Papers by Jiahao Yuan
ReflectDiffu: Reflect between Emotion-intent Contagion and Mimicry for Empathetic Response Generation via a RL-Diffusion Framework (2025.acl-long)
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| Challenge: | Existing models for empathetic dialogue generation neglect the intricate interplay between emotion and intent, leading to suboptimal controllability of empathy. |
| Approach: | They propose a framework that integrates emotion contagion and intent mimicry to enhance empathetic response generation. |
| Outcome: | The proposed framework outperforms existing models in relevance, controllability, and informativeness. |
Reversal of Thought: Enhancing Large Language Models with Preference-Guided Reverse Reasoning Warm-up (2025.acl-long)
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| Challenge: | Existing methods to improve LLMs’ logical capabilities involve traceable or verifiable logical sequences that generate more reliable responses yet increase computational costs, or introduce rigid logic template rules, reducing flexibility. |
| Approach: | They propose a plug-and-play reasoning framework that enhances LLMs' logical reasoning abilities during the warm-up phase prior to batch inference. |
| Outcome: | The proposed framework surpasses baselines in both reasoning accuracy and efficiency. |
Refuse Whenever You Feel Unsafe: Improving Safety in LLMs via Decoupled Refusal Training (2025.acl-long)
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Youliang Yuan, Wenxiang Jiao, Wenxuan Wang, Jen-tse Huang, Jiahao Xu, Tian Liang, Pinjia He, Zhaopeng Tu
| Challenge: | Large Language Models exhibit a level of intelligence that is both impressive and everevolving, but their ability to refuse generating unsafe content is a double-edged sword. |
| Approach: | They propose a method to tackle a refusal position bias within safety tuning data that compromises the models’ ability to appropriately refuse generating unsafe content. |
| Outcome: | The proposed method significantly improves model safety without compromising performance and surpasses baseline methods in defending against attacks. |
Chain-of-Thought Prompting Obscures Hallucination Cues in Large Language Models: An Empirical Evaluation (2025.findings-emnlp)
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| Challenge: | Chain-of-Thought (CoT) prompting can mitigate hallucinations by encouraging step-by-step reasoning, but its impact on halluciation detection remains underexplored. |
| Approach: | They conduct an empirical evaluation of CoT prompting in Large Language Models (LLMs) to examine their impact on hallucination detection methods. |
| Outcome: | The proposed method significantly affects the internal states and token probability distributions of the LLM. |
ReMamba: Equip Mamba with Effective Long-Sequence Modeling (2025.findings-emnlp)
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| Challenge: | Mamba models demonstrate superior inference efficiency and competitive performance on short-context tasks, but their capacity to comprehend long contexts is limited compared to transformer-based models. |
| Approach: | They propose a model which incorporates selective compression and adaptation techniques within a two-stage re-forward process, incurring minimal additional inference costs overhead. |
| Outcome: | The proposed model improves on the LongBench and L-Eval benchmarks by 3.2 and 1.6 points and attains performance almost on par with same-size transformer models. |
ATLAS: Orchestrating Heterogeneous Models and Tools for Multi-Domain Complex Reasoning (2026.findings-acl)
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Jinyang Wu, Guocheng Zhai, Ruihan Jin, Jiahao Yuan, Yuhao Shen, Shuai Zhang, Zhengqi Wen, Jianhua Tao
| Challenge: | Existing approaches to optimize large language models with external tools are limited. |
| Approach: | They propose a dual-path framework for dynamic tool usage in cross-domain complex reasoning . they exploit empirical priors for domain-specific alignment and RL-based multi-step routing . |
| Outcome: | The proposed framework outperforms closed-source models and existing methods on in-distribution and out-of-distortion tasks. |