Papers by Yifu Ding

2 papers
Dynamic Parallel Tree Search for Efficient LLM Reasoning (2025.acl-long)

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Challenge: Recent methods focus on search accuracy while overlooking computational efficiency.
Approach: They propose a parallelism framework that dynamically optimizes reasoning path in inference.
Outcome: The proposed framework improves efficiency by 2-4 on average while maintaining or even surpassing existing reasoning algorithms in accuracy.
DB-LLM: Accurate Dual-Binarization for Efficient LLMs (2024.findings-acl)

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Challenge: Existing methods for ultra-low bit quantization cause severe accuracy drops . a novel Dual-Binarization method is proposed for efficient Large Language Models .
Approach: They propose a Dual-Binarization method that takes 2-bit-width and binarization into account . they propose DB-LLM, which uses a 2-bit binarized weighted model to represent weights efficiently .
Outcome: The proposed method surpasses the current State-of-the-Art in ultra-low bit quantization and achieves 20% reduction in computational consumption compared to the SOTA method under the same bit-width.

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