Papers by Yitong Zhu

2 papers
QA-MoE: Towards a Continuous Reliability Spectrum with Quality-Aware Mixture of Experts for Robust Multimodal Sentiment Analysis (2026.acl-long)

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Challenge: Existing models that use multimodal inputs are often noisy or incomplete.
Approach: They propose a Quality-Aware Mixture-of-Experts framework that quantifies modality reliability via aleatoric uncertainty.
Outcome: The proposed framework is competitive or state-of-the-art across diverse degradation scenarios and exhibits a promising One-Checkpoint-for-all property in practice.
Concise and Precise Context Compression for Tool-Using Language Models (2024.findings-acl)

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Challenge: Existing methods suffer from key information loss and difficulty in adjusting the length of compressed sequences based on documentation lengths.
Approach: They propose two strategies for compressing tool documentation into concise and precise summary sequences for tool-using language models.
Outcome: The proposed approach achieves comparable performance to the upper-bound baseline under 16x compression ratio.

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