Summary of Itertl: An Iterative Framework For Fine-tuning Llms For Rtl Code Generation, by Peiyang Wu et al.
ITERTL: An Iterative Framework for Fine-tuning LLMs for RTL Code Generation
by Peiyang Wu, Nan Guo, Xiao Xiao, Wenming Li, Xiaochun Ye, Dongrui Fan
First submitted to arxiv on: 28 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 The researchers explore the potential of large language models (LLMs) in generating Register Transfer Language (RTL) code, building upon their success in understanding human instructions and generating code. To address the limitations of existing fine-tuning approaches, which rely on fixed datasets and require large amounts of reference data, the authors introduce an iterative training paradigm called ITERTL. This method iteratively draws samples from the model trained in the previous cycle and uses them to train the model in the current loop, reducing distribution mismatch and enabling the model to explore a broader generative space. Theoretical analyses support the effectiveness of this approach, and experimental results show that the proposed method can compete with or outperform state-of-the-art open-source models using nearly 37% reference samples, achieving impressive pass@1 rates on VerilogEval evaluation datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs are super smart computers that can understand human instructions and generate code. Researchers want to use these models to create special computer language called RTL code. The problem is that existing methods need a lot of data and don’t fully work. To fix this, the researchers created a new way to train the model called ITERTL. It’s like a loop where the model learns from its own mistakes and gets better each time. This helps the model create more accurate RTL code even with limited data. The results are impressive, showing that the new method can work just as well as other top-performing models. |
Keywords
* Artificial intelligence * Fine tuning
