When retrieval outperforms generation: Dense evidence retrieval for scalable fake news detection (2025)

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.

## introduction the exponential growth of misinformation across digital platforms presents an urgent challenge to information integrity and societal discourse @xcite . while recent advances in automated fact-verification systems have shown promise in addressing this challenge @xcite @xcite , current approaches face significant limitations in both computational efficiency and verification reliability @xcite . recent work in automated fact-checking and in particular, state-of-the-art systems heavily rely on llms to generate natural language explanations that justify verification decisions @xcite @xcite . while these approaches have demonstrated impressive capabilities in reasoning about complex claims, they face three critical challenges: the computational demands of running inference with large models make real-time fact checking impractical @xcite . llm-generated explanations frequently contain hallucinations or factual inconsistencies that compromise verification reliability @xcite , and the generated rationales often lack direct grounding in verifiable evidence sources @xcite @xcite . such limitations motivate the development of alternative strategies that prioritize both efficiency and transparency. in this work, we introduce derec (dense retrieval classification), an evidence-enhanced hybrid framework that directly incorporates retrieved textual evidence into the fact-checking process. derec leverages sentence embeddings and facebook ai similarity search (faiss) @xcite to extract pertinent evidence from source documents, which is then integrated with the claim to form a robust input for downstream classification. by grounding predictions in actual evidence, we achieve both improved verification accuracy while significantly reducing computational overhead compared to llm-based approaches. unlike traditional retrieval-augmented generation (rag) systems that use retrieved content to enhance llm prompts, derec directly grounds verification decisions in relevant evidence through efficient similarity search and targeted classification. our experimental results demonstrate that this evidence-centric approach exceeds the performance of more complex llm-based systems, while maintaining faster inference times and lower evidence extraction evidence retrieval veracity prediction relevant report sentences sentence embeddings build search index claim query embedding similarity search top-k relevant evidence prompt to encoder classification figure 1: derec: three-stage pipeline for evidence-based fact verification. resource requirements. the code is available publicly. foot_0 our contribution can be summarized as follows: 1. we propose derec, a light-weight denseretrieval-classification framework that combines advanced text embeddings with a specialized classifier to directly ground claims in factual evidence, achieving high verification accuracy without llm-based rationale generation. 2. we demonstrate that general-purpose text embeddings combined with dense retrieval can effectively replace llm-based approaches in specialized tasks like fact verification, achieving better accuracy with significantly lower computational overhead (1.5b/137m parameters vs typical 7b+ llm approaches). 3. empirical evaluations reveal state-of-the-art results on two datasets with an f1 score of 65.58% on rawfc and 33.13% on liar-raw. ## method and overall architecture we present an integrated retrieval and classification architecture for automated fact verification that improves upon existing llm-based methods. our framework consists of three key components: evidence extraction using dense embeddings, evidence retrieval through faiss-based similarity search, and veracity prediction using a specialized classifier. ## experiments and results our framework achieves state-of-the-art results on both liar-raw and rawfc benchmarks, outperforming all baseline models in terms of f1 score, precision and recall. ## discussion our experimental results reveal several key insights about the relationship between evidence retrieval and fact verification. the performance improvements achieved by our hybrid architecture suggest important implications for future development of automated fact-checking systems. the memory footprint differential between these approaches is substantial. llm-based methods must maintain the full model parameters in gpu memory while also allocating space for attention computations that scale quadratically with sequence length. additionally, these models require kv-cache memory for generation @xcite . traditional approaches utilizing llms such as chatgpt (175b+ parameters) or llama2 (7b parameters) for explanation generation face significant computational challenges. the fundamental bottleneck lies in the autoregressive nature of text generation, which necessitates sequential processing with quadratic complexity o(n 2 ) for generating n tokens. these models require substantial gpu memory allocation due to their massive parameter counts. moreover, methods like l-defense require multiple llm calls per claim to generate competing explanations, further amplifying the computational overhead. our retrieval-based approach fundamentally refactors this paradigm by eliminating the need for explanation generation entirely. the architecture employs a significantly smaller embedding model (nomic-ai/nomic-embed-text-v1.5, 137m) requires only 0.5gb in fp32 precision which still beats most benchmarks while a slightly bigger model (alibaba-nlp/gte-qwen2-1.5b-instruct, 1.5b parameters) requires only 6gb in fp32 precision. this model performs single-pass encoding with linear complexity o(n), followed by efficient faissbased similarity search with sub-linear complexity o(log k) for k evidence sentences. the final classification step utilizes a lightweight deberta-v3-large classifier (304m parameters) that requires only a single forward pass. ## conclusion we present a hybrid retrieval-classification framework for fact verification that achieves state-of-theart performance on the liar-raw and rawfc benchmarks. our approach demonstrates that carefully engineered dense retrieval systems can match or exceed the performance of llms while significantly reducing computational overhead. the empirical results show that derec achieves a 95% reduction in runtime while improving accuracy, challenging the assumption that llm-based generation is necessary for effective fact verification. our findings have several important implications for the field of automated fact-checking. first, they demonstrate that efficient dense embeddings combined with targeted classification can effectively replace more complex llm-based approaches in specialized tasks. second, the dramatic reduction in computational requirements (from 7b+ parameters to 137m-1.5b) makes real-time fact verification more practically feasible for deployment in resource-constrained environments. third, our results suggest that explicit rationale generation, while interpretable, may not be necessary for achieving high verification accuracy. the modular nature of our architecture enables straightforward incorporation of improved embedding models as they become available. our results suggest several promising research directions: investigating methods for dynamic evidence corpus updates, exploring techniques for handling multilingual verification scenarios, and developing lightweight explanation generation methods that maintain both computational efficiency and interpretability. these findings contribute to the broader discussion about the role of large language models in practical applications, suggesting that targeted, efficient approaches may often be preferable to more computationally intensive general-purpose models. as misinformation continues to pose significant challenges to online discourse, frameworks like derec demonstrate how we can build more scalable and efficient solutions for automated fact verification.