Effectiveness of Sparse Retrieval for Automated Fact-Checking

Abstract

As fact-checking pipelines grow increasingly complex, a critical question remains unanswered: does retrieval matter more than reasoning? We investigate this question on the AVeriTeC 2.0 Shared Task, a leading and highly competitive benchmark for automated fact-checking that requires systems to verify real-world claims against open-domain web evidence. On this benchmark, we compare dense retrieval (FAISS), learned sparse retrieval (SPLADE), late interaction (ColBERT), and contextual embeddings across two architecturally distinct systems. SPLADE consistently outperforms all alternatives, improving new AVeriTeC scores by up to 86% on the development set and up to 27% on the hidden test set, where it enables a lower-ranked system to claim the top leaderboard position. Its advantage stems from vocabulary-space representations that bridge lexical gaps, preserve fine-grained numerical and entity matches, and remain robust to noisy web evidence, capabilities that are difficult to achieve with dense retrieval without additional techniques such as query expansion, reranking, or entity-aware training. Strikingly, two systems of vastly different complexity, one consolidating all reasoning into a single LLM call and the other orchestrating five models across seven tasks, converge to virtually identical performance once equipped with SPLADE. This demonstrates that retrieval quality is a major bottleneck in these systems. Beyond accuracy, SPLADE reduces preprocessing time by an order of magnitude while allowing both pipelines to share a single index. Our findings establish learned sparse retrieval as a highly impactful design choice in automated fact-checking, with retrieval quality serving as a critical determinant of end-to-end performance in the studied systems.