A novel agent-based framework called FIRE is introduced for fact-checking long-form text. FIRE iteratively integrates evidence retrieval and claim verification, deciding whether to provide a final answer or generate a subsequent search query. Experiments show FIRE achieves comparable performance to existing methods while reducing LLM costs by 7.6x and search costs by 16.5x.
MBZUAI researchers presented FIRE, a new fact-checking framework for LLM outputs, at NAACL 2025. FIRE first assesses the LLM's confidence in its claims before searching the web, reducing computational cost. It also stores knowledge gained from web searches to aid in classifying other claims. Why it matters: This approach improves the efficiency and cost-effectiveness of automatically verifying the accuracy of LLMs, addressing a key limitation in their reliability.
This paper introduces ProgramFC, a fact-checking model that decomposes complex claims into simpler sub-tasks using a library of functions. The model uses LLMs to generate reasoning programs and executes them by delegating sub-tasks, enhancing explainability and data efficiency. Experiments on fact-checking datasets demonstrate ProgramFC's superior performance compared to baseline methods, with publicly available code and data.