Hallucination Mitigation in Large Language Models: A Review and Exploratory Small-Model Pilot
Abstract
Large language models (LLMs) can produce fluent responses that are false, unsupported, outdated, or more certain than the available evidence justifies. This manuscript presents a narrative review of major hallucination-mitigation approaches and an exploratory pilot intended to refine a future evaluation protocol rather than establish model-level performance. The review covers false-premise handling, abstention and knowledge-boundary estimation, retrieval-augmented generation, grounding, verification, self-checking, and multimodal hallucination. It also distinguishes published evidence from an engineering proposal for failure-driven correction. The pilot used a locally executed interface labeled Gemma 3 1B-IT and ten hand-designed stress prompts spanning false premises, insufficient evidence, fabricated-citation traps, unknowable events, and future-value requests. The revised analysis deliberately avoids compressing these observations into a single hallucination score. Two responses were epistemically appropriate, three were partially appropriate but introduced unsupported content, and five failed the target behavior. Generation stability was tracked separately: one otherwise appropriate answer entered a repetition loop, and another item required a restart after a context-length error. An extended follow-up run was terminated when the local system became unstable and began returning empty outputs; those observations are reported only qualitatively. The pilot is too small and…
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