Summary

• The paper discusses the concept of an effective cutoff, which is different from the LLM designer reported cutoff and is applied to individual sub-resources and topics within LLMs.
• A method is proposed to estimate these cutoffs by probing across versions of the data, revealing how effective cutoffs often deviate from reported ones.
• Due to temporal biases from data sources and challenges in deduplication, the effective cutoffs vary significantly.
• The analysis emphasizes the importance of adhering to effective cutoff dates for applications relying on up-to-date information from LLMs.

Importance: This research highlights critical oversight in the reported knowledge cutoffs of LLMs and proposes a methodology to better manage and understand these cutoffs. Such insights are essential for improving the reliability of LLM applications in dynamic environments.