When LLMs Over-Answer: Measuring and Mitigating Quality Issues in LLM-Based Hardware Description Language Question Answering

2026-07-19

AI Summary

This study addresses the prevalent issue of over-generation in large language models (LLMs) when answering hardware description language (HDL) questions, which often results in verbose, redundant outputs that deviate from expert practices and may introduce critical design errors. The work presents the first systematic evaluation of this problem by constructing a dataset of 6,246 HDL-related question-answer pairs and introducing structured metrics based on the number of core answers and the length of non-core content. To enhance answer precision and conciseness, the authors propose a multi-agent collaborative framework. Experimental results across four major LLMs demonstrate significant improvements: core answer quality increases from 3.71 to 4.67, and non-core content quality rises from 3.72 to 4.23 on a five-point scale, effectively mitigating redundancy.

Abstract

The rapid advancement of large language models (LLMs) has led practitioners to increasingly rely on them for answering questions about hardware description languages (HDLs). Because HDL is ultimately synthesized into physical hardware, an imprecise or redundant answer can propagate into timing violations or non-synthesizable logic that surface only late in the design flow, making the quality of HDL answers especially consequential. However, the quality of LLM-generated responses, particularly in comparison with answers provided by human experts, remains unclear. To investigate this question, we collect 6,246 HDL Q&A posts with accepted answers from Stack Overflow and curate them into a dataset, organized into a taxonomy of four main categories (Conceptual, Debugging, Generation, and Optimization) and ten subcategories. Using this dataset, we design a user study conducted with 19 HDL engineers with one to three years of experience. Our findings reveal a pervasive over answering tendency: LLMs supply correct content but bury it under redundant alternatives (65.7%) and verbose padding (69.1%), while nearly half of answers (49.0%) fail to fully align with expert answers yet participants still preferred LLM responses for readability (58.3%). Motivated by these findings, we propose a multi-agent framework for improving LLM-based HDL question answering. We evaluate answer quality using an LLM-as-Judge and two structural metrics: the number of core answers, which reflects redundancy since LLMs often provide multiple alternative solutions, and the length of non-core content, which reflects verbosity. Evaluated on the four mainstream LLMs, our framework increases the average core-answer quality score from 3.71 to 4.67 (+0.96) and the non-core content quality from 3.72 to 4.23 (+0.51), on a five-point scale.

Problem

• over-answering
• hardware description language
• answer quality
• large language models
• redundancy

Innovation

• over-answering
• hardware description language (HDL)
• multi-agent framework
• answer quality evaluation
• LLM-as-Judge