Papers by Yuhan Song

CPT-Agent: A Cognitive Process Theory-driven Framework for Student Simulation in Writing Development (2026.acl-long)

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Yuhan Chen, Zizhuo Shen, Miaomiao Cheng, Xu Han, Jiefu Gong, Shijin Wang, Wei Song

Challenge:	Existing LLMs model overly capable learners who over-apply feedback, resulting in pedagogically implausible behavior.
Approach:	They propose a framework that decouples cognitive ability from writing proficiency and models their interaction during writing and revision.
Outcome:	The proposed model produces distinguishable proficiency levels and is consistent with instructional theories.

The Stepwise Deception: Simulating the Evolution from True News to Fake News with LLM Agents (2025.emnlp-main)

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Yuhan Liu, Zirui Song, Juntian Zhang, Xiaoqing Zhang, Xiuying Chen, Rui Yan

Responsible & Trustworthy NLP

Challenge:	Existing studies assume fake news is inherently existing rather than exploring its gradual formation.
Approach:	They propose a Large Language Model-based simulation approach explicitly focusing on fake news evolution from real news.
Outcome:	The proposed framework captures fake news evolution patterns and accurately reproduces known fake news, aligning closely with human evaluations.

Beyond Static Testbeds: An Interaction-Centric Agent Simulation Platform for Dynamic Recommender Systems (2025.emnlp-main)

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Song Jin, Juntian Zhang, Yuhan Liu, Xun Zhang, Yufei Zhang, Guojun Yin, Fei Jiang, Wei Lin, Rui Yan

Challenge:	Existing platforms lack a mechanism for user actions to dynamically reshape the environment.
Approach:	They propose a novel agent-based simulation platform for recommender systems with a robust interaction mechanism.
Outcome:	The proposed platform improves the credibility of the simulation and replicates the Matthew Effect and Brand Loyalty.

Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey (2025.findings-emnlp)

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Zirui Song, Bin Yan, Yuhan Liu, Miao Fang, Mingzhe Li, Rui Yan, Xiuying Chen

Language Models

Challenge:	specialized LLMs are often limited in domain-specific applications that require specialized knowledge.
Approach:	They provide a comprehensive overview of four key methods to enhance large language models by integrating domain-specific knowledge.
Outcome:	The proposed methods are categorized into four key approaches: dynamic knowledge injection, static knowledge embedding, modular adapters, and prompt optimization.

Unsupervised Chinese Word Segmentation with BERT Oriented Probing and Transformation (2022.findings-acl)

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Wei Li, Yuhan Song, Qi Su, Yanqiu Shao

Low-Resource NLP

Challenge:	Existing methods for unsupervised Chinese word segmentation exploit shallow semantic information, which can miss important context.
Approach:	They propose to take advantage of deep contextual semantic information with a self-training manner to transform it into explicit word segmentation ability.
Outcome:	The proposed approach achieves state-of-the-art F1 score on two CWS benchmark datasets.

ParetoRAG: Leveraging Sentence-Context Attention for Robust and Efficient Retrieval-Augmented Generation (2025.findings-emnlp)

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Ruobing Yao, Yifei Zhang, Shuang Song, Yuhan Liu, Neng Gao, Chenyang Tu

Responsible & Trustworthy NLP

Challenge:	Retrieval-augmented generation systems face persistent challenges in retrieval inefficiency and the inability of LLMs to filter out irrelevant information.
Approach:	They propose an unsupervised framework that optimizes RAG systems through sentence-level refinement guided by the Pareto principle.
Outcome:	The proposed framework achieves dual improvements in retrieval precision and generation quality without additional training or API resources while using only 40% of the tokens compared to traditional approaches.

Attention Weights as an Indicator: Analyzing and Improving Document Utilization in Retrieval-Augmented Generation (2026.acl-long)

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Jing Jin, Yuhan Song, Wen Luo, Houfeng Wang

Information Retrieval

Challenge:	In traditional RAG models, documents are grouped into categories based on their quality and order, and the quality of inputs is variable due to ineffective retrievers or misalignment between the retriever and generator.
Approach:	They propose to use attention weights to enhance document utilization from three perspectives: document ranking, placement, and filtering.
Outcome:	The proposed method outperforms baselines and improves document utilization effectiveness in a training-free manner.

Temporal Contrastive Decoding: A Training-Free Method for Large Audio-Language Models (2026.findings-acl)

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Yanda Li, Yuhan Liu, Zirui Song, Yunchao Wei, Martin Takáč, Salem Lahlou

Semantic Text Processing

Challenge:	Large audio-language models (LALMs) can exhibit a temporal smoothing bias . unified decoders can produce less specific audio-grounded outputs .
Approach:	They propose a temporally blurred slow-path view that is re-encoded by a token-level logit update.
Outcome:	Experiments on MMAU and AIR-Bench show consistent improvements on strong unified LALMs.

Beyond Transcription: Unified Audio Schema for Perception-Aware AudioLLMs (2026.findings-acl)

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Linhao Zhang, Yuhan Song, Aiwei Liu, Chuhan Wu, Sijun Zhang, Wei Jia, Yuan Liu, Houfeng Wang, Zhou Xiao

Multimodality

Challenge:	Recent Audio Large Language Models (AudioLLMs) excel at reasoning tasks, but struggle at elementary auditory perception.
Approach:	They propose a framework that organizes audio information into three explicit components in a unified JSON format.
Outcome:	The proposed framework boosts fine-grained perception by 10.9% on MMSU over state-of-the-art models while preserving robust reasoning capabilities.

Would You Like to Make a Donation? A Dialogue System to Persuade You to Donate (2024.lrec-main)

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Yuhan Song, Houfeng Wang

Dialogue Systems & Conversational Agents

Challenge:	Persuasive automated dialogue systems are a popular way to influence people's behavior and decision making.
Approach:	They propose to use a context-aware persuasion strategy selection module to persult users . they also propose a persuasiveness prediction model to automatically evaluate the persuasiveness of generated text.
Outcome:	The proposed system can achieve better performance on several automated evaluation metrics than baseline models.

GlossaGen: Making Academic Translation Smarter with Glossing (2026.findings-acl)

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Zixiao Wang, Duzhen Zhang, Juntian Zhang, Yuhan Liu, Guoming Li, Haolun Wu, Le Song, Xiuying Chen

Responsible & Trustworthy NLP

Challenge:	Existing machine translation systems obscure or mistranslate key terminology, while paraphrasing aimed at lay readers often oversimplifies it, hindering their ability to master domain-specific technical vocabulary.
Approach:	They propose a task which produces translations dynamically adapted to a reader’s academic proficiency, or level, and a framework to address this challenge.
Outcome:	The proposed framework achieves higher scores than baselines on a synthesized benchmark and human evaluations.