Summary of Amr-evol: Adaptive Modular Response Evolution Elicits Better Knowledge Distillation For Large Language Models in Code Generation, by Ziyang Luo et al.
AMR-Evol: Adaptive Modular Response Evolution Elicits Better Knowledge Distillation for Large Language Models in Code Generation
by Ziyang Luo, Xin Li, Hongzhan Lin, Jing Ma, Lidong Bing
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 This study tackles the limitations of open-source models in replicating proprietary large language models (LLMs) like GPT4 in code generation. By focusing solely on response quality, researchers have often overlooked the importance of direct response distillation, which can degrade synthesized data and compromise knowledge distillation. The proposed Adaptive Modular Response Evolution (AMR-Evol) framework addresses this issue through a two-stage process: modular decomposition breaks down complex instructions into manageable sub-modules, while adaptive response evolution evolves the response with related function modules. Experimental results on three popular code benchmarks (HumanEval, MBPP, and EvalPlus) demonstrate the superiority of AMR-Evol over baseline methods, with performance enhancements of +3.0 points on HumanEval-Plus and +1.0 points on MBPP-Plus compared to open-source Code LLMs trained on similar data scales. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how to make computer code generation better. Right now, some models are really good at it, but they’re not open-source. Researchers want to create their own models that can do something similar. However, they’ve been focusing too much on making the generated code look like what a teacher model would say. This isn’t great because it can make the generated code worse. The study introduces a new way of doing things called Adaptive Modular Response Evolution (AMR-Evol). It breaks down complex instructions into smaller parts and then makes sure those parts are good by linking them to other relevant parts. The researchers tested this method on some coding challenges and found that it did better than previous methods. |
Keywords
» Artificial intelligence » Distillation » Knowledge distillation » Teacher model
