A Metric-Aware Analysis of Trigger-Guided Adapter Training for Hallucination Mitigation
Large language models can produce fluent responses that are unsupported by the provided evidence or inconsistent with reference answers. Adapter-based fine-tuning offers a practical way to modify model behavior without updating all model parameters, but its effect on hallucination mitigation depends strongly on how improvement is measured. This paper presents TruthShield, a metric-aware trigger-guided QLoRA adapter training and evaluation pipeline for hallucination-aware language model adaptation. We construct hallucination-related failure cases from baseline evaluations, assign rule-based failure labels, generate counterexample triggers, and train a Mistral-7B adapter to respond to these trigger patterns. We then compare the baseline model and the adapter across TruthfulQA generation, TruthfulQA multiple choice, and HaluEval using two evaluation views: strict matching and an LLM judge. The adapter substantially improves strict scores across all tasks, increasing strict accuracy by 1.0000 on both TruthfulQA settings and by 0.6957 on HaluEval. However, these gains do not translate into judge-based improvement: judge scores remain nearly unchanged for TruthfulQA generation and decrease for TruthfulQA multiple choice and HaluEval. The results suggest that trigger-guided adapter training may learn surface-level response patterns without clear evidence of semantic hallucination mitigation under our current single-judge setting. This finding highlights the risk of overestimating reliability gains when strict metrics are used without complementary semantic evaluation.