# Assessing Large Language Models in Generating RTL Design Specifications

arXiv: https://arxiv.org/abs/2512.00045 · HTML: https://mlsanity.com/paper/2512.00045/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 17 Nov 2025.

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

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications](https://mlsanity.com/paper/2402.07927/) (2024)
- [VerilogEval: Evaluating Large Language Models for Verilog Code Generation](https://mlsanity.com/paper/2309.07544/) (2023)
- [On the Evaluation of Neural Code Summarization](https://mlsanity.com/paper/2107.07112/) (2021)
- [Revisiting VerilogEval: A Year of Improvements in Large-Language Models for Hardware Code Generation](https://mlsanity.com/paper/2408.11053/) (2024)
- [MAGE: A Multi-Agent Engine for Automated RTL Code Generation](https://mlsanity.com/paper/2412.07822/) (2024)
- [DeepRTL: Bridging Verilog Understanding and Generation with a Unified Representation Model](https://mlsanity.com/paper/2502.15832/) (2025)
- [Evaluating Code Summarization Techniques: A New Metric and an Empirical Characterization](https://mlsanity.com/paper/2312.15475/) (2023)
- [Semantic Similarity Metrics for Evaluating Source Code Summarization](https://mlsanity.com/paper/2204.01632/) (2022)
- [DeepRTL2: A Versatile Model for RTL-Related Tasks](https://mlsanity.com/paper/2506.15697/) (2025)
- [Automated and Context-Aware Code Documentation Leveraging Advanced LLMs](https://mlsanity.com/paper/2509.14273/) (2025)

## Built on by (most cited first, 2 of 2)

- [Exploring the Agentic Frontier of Verilog Code Generation](https://mlsanity.com/paper/2603.19347/) (2026)
- [RTL-BenchLS: A Large-Scale Benchmark for RTL Reasoning and Generation with Large Language Models](https://mlsanity.com/paper/2606.08976/) (2026)

## Abstract (arXiv)

As IC design grows more complex, automating comprehension and documentation of RTL code has become increasingly important. Engineers currently should manually interpret existing RTL code and write specifications, a slow and error-prone process. Although LLMs have been studied for generating RTL from specifications, automated specification generation remains underexplored, largely due to the lack of reliable evaluation methods. To address this gap, we investigate how prompting strategies affect RTL-to-specification quality and introduce metrics for faithfully evaluating generated specs. We also benchmark open-source and commercial LLMs, providing a foundation for more automated and efficient specification workflows in IC design.