When retrieval outperforms generation: Dense evidence retrieval for scalable fake news detection

• URL: http://arxiv.org/abs/2511.04643v1
• Date: Thu, 06 Nov 2025 18:35:45 GMT
• Title: When retrieval outperforms generation: Dense evidence retrieval for scalable fake news detection
• Authors: Alamgir Munir Qazi, John P. McCrae, Jamal Abdul Nasir,
• Abstract summary: DeReC is a lightweight framework that demonstrates how general-purpose text embeddings can effectively replace autoregressive LLM-based approaches in fact verification tasks.<n>By combining dense retrieval with specialized classification, our system achieves better accuracy while being significantly more efficient.<n>Our results demonstrate that carefully engineered retrieval-based systems can match or exceed LLM performance in specialized tasks.
• Score: 1.329253775274691
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The proliferation of misinformation necessitates robust yet computationally efficient fact verification systems. While current state-of-the-art approaches leverage Large Language Models (LLMs) for generating explanatory rationales, these methods face significant computational barriers and hallucination risks in real-world deployments. We present DeReC (Dense Retrieval Classification), a lightweight framework that demonstrates how general-purpose text embeddings can effectively replace autoregressive LLM-based approaches in fact verification tasks. By combining dense retrieval with specialized classification, our system achieves better accuracy while being significantly more efficient. DeReC outperforms explanation-generating LLMs in efficiency, reducing runtime by 95% on RAWFC (23 minutes 36 seconds compared to 454 minutes 12 seconds) and by 92% on LIAR-RAW (134 minutes 14 seconds compared to 1692 minutes 23 seconds), showcasing its effectiveness across varying dataset sizes. On the RAWFC dataset, DeReC achieves an F1 score of 65.58%, surpassing the state-of-the-art method L-Defense (61.20%). Our results demonstrate that carefully engineered retrieval-based systems can match or exceed LLM performance in specialized tasks while being significantly more practical for real-world deployment.

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