LLMs Over-Answer HDL Questions, New Framework Improves Quality.

Research into Large Language Models (LLMs) answering questions about Hardware Description Languages (HDLs) has revealed a significant quality issue: LLMs frequently provide verbose and redundant responses. While human experts preferred LLM answers for readability, nearly half of these answers did not fully align with expert responses, often burying correct information under excessive alternatives and padding. This "over-answering" tendency is particularly problematic for HDL, as imprecise or redundant logic can lead to critical errors in physical hardware design, surfacing late in the development cycle. To investigate this, a dataset of 6,246 HDL Q&A posts from Stack Overflow was curated and categorized. A user study involving 19 HDL engineers confirmed that LLMs exhibited 65.7% redundancy and 69.1% verbosity. Motivated by these findings, the researchers developed a multi-agent framework designed to enhance the quality of LLM-based HDL question answering. This framework was evaluated using an LLM-as-Judge and structural metrics like the number of core answers and the length of non-core content. Across four mainstream LLMs, the framework substantially improved core-answer quality scores by 0.96 points and non-core content quality by 0.51 points on a five-point scale. This demonstrates a practical method for mitigating the verbosity and redundancy issues in LLM-generated technical responses.