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

2025-11-06

AI Summary

To address the proliferation of misinformation, existing large language model (LLM)-based fact-checking approaches suffer from high computational overhead, severe hallucination risks, and poor deployability. This paper proposes DeReC, a lightweight and efficient fact verification framework that pioneers the integration of general-purpose text embeddings with dense retrieval—replacing LLM-based generative reasoning—and introduces a dedicated classifier for end-to-end verification. By preserving semantic understanding while eliminating autoregressive generation, DeReC significantly reduces computational cost. Experiments show that DeReC achieves an F1 score of 65.58% on RAWFC, outperforming the state-of-the-art L-Defense (61.20%) and accelerating inference by 20× (95% runtime reduction); on LIAR-RAW, it achieves 12× speedup (92% reduction). This work is the first to empirically validate the superiority of non-generative dense retrieval for fact-checking, establishing a new paradigm for scalable, low-cost, and robust verification systems.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Detecting fake news efficiently with dense evidence retrieval
• Replacing LLM-based generation to reduce computational barriers
• Improving fact verification accuracy while enhancing runtime efficiency

Innovation

• Dense retrieval replaces autoregressive LLM generation
• Combines specialized classification with evidence retrieval
• Lightweight framework using general-purpose text embeddings