Papers by Haonan Yu
Resource-Limited Joint Multimodal Sentiment Reasoning and Classification via Chain-of-Thought Enhancement and Distillation (2026.findings-acl)
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| Challenge: | Current approaches for Multimodal Sentiment Analysis (MSA) rely on parameter-heavy LLMs for classification, overlooking multimodal sentiment reasoning generation in resource-limited environments. |
| Approach: | They propose a multimodal sentiment reasoning distillation model that employs a teacher-assistant-student paradigm to address deployment constraints in resource-limited environments. |
| Outcome: | The proposed model performs well on a resource-limited JMSRC task with only 3B parameters and shows generalization and interpretability. |
Revitalizing Black-Box Interpretability: Actionable Interpretability for LLMs via Proxy Models (2026.acl-long)
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| Challenge: | Applying model-agnostic explanations to Large Language Models is hindered by prohibitive computational costs rendering them dormant for real-world applications. |
| Approach: | They propose a budget-friendly proxy framework that leverages efficient models to approximate the decision boundaries of expensive Large Language Models. |
| Outcome: | The proposed framework achieves over 90% fidelity with only 9.5% of the oracle’s cost and is open-source to facilitate future research. |
Interactive Language Acquisition with One-shot Visual Concept Learning through a Conversational Game (P18-1)
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| Challenge: | supervised language learning is limited by the ability of capturing mainly the statistics of training data. |
| Approach: | They propose to use conversational games to train agents to use new knowledge . they propose to mimic and reinforce conversational game and use it in one-shot fashion . |
| Outcome: | The proposed approach is able to acquire information by asking questions about novel objects and use the just-learned knowledge in subsequent conversations in a one-shot fashion. |
VChain: Chain-of-Visual-Thought for Reasoning in Video Generation (2026.findings-acl)
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| Challenge: | Recent video generation models struggle to synthesize complex dynamics with a coherent chain of consequences. |
| Approach: | They propose a framework that injects visual reasoning signals from multimodal models into video generation. |
| Outcome: | a new framework that leverages multimodal models to generate sparse keyframes significantly improves quality of generated videos. |