Papers by Zimo Chen
GALA: Geometric Data Selection with Strategic Prospecting for Large Language Model Self-training (2026.findings-acl)
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| Challenge: | Existing approaches to self-training are based on reject sampling and lack quality reasoning paths. |
| Approach: | They propose a framework for self-training using a generate-and-filter paradigm . they propose to identify diverse and informative samples from redundant data and exploit them more strategically. |
| Outcome: | The proposed framework exploits informative samples from redundant data and improves reasoning trajectory prospecting. |
Beyond Prompt: Fine-grained Simulation of Cognitively Impaired Standardized Patients via Stochastic Steering (2026.findings-acl)
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| Challenge: | Existing methods for training patients with cognitive impairment rely on discrete prompt engineering and fail to capture the heterogeneity of deficits across domains and severity levels. |
| Approach: | They propose to use steering vectors from contrastive pairs of instructions and responses to capture domain-specific features and introduce a Stochastic Token Modulation mechanism to regulate the intervention probability. |
| Outcome: | The proposed model outperforms baselines in clinical authenticity and severity controllability while remaining open-source. |
JanusMM: A Benchmark for Self-Deprecation Understanding in Real-World Multimodal Conversations (2026.acl-long)
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Xinyi Xu, Bingguang Hao, Yongyi Xiong, Zimo Chen, Xinchen Liu, Hongxin Guo, Xuelong Wang, Silin Zhou, Shihan Dou
| Challenge: | Multimodal large language models (MLLMs) are a common communicative strategy in human society, often using image-text interplay to express emotions and intentions. |
| Approach: | They propose to evaluate multimodal large language models (MLLMs)' understanding of self-deprecation in real-world conversations using 2,016 bilingual memes. |
| Outcome: | The proposed framework evaluates MLLMs' understanding of self-deprecation in real-world conversations. |