Title: Low-Resource Finetuning for Hallucination Mitigation in Language Models
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: LLM, hallucination, mitigation, fine-tuning, LettuceDetect

Abstract: Hallucinations in Large Language Models (LLMs) pose a significant challenge to their reliable deployment across domains, arising inherently from their design as statistical models that maximize next-token prediction probability based on training data. While methods such as LettuceDetect, RAG-HAT, and prompting techniques have demonstrated efficacy in hallucination detection and mitigation within Retrieval-Augmented Generation (RAG) frameworks, limitations persist. To address these, we propose a novel low-resource hallucination mitigation pipeline that fine-tunes LLMs on synthetic dataset using feedback from LettuceDetect. Our approach reduces hallucination rates in open-source small language models, as validated through evaluations on RAGTruth and PILE-10K benchmarks. We further discuss the pipeline’s extensibility to domain-specific applications.

TL;DR: Novel hallucination mitigation fine-tuning pipeline on synthetic dataset using feedback from LettuceDetect.