Automated fact-checking systems built on large language model agents face persistent difficulties in retrieving relevant evidence and verifying claims, according to a new arXiv preprint. The abstract notes that current attempts to address these shortcomings tend to rely on computationally expensive techniques, which limits their practicality.
The paper, titled "Standing on the Shoulders of Giants," suggests an alternative direction: enhancing LLM agents with skills derived from human expert fact-checkers. Rather than adding more computation, the approach appears to encode expert knowledge into the agent's workflow.
The provided abstract is truncated, so specific methods and experimental results are not available in this excerpt. The significance lies in the potential to improve fact-checking accuracy without escalating computational demands, though the full paper would be needed to assess the evidence.