Large Language Model for Verilog Generation with Golden Code Feedback

• URL: http://arxiv.org/abs/2407.18271v2
• Date: Mon, 5 Aug 2024 06:12:46 GMT
• Title: Large Language Model for Verilog Generation with Golden Code Feedback
• Authors: Ning Wang, Bingkun Yao, Jie Zhou, Xi Wang, Zhe Jiang, Nan Guan,
• Abstract summary: This study introduces a novel approach utilizing reinforcement learning with golden code feedback to enhance the performance of pre-trained models. We have achieved state-of-the-art (SOTA) results with a substantial margin. Notably, our 6.7B parameter model ours demonstrates superior performance compared to current best-in-class 13B and 16B models.
• Score: 29.135207235743795
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in large language models (LLMs) have catalyzed significant interest in the automatic generation of Register-Transfer Level (RTL) code, particularly Verilog, from natural language instructions. While commercial LLMs like ChatGPT have dominated this domain, open-source alternatives have lagged considerably in performance, limiting the flexibility and data privacy of this emerging technology. This study introduces a novel approach utilizing reinforcement learning with golden code feedback to enhance the performance of pre-trained models. Leveraging open-source data and base models, we have achieved state-of-the-art (SOTA) results with a substantial margin. Notably, our 6.7B parameter model \ours{} demonstrates superior performance compared to current best-in-class 13B and 16B models. Furthermore, through a comprehensive analysis of the limitations in direct fine-tuning and the training dynamics of reinforcement learning, we posit that the development of comprehensive supervisory signals, which are align with the inherent parallel semantics of Verilog code, is critical to effective generation. The code and data associated with this research are publicly available at \url{https://github.com/CatIIIIIIII/veriseek}. The model weights can be accessed at \url{https://huggingface.co/WANGNingroci/VeriSeek}.

Related papers

• Language Models are Graph Learners [70.14063765424012]
  Language Models (LMs) are challenging the dominance of domain-specific models, including Graph Neural Networks (GNNs) and Graph Transformers (GTs) We propose a novel approach that empowers off-the-shelf LMs to achieve performance comparable to state-of-the-art GNNs on node classification tasks.
  arXiv  Detail & Related papers  (2024-10-03T08:27:54Z)
• SIaM: Self-Improving Code-Assisted Mathematical Reasoning of Large Language Models [54.78329741186446]
  We propose a novel paradigm that uses a code-based critic model to guide steps including question-code data construction, quality control, and complementary evaluation. Experiments across both in-domain and out-of-domain benchmarks in English and Chinese demonstrate the effectiveness of the proposed paradigm.
  arXiv  Detail & Related papers  (2024-08-28T06:33:03Z)
• SoTaNa: The Open-Source Software Development Assistant [81.86136560157266]
  SoTaNa is an open-source software development assistant. It generates high-quality instruction-based data for the domain of software engineering. It employs a parameter-efficient fine-tuning approach to enhance the open-source foundation model, LLaMA.
  arXiv  Detail & Related papers  (2023-08-25T14:56:21Z)
• RAVEN: In-Context Learning with Retrieval-Augmented Encoder-Decoder Language Models [57.12888828853409]
  RAVEN is a model that combines retrieval-augmented masked language modeling and prefix language modeling. Fusion-in-Context Learning enables the model to leverage more in-context examples without requiring additional training. Our work underscores the potential of retrieval-augmented encoder-decoder language models for in-context learning.
  arXiv  Detail & Related papers  (2023-08-15T17:59:18Z)
• Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations [63.19448893196642]
  We present a novel framework that harnesses the rich contextual information and semantic representations provided by large language models to analyze behavior graphs. By leveraging this capability, our framework enables personalized and accurate job recommendations for individual users.
  arXiv  Detail & Related papers  (2023-07-10T11:29:41Z)
• ReGen: Zero-Shot Text Classification via Training Data Generation with Progressive Dense Retrieval [22.882301169283323]
  We propose a retrieval-enhanced framework to create training data from a general-domain unlabeled corpus. Experiments on nine datasets demonstrate that REGEN achieves 4.3% gain over the strongest baselines and saves around 70% of the time compared to baselines using large NLG models.
  arXiv  Detail & Related papers  (2023-05-18T04:30:09Z)
• Enhancing Knowledge Graph Construction Using Large Language Models [0.0]
  This paper analyzes how the current advances in foundational LLM, like ChatGPT, can be compared with the specialized pretrained models, like REBEL, for joint entity and relation extraction. We created pipelines for the automatic creation of Knowledge Graphs from raw texts, and our findings indicate that using advanced LLM models can improve the accuracy of the process of creating these graphs from unstructured text.
  arXiv  Detail & Related papers  (2023-05-08T12:53:06Z)
• Better Language Models of Code through Self-Improvement [18.75015225501755]
  We propose a simple data augmentation framework for pre-trained language models for code (PLMCs) Our framework utilizes knowledge gained during the pre-training and fine-tuning stage to generate pseudo data, which is then used as training data for the next step. The results show that our framework significantly improves PLMCs' performance in code-related sequence generation tasks.
  arXiv  Detail & Related papers  (2023-04-02T10:59:19Z)
• Unsupervised Paraphrasing with Pretrained Language Models [85.03373221588707]
  We propose a training pipeline that enables pre-trained language models to generate high-quality paraphrases in an unsupervised setting. Our recipe consists of task-adaptation, self-supervision, and a novel decoding algorithm named Dynamic Blocking. We show with automatic and human evaluations that our approach achieves state-of-the-art performance on both the Quora Question Pair and the ParaNMT datasets.
  arXiv  Detail & Related papers  (2020-10-24T11:55:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.