Ah, the knowledge cutoff date, that elusive timestamp indicating when an LLM last had its intelligence upgrade. Or is it? This paper takes a Sherlock Holmes approach to scrutinize the proclaimed knowledge cutoffs and introduces an original concept of an ‘effective cutoff’. The quest? To probe the forgotten lands of sub-resources and unearth their actual temporal alignment with the LLM’s brain.

• Effective cutoffs often don’t RSVP to the same date as reported cutoffs.
• A magnifying glass over CommonCrawl data uncovers oldies in new dumps, leading to temporal biases.
• The deduplication shenanigans of LLMs have their own share of drama with lexical doppelgängers muddling knowledge cutoffs.
• LLM gurus and users must navigate this foggy timeline with care to reap the right wisdom.

The esoteric nature of knowledge timelines in LLMs can be daunting, but this article heralds a new dawn by urging meticulous diligence in dataset curation and utilization. Grab your deerstalker and investigate further.