Papers by Yixuan Zhang
HiCoLoRA: Addressing Context-Prompt Misalignment via Hierarchical Collaborative LoRA for Zero-Shot DST (2026.findings-acl)
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| Challenge: | Existing approaches to zero-shot Dialog State Tracking (zs-DST) are inadequate to generalize to new domains without extensive training. |
| Approach: | They propose a framework that enhances zero-shot slot inference through robust prompt alignment. |
| Outcome: | Experiments on multi-domain datasets show that HiCoLoRA outperforms baselines, achieving SOTA in zs-DST. |
ScalingFilter: Assessing Data Quality through Inverse Utilization of Scaling Laws (2024.emnlp-main)
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| Challenge: | Existing quality filtering methods rely on a high-quality dataset as reference . Existing methods introduce potential biases and compromise diversity . |
| Approach: | They propose a method that evaluates text quality based on the perplexity difference between two language models trained on the same data. |
| Outcome: | The proposed approach improves performance of pre-trained models without increasing training costs. |
Make Some Noise: Unlocking Language Model Parallel Inference Capability through Noisy Training (2024.emnlp-main)
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Yixuan Wang, Xianzhen Luo, Fuxuan Wei, Yijun Liu, Qingfu Zhu, Xuanyu Zhang, Qing Yang, Dongliang Xu, Wanxiang Che
| Challenge: | Existing speculative decoding methods require additional model structure and training processes to assist the model for draft token generation. |
| Approach: | They propose a make some noise training framework that introduces some noise at the input for the model to learn the denoising task. |
| Outcome: | The proposed model improves inference speed by 2.3-2.7x times without compromising model performance. |
MMA: Cross-Domain Knowledge Integration via Mixture of Multi-Domain Agents (2025.findings-emnlp)
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| Challenge: | achieving synergistic improvements between generalization and domain specialization remains a challenge in pre-training and post-training. |
| Approach: | They propose a test-time cross-domain knowledge integration method that integrates general-purpose and domain-specific models to enhance their performance on complex, domainspecific tasks. |
| Outcome: | The proposed method combines the outputs of general-purpose and domain-specific models to improve their performance on complex, domainspecific tasks. |
RL-Guider: Leveraging Historical Decisions and Feedback for Drug Editing with Large Language Models (2025.findings-acl)
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| Challenge: | RLs can be used to refine drugs by iterative conversations with domain experts . existing methods do not leverage past knowledge, but human experts develop intuition over time through historical experience . |
| Approach: | They propose a reinforcement-learning agent to provide suggestions to large language models . RL-Guider leverages the “world-level” knowledge of LLMs and historical feedback . |
| Outcome: | a new reinforcement-learning agent improves the performance of large language models . the proposed agent leverages the “world-level” knowledge of LLMs and historical feedback . |
Under the Shadow of Babel: How Language Shapes Reasoning in LLMs (2025.findings-emnlp)
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| Challenge: | linguistic relativity suggests that the structure of language shapes cognitive patterns . large language models internalize the habitual logical structures embedded in different languages, authors say . |
| Approach: | a study introduces a bilingual dataset for causal reasoning in Chinese and English. |
| Outcome: | a new study shows that large language models internalize reasoning biases shaped by language . the model internalizes language-specific preferences and rigidly applies them to atypical inputs, the study shows . |
AI Agents for the Science of Science: A Survey of Tasks, Architectures, Evaluations, and Challenges (2026.findings-acl)
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| Challenge: | The Science of Science (SciSc) examines how scientific knowledge is produced, evaluated, and transformed by utilizing large-scale scholarly and bibliometric data. |
| Approach: | They propose a task-centered taxonomy for AI agents that model citations, collaborations, and community dynamics. |
| Outcome: | The proposed taxonomy distinguishes agents as simulations from tools for empirical analysis and scientific workflows. |
Fine-Grained Data Ordering Improves Fine-Tuning for Large Language Models (2026.findings-acl)
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Xiaomeng Hu, Yixuan Tang, Haoze Li, Hao Chen, Qi Zhang, Zhanming Shen, Yiming Zhang, Haobo Wang, Junbo Zhao
| Challenge: | Prior work focused on data preprocessing, focusing on filtering and cleaning data . a study aimed to improve fine-grained scheduling of data order in epochs . |
| Approach: | They propose a fine-grained scheduling method of data order in epochs to fill this gap . they define data difficulty based on relevance between data and model . |
| Outcome: | The proposed method improves on pre-training and small-scale fine-tuning experiments 2.4% over baselines. |
EA-Agent: A Structured Multi-Step Reasoning Agent for Entity Alignment (2026.acl-long)
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| Challenge: | Entity alignment (EA) aims to identify entities across different knowledge graphs (KGs) that refer to the same real-world object. |
| Approach: | They propose to use large language models to integrate semantic knowledge into EA to identify entities across different knowledge graphs that refer to the same object. |
| Outcome: | The proposed agent outperforms existing methods and achieves state-of-the-art performance on three benchmark datasets. |
CMMLU: Measuring massive multitask language understanding in Chinese (2024.findings-acl)
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| Challenge: | Existing large language models struggle to achieve an accuracy of even 60%, which is the pass mark for Chinese exams. |
| Approach: | They propose to use CMMLU to evaluate Chinese multilingual and Chinese LLMs in a comprehensive benchmark that covers various subjects and settings. |
| Outcome: | The proposed benchmark covers natural sciences, social sciences, engineering, and the humanities and aims to improve on existing models. |
UIOrchestra: Generating High-Fidelity Code from UI Designs with a Multi-agent System (2025.findings-emnlp)
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Chuhuai Yue, Jiajun Chai, Yufei Zhang, Zixiang Ding, Xihao Liang, Peixin Wang, Shihai Chen, Wang Yixuan, null Wangyanping, Guojun Yin, Wei Lin
| Challenge: | Recent advances in large language models have significantly improved automated code generation . however, the translation of complex mobile UI designs into high-fidelity front-end code remains a challenge . |
| Approach: | They propose a collaborative multi-agent system to reconstruct static single-page apps from mockups. |
| Outcome: | The proposed system outperforms existing methods in reconstructing complex app pages . the code and data will be released upon paper acceptance . |
NAT: Enhancing Agent Tuning with Negative Samples (2025.naacl-long)
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| Challenge: | Existing methods for fine-tuning and reinforcement learning use only positive examples, limiting their efficiency in low-resource scenarios. |
| Approach: | They propose a method that leverages both successful and failed trajectories for fine-tuning, maximizing the utility of limited resources. |
| Outcome: | The proposed method surpasses existing methods, including SFT, DPO, and PPO, across various tasks. |
Audio-centric Video Understanding Benchmark without Text Shortcut (2025.emnlp-main)
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Yudong Yang, Jimin Zhuang, Guangzhi Sun, Changli Tang, Yixuan Li, Peihan Li, Yifan Jiang, Wei Li, Zejun Ma, Chao Zhang
| Challenge: | Recent advances in multimodal large language models (MLLMs) focus on visual abilities, but audio is essential for video understanding. |
| Approach: | They propose an audio-centric video understanding benchmark to evaluate video comprehension capabilities of multimodal LLMs with a particular focus on auditory information. |
| Outcome: | The proposed video understanding benchmarks evaluate video comprehension capabilities of multimodal models with a particular focus on auditory information. |
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System (2022.acl-long)
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| Challenge: | Existing pre-trained language models often form a cascaded generation problem . this can lead to error accumulation across different sub-tasks and greater data annotation overhead. |
| Approach: | They propose a plug-and-play model for task-oriented dialogue that learns primary TOD task completion skills from heterogeneous dialog corpora. |
| Outcome: | The proposed model learns primary TOD task completion skills from heterogeneous dialog corpora. |
AutoFigure-Edit: Generating Editable Scientific Illustrations via Reference-Guided Styling (2026.acl-demo)
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Zhen Lin, Qiujie Xie, Minjun Zhu, Shichen Li, QiYao Sun, Enhao Gu, Yiran Ding, Ke Sun, Fang Guo, Panzhong Lu, Zhiyuan Ning, Yixuan Weng, Yue Zhang
| Challenge: | Existing automated systems for scientific illustrations are limited in editability, stylistic controllability, and efficiency. |
| Approach: | They propose an end-to-end system that generates fully editable scientific illustrations from long-form scientific text while enabling flexible style adaptation through user-provided reference images. |
| Outcome: | The proposed system generates fully editable scientific illustrations from long-form scientific texts while enabling flexible style adaptation through user-provided reference images. |
A Critical Analysis of Document Out-of-Distribution Detection (2023.findings-emnlp)
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Jiuxiang Gu, Yifei Ming, Yi Zhou, Jason Kuen, Vlad Morariu, Handong Zhao, Ruiyi Zhang, Nikolaos Barmpalios, Anqi Liu, Yixuan Li, Tong Sun, Ani Nenkova
| Challenge: | Existing document understanding models focus on single-modal inputs such as images or texts. |
| Approach: | They propose to use a spatial-aware adapter to adapt transformer-based language models to document domain to exploit multi-modal information. |
| Outcome: | The proposed model significantly improves the OOD detection performance compared to using a standard language model and to competitive baselines. |
DeepReview: Improving LLM-based Paper Review with Human-like Deep Thinking Process (2025.acl-long)
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| Challenge: | Existing Large Language Models (LLMs) face limited domain expertise, hallucinated reasoning, and a lack of structured evaluation. |
| Approach: | They propose a multi-stage framework to emulate expert reviewers by incorporating structured analysis, literature retrieval, and evidence-based argumentation. |
| Outcome: | The proposed model outperforms CycleReviewer-70B with fewer tokens and achieves 88.21% and 80.20% win rates. |
Unsolvable Problem Detection: Robust Understanding Evaluation for Large Multimodal Models (2025.acl-long)
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Atsuyuki Miyai, Jingkang Yang, Jingyang Zhang, Yifei Ming, Qing Yu, Go Irie, Yixuan Li, Hai Helen Li, Ziwei Liu, Kiyoharu Aizawa
| Challenge: | Multiple-choice question answering (MCQA) is widely used to assess the understanding capability of Large Multimodal Models (LMMs). |
| Approach: | They propose a task to evaluate the robust understanding capability of Large Multimodal Models (LMMs) they introduce a benchmark to assess performance across various ability dimensions . |
| Outcome: | The proposed model can withhold answers when encountering unsolvable problems of MCQA, proving it understands the answer. |
AiraXiv: An AI-Driven Open-Access Platform for Human and AI Scientists (2026.acl-demo)
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| Challenge: | Recent advances in artificial intelligence (AI) have accelerated the growth of both human-authored and AI-generated research outputs. |
| Approach: | They propose an AI-driven open-access platform built on open preprints, AI-augmented analysis and review, and reader feedback. |
| Outcome: | The proposed platform supports human scientists through an interactive UI and AI scientists through Model Context Protocol (MCP)-based interactions. |
Turning Trash into Treasure: Accelerating Inference of Large Language Models with Token Recycling (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) generate only one token at each decoding step, leading to high latency. |
| Approach: | They propose a speculative decoding paradigm that stores tokens in an adjacency matrix and employs a breadth-first-search algorithm to construct a draft tree. |
| Outcome: | The proposed method outperforms existing train-free methods by 30% and even a training method by 25%. |