Context Shapes LLMs Retrieval-Augmented Fact-Checking Effectiveness

• URL: http://arxiv.org/abs/2602.14044v2
• Date: Mon, 23 Feb 2026 22:32:36 GMT
• Title: Context Shapes LLMs Retrieval-Augmented Fact-Checking Effectiveness
• Authors: Pietro Bernardelle, Stefano Civelli, Kevin Roitero, Gianluca Demartini,
• Abstract summary: We show that large language models (LLMs) show strong reasoning abilities across diverse tasks, yet their performance on extended contexts remains inconsistent.<n>We evaluate both factual knowledge and the impact of evidence placement across varying context lengths.
• Score: 6.250095470690937
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) show strong reasoning abilities across diverse tasks, yet their performance on extended contexts remains inconsistent. While prior research has emphasized mid-context degradation in question answering, this study examines the impact of context in LLM-based fact verification. Using three datasets (HOVER, FEVEROUS, and ClimateFEVER) and five open-source models accross different parameters sizes (7B, 32B and 70B parameters) and model families (Llama-3.1, Qwen2.5 and Qwen3), we evaluate both parametric factual knowledge and the impact of evidence placement across varying context lengths. We find that LLMs exhibit non-trivial parametric knowledge of factual claims and that their verification accuracy generally declines as context length increases. Similarly to what has been shown in previous works, in-context evidence placement plays a critical role with accuracy being consistently higher when relevant evidence appears near the beginning or end of the prompt and lower when placed mid-context. These results underscore the importance of prompt structure in retrieval-augmented fact-checking systems.

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