Customized Retrieval Augmented Generation and Benchmarking for EDA Tool Documentation QA

• URL: http://arxiv.org/abs/2407.15353v2
• Date: Fri, 26 Jul 2024 08:36:25 GMT
• Title: Customized Retrieval Augmented Generation and Benchmarking for EDA Tool Documentation QA
• Authors: Yuan Pu, Zhuolun He, Tairu Qiu, Haoyuan Wu, Bei Yu,
• Abstract summary: Retrieval augmented generation (RAG) enhances the accuracy and reliability of generative AI models by sourcing factual information from external databases. This paper proposes a customized RAG framework along with three domain-specific techniques for EDA tool documentation QA. We have developed and released a documentation QA evaluation benchmark, ORD-QA, for OpenROAD, an advanced RTL-to-GDSII design platform.
• Score: 5.0108982850526
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Retrieval augmented generation (RAG) enhances the accuracy and reliability of generative AI models by sourcing factual information from external databases, which is extensively employed in document-grounded question-answering (QA) tasks. Off-the-shelf RAG flows are well pretrained on general-purpose documents, yet they encounter significant challenges when being applied to knowledge-intensive vertical domains, such as electronic design automation (EDA). This paper addresses such issue by proposing a customized RAG framework along with three domain-specific techniques for EDA tool documentation QA, including a contrastive learning scheme for text embedding model fine-tuning, a reranker distilled from proprietary LLM, and a generative LLM fine-tuned with high-quality domain corpus. Furthermore, we have developed and released a documentation QA evaluation benchmark, ORD-QA, for OpenROAD, an advanced RTL-to-GDSII design platform. Experimental results demonstrate that our proposed RAG flow and techniques have achieved superior performance on ORD-QA as well as on a commercial tool, compared with state-of-the-arts. The ORD-QA benchmark and the training dataset for our customized RAG flow are open-source at https://github.com/lesliepy99/RAG-EDA.

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