Provenance: A Light-weight Fact-checker for Retrieval Augmented LLM Generation Output

• URL: http://arxiv.org/abs/2411.01022v1
• Date: Fri, 01 Nov 2024 20:44:59 GMT
• Title: Provenance: A Light-weight Fact-checker for Retrieval Augmented LLM Generation Output
• Authors: Hithesh Sankararaman, Mohammed Nasheed Yasin, Tanner Sorensen, Alessandro Di Bari, Andreas Stolcke,
• Abstract summary: We present a light-weight approach for detecting nonfactual outputs from retrieval-augmented generation (RAG) We compute a factuality score that can be thresholded to yield a binary decision. Our experiments show high area under the ROC curve (AUC) across a wide range of relevant open source datasets.
• Score: 49.893971654861424
• License:
• Abstract: We present a light-weight approach for detecting nonfactual outputs from retrieval-augmented generation (RAG). Given a context and putative output, we compute a factuality score that can be thresholded to yield a binary decision to check the results of LLM-based question-answering, summarization, or other systems. Unlike factuality checkers that themselves rely on LLMs, we use compact, open-source natural language inference (NLI) models that yield a freely accessible solution with low latency and low cost at run-time, and no need for LLM fine-tuning. The approach also enables downstream mitigation and correction of hallucinations, by tracing them back to specific context chunks. Our experiments show high area under the ROC curve (AUC) across a wide range of relevant open source datasets, indicating the effectiveness of our method for fact-checking RAG output.

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