Finding 2667Emerging EvidenceValidation V0
The study reveals substantial temporal biases in LLM performance, with models excelling on post-1995 data following the SECs EDGAR filing system yet struggling and hallucinating details for older financial information.
78%Confidence
1Evidence objects
v1Version
DraftStatus
Evidence trail
Supporting78% linkage confidence
The study reveals substantial temporal biases in LLM performance, with models excelling on post-1995 data following the SECs EDGAR filing system yet struggling and hallucinating details for older financial information.
key_findings bullet 1 · key_findings
Inspect source: Beyond the Reported Cutoff: Where Large Language Models Fall Short on Financial Knowledge →This Finding was extracted from the configured corpus. It is versioned, traceable, and may evolve through editorial review or new corpus evidence.