Summary of Multi-stage Balanced Distillation: Addressing Long-tail Challenges in Sequence-level Knowledge Distillation, by Yuhang Zhou et al.
Multi-Stage Balanced Distillation: Addressing Long-Tail Challenges in Sequence-Level Knowledge Distillation
by Yuhang Zhou, Jing Zhu, Paiheng Xu, Xiaoyu Liu, Xiyao Wang, Danai Koutra, Wei Ai, Furong Huang
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers explore ways to deploy large language models (LLMs) efficiently while maintaining their capabilities. Knowledge distillation (KD), a technique that transfers skills from teacher LLMs to smaller student models, is studied in detail. Specifically, sequence-level KD, which focuses on the reasoning process rather than just final outcomes, shows great promise for enhancing students’ abilities. However, existing methods struggle with applying KD under long-tailed data distributions, leading to poor generalization on less represented domains. To address this issue, the authors propose the Multi-Stage Balanced Distillation (BalDistill) framework, which iteratively balances training data within a fixed computational budget. By dynamically selecting representative head domain examples and synthesizing tail domain examples, BalDistill achieves state-of-the-art performance across diverse long-tailed datasets, improving both the efficiency and efficacy of the distilled models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making big language models work better on smaller computers without losing their abilities. The researchers are trying to find a way to make these models learn from each other more efficiently. They’re looking at a technique called knowledge distillation, which helps smaller models learn from bigger ones by copying the steps they take to solve problems. The problem is that this process doesn’t work well when there’s not much data available for certain topics. To fix this, the authors came up with a new way of doing knowledge distillation called Multi-Stage Balanced Distillation (BalDistill). It helps the models learn from each other better and do it more efficiently. |
Keywords
» Artificial intelligence » Distillation » Generalization » Knowledge distillation
