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

🕒 Published (v1): 2026-07-19 04:12 UTC · Source: Arxiv · link

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TL;DR

LLMs answering hardware description language (HDL) questions exhibit a pervasive "over-answering" tendency—producing correct content buried under redundant alternative solutions (65.7%) and verbose non-core padding (69.1%). The authors empirically quantify this gap via a 19-engineer user study on 6,246 Stack Overflow HDL Q&A pairs, then propose a task-aware multi-agent refinement framework that reduces core-answer count by 37% and non-core length by 31% while raising LLM-judge quality scores.

Problem

Existing HDL-LLM evaluations focus on functional correctness (simulation pass/fail, synthesizability) via benchmarks like VerilogEval and RTLLM, leaving communicative quality dimensions—redundancy, conciseness, and alignment with expert practice—unmeasured. Because HDL synthesizes into physical hardware, imprecise or over-verbose answers can propagate timing violations or non-synthesizable logic late in the design flow. No systematic dataset or framework addresses the gap between LLM-generated and human-expert HDL answers along these pragmatic dimensions.

Method

Dataset construction: 6,246 Stack Overflow HDL posts (Verilog/SystemVerilog 53.4%, VHDL 43.2%) with accepted answers are classified into 4 main × 10 sub-categories using DeepSeek-V4 as classifier (93.94% category-level accuracy on 363 stratified human-verified samples, Wilson CI [91.0%, 96.0%]).

User study: 19 engineers (1–3 years HDL experience) evaluated 363 questions on four dimensions—redundancy, conciseness, consistency with accepted answer, readability—using blinded A/B presentation (randomized order). Inter-annotator agreement: macro-average 91.5%, \(\kappa = 0.84\).

Multi-agent framework (3 stages): - M1 — Task-aware Generation: A profiling agent infers task type \(\hat{t} \in \{\text{Conceptual, Debugging, Generation, Optimization}\}\), user intent \(u\), and verification needs \(v\); produces draft \(a_0 = M_1(q, \hat{t}, u, v)\). - M2 — Task-aware Refinement: (M2.1) Decomposes draft into core answers \(\{c_1,\ldots,c_n\}\) and non-core units \(\{e_1,\ldots,e_m\}\). (M2.2) A three-role debate panel (Proposer, Challenger, Reflector) jointly evaluates all core candidates against four criteria (semantic correctness, code validity, functional alignment, quality standards), eliminating redundant/derivative candidates. (M2.3) A task-specific compressor trims non-core content according to category policy. - M3 — Dual-view Evaluation: LLM-as-Judge scores core and non-core quality on a 5-point scale; an accepted-answer consistency check measures coverage overlap.

Evaluated on four LLMs: Qwen3.5, DeepSeek-V4, GPT-5.4, Gemini-3.1 (zero-shot baselines).

Key Contributions

  • Empirical characterization of LLM over-answering in HDL Q&A via a 19-engineer user study on 363 questions, with inter-annotator agreement \(\kappa = 0.84\).
  • Taxonomy of 4 main / 10 sub-categories for real-world HDL questions, with LLM-based annotation achieving >92% subcategory accuracy.
  • Task-aware multi-agent framework with multi-role debate for core-answer deduplication and category-specific compression for non-core verbosity.
  • Public dataset of 6,246 HDL Q&A pairs with accepted answers and framework source code.

Results

  • Core-answer quality score: \(3.71 \to 4.67\) (+0.96 on 5-point scale), averaged across 4 backbone LLMs and 4 task categories.
  • Non-core content quality score: \(3.72 \to 4.23\) (+0.51 on 5-point scale).
  • Number of core answers reduced by 37% on average (addresses redundancy).
  • Non-core content length reduced by 31% on average (addresses verbosity).
  • Gains hold across all four backbone models and all four task categories.
  • Consistency with accepted answers is preserved or slightly improved after refinement (no substantive content removed).
  • Baselines: the unrefined LLM outputs (zero-shot) from Qwen3.5, DeepSeek-V4, GPT-5.4, Gemini-3.1.

Limitations

  • User study uses single annotation per question; reliability is supported by \(\kappa = 0.84\) on a subset but not per-item majority vote.
  • Evaluation relies on LLM-as-Judge for quantitative scoring, which may itself be biased toward verbosity or a particular style.
  • Framework adds multi-stage inference cost (three sequential agentic modules) not quantified in the paper.
  • Consistency metric measures overlap with the Stack Overflow accepted answer, which need not be the uniquely correct solution.
  • Dataset is English-only from Stack Overflow; HDL Q&A in other languages or on internal forums is out of scope.
  • Security-optimization subcategory is small (125 instances), limiting subcategory-level generalization.

Relevance to Harnesses / Meta-Harnesses

This paper instantiates a three-stage agentic meta-harness where each module (task profiling, debate-based refinement, dual-view evaluation) is itself an LLM agent, and their composition is driven by empirically measured failure modes rather than intuition—a pattern directly relevant to harness design methodology. The multi-role debate mechanism (Proposer / Challenger / Reflector) is a reusable coordination primitive for output-consolidation harnesses where parallel LLM proposals must be pruned to a minimal, non-redundant set. The LLM-as-Judge + accepted-answer consistency dual evaluation pipeline illustrates how meta-harnesses can embed quality gates with heterogeneous signal sources. Researchers building harnesses for domain-specific code generation (RTL, SQL, shell scripts) can directly adopt the decompose-then-debate architecture and the category-conditioned compression policy.