# EDA Corpus: A Large Language Model Dataset for Enhanced Interaction with OpenROAD

arXiv: https://arxiv.org/abs/2405.06676 · HTML: https://mlsanity.com/paper/2405.06676/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 4 May 2024.

## Builds on (most cited first, 12 of 12)

- [LIMA: Less Is More for Alignment](https://mlsanity.com/paper/2305.11206/) (2023)
- [VerilogEval: Evaluating Large Language Models for Verilog Code Generation](https://mlsanity.com/paper/2309.07544/) (2023)
- [ChipNeMo: Domain-Adapted LLMs for Chip Design](https://mlsanity.com/paper/2311.00176/) (2023)
- [ChatEDA: A Large Language Model Powered Autonomous Agent for EDA](https://mlsanity.com/paper/2308.10204/) (2023)
- [ChipGPT: How far are we from natural language hardware design](https://mlsanity.com/paper/2305.14019/) (2023)
- [RTLFixer: Automatically Fixing RTL Syntax Errors with Large Language Models](https://mlsanity.com/paper/2311.16543/) (2023)
- [AutoChip: Automating HDL Generation Using LLM Feedback](https://mlsanity.com/paper/2311.04887/) (2023)
- [GPT4AIGChip: Towards Next-Generation AI Accelerator Design Automation via Large Language Models](https://mlsanity.com/paper/2309.10730/) (2023)
- [LLM4EDA: Emerging Progress in Large Language Models for Electronic Design Automation](https://mlsanity.com/paper/2401.12224/) (2023)
- [Advanced Large Language Model (LLM)-Driven Verilog Development: Enhancing Power, Performance, and Area Optimization in Code Synthesis](https://mlsanity.com/paper/2312.01022/) (2023)
- [Unlocking Hardware Security Assurance: The Potential of LLMs](https://mlsanity.com/paper/2308.11042/) (2023)
- [iEDA: An Open-Source Intelligent Physical Implementation Toolkit and Library](https://mlsanity.com/paper/2308.01857/) (2023)

## Built on by (most cited first, 13 of 13)

- [A Survey of Research in Large Language Models for Electronic Design Automation](https://mlsanity.com/paper/2501.09655/) (2025)
- [DeepCircuitX: A Comprehensive Repository-Level Dataset for RTL Code Understanding, Generation, and PPA Analysis](https://mlsanity.com/paper/2502.18297/) (2025)
- [Enhancing Large Language Models for Hardware Verification: A Novel SystemVerilog Assertion Dataset](https://mlsanity.com/paper/2503.08923/) (2025)
- [MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs](https://mlsanity.com/paper/2507.19525/) (2025)
- [ORAssistant: A Custom RAG-based Conversational Assistant for OpenROAD](https://mlsanity.com/paper/2410.03845/) (2024)
- [Timing Analysis Agent: Autonomous Multi-Corner Multi-Mode (MCMM) Timing Debugging with Timing Debug Relation Graph](https://mlsanity.com/paper/2504.11502/) (2025)
- [Automated QoR improvement in OpenROAD with coding agents](https://mlsanity.com/paper/2601.06268/) (2026)
- [Improving LLM-Powered EDA Assistants with RAFT](https://mlsanity.com/paper/2506.06500/) (2025)
- [ALKAFI-LLAMA3: Fine-Tuning LLMs for Precise Legal Understanding in Palestine](https://mlsanity.com/paper/2412.14771/) (2024)
- [Lorecast: Layout-Aware Performance and Power Forecasting from Natural Language](https://mlsanity.com/paper/2503.11662/) (2025)
- [DALI-PD: Diffusion-based Synthetic Layout Heatmap Generation for ML in Physical Design](https://mlsanity.com/paper/2507.10606/) (2025)
- [SpecRead: A Benchmark for Measuring Whether Language Models Understand Hardware Specifications](https://mlsanity.com/paper/2609.33699/) (2026)
- [LATCH: LLM-Assisted Timing Closure Harness for Post-Route FPGA Optimisation](https://mlsanity.com/paper/2610.16625/) (2026)

## Abstract (arXiv)

Large language models (LLMs) serve as powerful tools for design, providing capabilities for both task automation and design assistance. Recent advancements have shown tremendous potential for facilitating LLM integration into the chip design process; however, many of these works rely on data that are not publicly available and/or not permissively licensed for use in LLM training and distribution. In this paper, we present a solution aimed at bridging this gap by introducing an open-source dataset tailored for OpenROAD, a widely adopted open-source EDA toolchain. The dataset features over 1000 data points and is structured in two formats: (i) a pairwise set comprised of question prompts with prose answers, and (ii) a pairwise set comprised of code prompts and their corresponding OpenROAD scripts. By providing this dataset, we aim to facilitate LLM-focused research within the EDA domain. The dataset is available at https://github.com/OpenROAD-Assistant/EDA-Corpus.