Summary of Divide-or-conquer? Which Part Should You Distill Your Llm?, by Zhuofeng Wu et al.
Divide-or-Conquer? Which Part Should You Distill Your LLM?
by Zhuofeng Wu, He Bai, Aonan Zhang, Jiatao Gu, VG Vinod Vydiswaran, Navdeep Jaitly, Yizhe Zhang
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach is proposed to improve the performance of Large Language Models (LLMs) on reasoning tasks by breaking them down into a problem decomposition phase and a problem-solving phase. This multi-stage strategy outperforms single-stage solutions, leveraging general problem-solving strategies for easier distillation into smaller models. While problem decomposition can be effectively distilled, the problem-solving capability is harder to distill without performance loss and struggles with generalization. The proposed approach combines small, distilled problem decomposition models with LLMs for cost-efficient inference and local adaptation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are very smart computers that can solve problems by breaking them down into smaller parts. This helps them figure out the solution more easily. In this paper, scientists came up with a new way to make these computers even better at solving problems. They divided the problem-solving process into two stages: first, they broke the problem down into smaller pieces, and then they solved each piece. This multi-step approach worked really well! The scientists also found that they could take the smaller problem-breaking stage and shrink it down into a tiny model that can be used in many different situations. However, the larger problem-solving stage is harder to shrink without losing its effectiveness. By combining these two approaches, scientists hope to create more efficient and adaptable problem-solvers. |
Keywords
* Artificial intelligence * Distillation * Generalization * Inference
