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Evaluating LLM Recommendation Safety under Hard Constraints

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2026, 31(10), pp.107~113
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : September 8, 2026
  • Accepted : October 12, 2026
  • Published : October 30, 2026

Tae-Yeon Eom 1,  Jae-Woong Kim 1,  Won-Young Jeon 1

1국립공주대학교

Accredited

ABSTRACT

This study compared final-action performance and rejection feedback conditions across four hard-constraint product recommendation procedures using a single local LLM (qwen3:8b). A fixed 40-monitor catalog, based on official manufacturer specifications and domestic prices, mapped 19 hard constraints to 46 product fields. With deterministic ground truth precomputed for 60 queries, we compared single pass (M1), self-refinement (M2), and Proposer–Critic with rejected-product-ID feedback (M3) or detailed rejection history containing violated constraint keys, required values, actual values, and natural-language reasons (M4). Under fixed evaluation conditions, final-action accuracy on REJECTION_STRESS was 40.0% (M1), 46.7% (M2), and 93.3% (M3 and M4). Both M3 and M4 recorded zero unsafe acceptances and correctly abstained on all 10 infeasible queries. Repeated same-constraint violation rates were 61.11% (M3) and 57.14% (M4), with a difference (M4−M3) of −3.97 percentage points (95% confidence interval: [−10.77, 0.00] percentage points). M4 showed a lower observed repeated-violation rate, although the confidence interval included zero, and slightly higher input-token use and response latency than M3.

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