Papers by Yihong Chen
ORPP: Self-Optimizing Role-playing Prompts to Enhance Language Model Capabilities (2025.emnlp-main)
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| Challenge: | Existing research has explored model-driven strategies for prompt optimization, but these methods suffer from high computational overhead or require strong optimization capabilities from the model itself, which limits their broad applicability. |
| Approach: | They propose a framework that optimizes and generates role-playing prompts by limiting the prompt search space to role-player scenarios. |
| Outcome: | The proposed framework matches and surpasses existing prompt optimization methods in terms of performance. |
Mini-Model Adaptation: Efficiently Extending Pretrained Models to New Languages via Aligned Shallow Training (2023.findings-acl)
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| Challenge: | Existing approaches to pretrain Masked Language Models (MLMs) are expensive and require a full forward and backward pass over the entire model. |
| Approach: | They propose to learn a shallow mini-model from a fraction of a large model's parameters and plug it into a larger model for rapid cross-lingual transfer. |
| Outcome: | Experiments on XNLI, MLQA and PAWS-X show that mini-model adaptation matches the standard approach using up to 2.3x less compute on average. |
ZoomRAG: Hierarchical Random-walk Zooming across Multi-scale Information Graphs for Fast and Accurate RAG (2026.findings-acl)
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Xianming Hu, Jingyang Chen, Bin Tang, Yihe Liu, Yihong Huang, Hongbo Zhao, Nuoyi Chen, Jie Zhang, Ping Li, Kai Zhang
| Challenge: | retrieval-augmented generation (RAG) is a powerful tool for NLP applications . but it is challenging to encode large knowledge bases as compact offline structures . |
| Approach: | They propose a coarse-to-fine hierarchical graph inference method that uses random walks to retrieve information from a corpus of documents. |
| Outcome: | The proposed method reduces offline indexing costs and accelerates retrieval. |
M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis (2025.emnlp-main)
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ChengYan Wu, Bolei Ma, Yihong Liu, Zheyu Zhang, Ningyuan Deng, Yanshu Li, Baolan Chen, Yi Zhang, Yun Xue, Barbara Plank
| Challenge: | Existing studies focus on English-centric aspects of sentiment analysis, limiting scope for multilingual evaluation and research. |
| Approach: | They propose to use a multilingual dataset to analyze aspects with associated sentiment elements in text. |
| Outcome: | The proposed dataset is the most extensive multilingual parallel dataset for ABSA to date. |
The Rise of Darkness: Safety-Utility Trade-Offs in Role-Playing Dialogue Agents (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) demonstrate their utility in character simulations, but they pose a risk of generating unsafe content. |
| Approach: | They propose a method which dynamically adjusts safety-utility preferences based on the degree of risk coupling and guides the model to generate responses biased toward utility or safety. |
| Outcome: | The proposed method improves safety metrics while maintaining utility. |
LLM-Powered Test Case Generation for Detecting Bugs in Plausible Programs (2025.acl-long)
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Kaibo Liu, Zhenpeng Chen, Yiyang Liu, Jie M. Zhang, Mark Harman, Yudong Han, Yun Ma, Yihong Dong, Ge Li, Gang Huang
| Challenge: | TrickCatcher generates test cases that pass existing tests yet contain bugs . a recent study found that tricky bugs are not detected by test suites . |
| Approach: | They propose an LLM-powered approach to generating test cases for uncovering bugs in plausible programs . they use a PUT and specification to generate program variants, an input generator and an Llm to construct test inputs . |
| Outcome: | The proposed approach achieves recall, precision, and F1 scores that are 1.80, 2.65, and 1.66 . trickCatcher generates program variants based on the program under test and its specification . |
Adaptive Zooming via Relevance-Informed Positional Resource Allocation for Training-free LLM Context Extension (2026.findings-acl)
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Hongbo Zhao, Huibin Wang, Bin Tang, Xianming Hu, Yihong Huang, Yijun Shen, Nuoyi Chen, Ping Li, Kai Zhang
| Challenge: | Existing training-free methods for extrapolating beyond training context lengths are semantics-agnostic . Existing methods that focus on relative token distances can indiscriminately blur semantically relevant and irrelevant tokens . |
| Approach: | They propose an adaptive positional zooming method that uses semantic relevance to extrapolate beyond training context lengths. |
| Outcome: | Experiments show that RiPRA outperforms existing training-free extrapolation methods . relevant tokens get higher positional resolution, while irrelevant tokens are compressed . |
You Impress Me: Dialogue Generation via Mutual Persona Perception (2020.acl-main)
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| Challenge: | Existing chit-chat systems tend to generate uninformative responses and lack coherent personality traits due to the diversity of speakers. |
| Approach: | They propose a transmitter-receiver framework which explicitly models understanding between interlocutors. |
| Outcome: | The proposed framework improves on a large public dataset, Persona-Chat, with a significant boost over the state-of-the-art frameworks. |
EvoCoT: Overcoming the Exploration Bottleneck in Reinforcement Learning for LLMs (2026.findings-acl)
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| Challenge: | Existing approaches to reinforcement learning with verifiable reward (RLVR) are limited by difficulty or lack of exploration. |
| Approach: | They propose a self-evolving curriculum learning framework based on chain-of-thought reasoning optimization that constrains exploration space by self-generating and verifying CoT trajectories. |
| Outcome: | The proposed framework enables LLMs to solve previously unsolved problems without external supervision and is compatible with various RL fine-tuning methods. |
On Relation-Specific Neurons in Large Language Models (2025.emnlp-main)
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Yihong Liu, Runsheng Chen, Lea Hirlimann, Ahmad Dawar Hakimi, Mingyang Wang, Amir Hossein Kargaran, Sascha Rothe, François Yvon, Hinrich Schuetze
| Challenge: | In large language models, certain neurons can store distinct pieces of knowledge learned during pretraining. |
| Approach: | They hypothesize that relation-specific neurons detect relation in input text and guide generation involving such a relation. |
| Outcome: | The proposed model can handle facts involving relation r and facts containing a different relation . |
Multilingual Language Model Pretraining using Machine-translated Data (2025.emnlp-main)
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Jiayi Wang, Yao Lu, Maurice Weber, Max Ryabinin, David Ifeoluwa Adelani, Yihong Chen, Raphael Tang, Pontus Stenetorp
| Challenge: | Existing methods for collecting and filtering multilingual web data lead to most languages lagging behind English performance due to the Internet's English-centric nature. |
| Approach: | They propose to translate a high-quality English web corpus into nine languages and pretrain a 1.3B-parameter model on it. |
| Outcome: | The proposed model matches or outperforms multilingual LLMs of similar size across Non-English understanding and reasoning tasks despite being trained on an order of magnitude less data. |
DefGen-Bench: A Benchmark for Chinese Criminal Defence Opinion Generation in LegalAI (2026.acl-long)
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| Challenge: | Existing LegalAI tasks are descriptive or predictive, requiring the users to translate the information into legal reasoning. |
| Approach: | They propose a task to generate a structured defence opinion conditioned jointly on an indictment and the defendant’s stated opinion, which often present conflicting claims. |
| Outcome: | The proposed approach improves on eight large language models (LLMs) and shows that it is more efficient than previous approaches. |
Exploiting Prompt-induced Confidence for Black-Box Attacks on LLMs (2025.findings-emnlp)
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| Challenge: | Large language models (LLMs) are vulnerable to adversarial attacks even in strict black-box settings with only hard-label feedback. |
| Approach: | They propose a black-box framework that leverages prompt-induced confidence as an auxiliary signal to guide attacks. |
| Outcome: | The proposed framework improves the attack success rate and query efficiency while maintaining semantic fidelity. |
RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization (2026.acl-long)
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Yihong Dong, Xue Jiang, Yongding Tao, Huanyu Liu, Kechi Zhang, Lili Mou, Rongyu Cao, Yingwei MA, Jue Chen, Binhua Li, Zhi Jin, Fei Huang, Yongbin Li, Ge Li
| Challenge: | Reinforcement Learning with Verifiable Reward (RLVR) has significantly advanced the complex reasoning abilities of Large Language Models (LLMs). |
| Approach: | They propose a hybrid-policy optimization approach that synergizes internal exploitation with external data to achieve stronger reasoning capabilities. |
| Outcome: | The proposed approach achieves state-of-the-art performance on six math reasoning benchmarks and superior performance on out-of distribution reasoning tasks. |