Summary of Xft: Unlocking the Power Of Code Instruction Tuning by Simply Merging Upcycled Mixture-of-experts, By Yifeng Ding et al.
XFT: Unlocking the Power of Code Instruction Tuning by Simply Merging Upcycled Mixture-of-Experts
by Yifeng Ding, Jiawei Liu, Yuxiang Wei, Terry Yue Zhuo, Lingming Zhang
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Software Engineering (cs.SE)
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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 The paper introduces XFT, a training scheme that combines upcycled Mixture-of-Experts (MoE) with Large Language Models (LLMs). While vanilla sparse upcycling fails to improve instruction tuning, XFT incorporates a shared expert mechanism and routing weight normalization strategy, boosting performance. The approach involves fine-tuning an upcycled MoE model and then merging it into a dense model, achieving state-of-the-art results on HumanEval and HumanEval+ with a 1.3B model. XFT also improves supervised fine-tuning (SFT) by 13% on HumanEval+, demonstrating its generalizability. The approach is orthogonal to existing techniques like Evol-Instruct and OSS-Instruct, opening new avenues for improving code instruction tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new way to train language models using a technique called XFT. This method combines two things: upcycled Mixture-of-Experts (MoE) and Large Language Models (LLMs). The result is a model that can learn more quickly and accurately than previous models. They tested this approach on several tasks and found it improved the results by 13%. What’s exciting about XFT is that it doesn’t replace other techniques, but rather works alongside them to make language models even better. |
Keywords
» Artificial intelligence » Boosting » Fine tuning » Instruction tuning » Mixture of experts » Supervised
