for employing LLMs.
Outcome:	The proposed approach outperforms the state-of-the-art system by 2.5 points and the best fine-tuned system by 5.1 points on the Spider dataset.

The GaoYao Benchmark: A Comprehensive Framework for Evaluating Multilingual and Multicultural Abilities of Large Language Models (2026.acl-long)

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Yilun Liu, Chunguang Zhao, Mengyao Piao, Lingqi Miao, Shimin Tao, Minggui HE, Chenxin Liu, Zhang Li, null Mahongxia, Jiaxin Guo, Chen Liu, Liqun Deng, Jiansheng Wei, Xiaojun Meng, Fanyi Du, Daimeng Wei, Yanghua Xiao

ARCQuant: Boosting NVFP4 Quantization with Augmented Residual Channels for LLMs (2026.acl-long)

Challenge:	Existing multilingual evaluation benchmarks neglect cultural nuances and lack language coverage in subjective tasks.
Approach:	They propose a framework that categorizes evaluation tasks into three cultural layers and nine cognitive sub-layers.
Outcome:	The proposed framework surpasses prior coverage by up to 111% on 20+ LLMs.

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Haoqian Meng, Yilun Luo, Yafei Zhao, Wenyuan Liu, Peng Zhang, Xindian Ma

SportReason: Evaluating Retrieval-Augmented Reasoning across Tables and Text for Sports Question Answering (2025.emnlp-main)

Challenge:	NVFP4 supports fine-grained block isolation, 4-bit quantization errors and mixed-precision approaches . ARCQuant boosts NVFO4 performance via Augmented Residual Channels .
Approach:	They propose a framework that boosts NVFP4 performance via Augmented Residual Channels.
Outcome:	ARCQuant boosts NVFP4 performance via Augmented Residual Channels . the proposed framework achieves state-of-the-art accuracy comparable to full-precision baselines compared to FP16 .

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Kaiyue Feng, Siyue Zhang, Bingsen Chen, Yilun Zhao, Chen Zhao