Face the Facts! Evaluating RAG-based Fact-checking Pipelines in Realistic Settings

• URL: http://arxiv.org/abs/2412.15189v1
• Date: Thu, 19 Dec 2024 18:57:11 GMT
• Title: Face the Facts! Evaluating RAG-based Fact-checking Pipelines in Realistic Settings
• Authors: Daniel Russo, Stefano Menini, Jacopo Staiano, Marco Guerini,
• Abstract summary: This work lifts several constraints of current state-of-the-art pipelines for automated fact-checking based on the Retrieval-Augmented Generation paradigm.<n>Our goal is to benchmark, under more realistic scenarios, RAG-based methods for the generation of verdicts.
• Score: 14.355271969637139
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Natural Language Processing and Generation systems have recently shown the potential to complement and streamline the costly and time-consuming job of professional fact-checkers. In this work, we lift several constraints of current state-of-the-art pipelines for automated fact-checking based on the Retrieval-Augmented Generation (RAG) paradigm. Our goal is to benchmark, under more realistic scenarios, RAG-based methods for the generation of verdicts - i.e., short texts discussing the veracity of a claim - evaluating them on stylistically complex claims and heterogeneous, yet reliable, knowledge bases. Our findings show a complex landscape, where, for example, LLM-based retrievers outperform other retrieval techniques, though they still struggle with heterogeneous knowledge bases; larger models excel in verdict faithfulness, while smaller models provide better context adherence, with human evaluations favouring zero-shot and one-shot approaches for informativeness, and fine-tuned models for emotional alignment.

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