Large language models can learn to interpret tokens differently based on their contextual usage and subword segmentation.

LLM breakdown 1/6: Tokenization (words to integers) hypothes.is/a/M51uijyJEfGhrPMwpVpnwg Through training, the models eventually learn that tokens 2339 and 588 can be conceptually identical, or they can have distinct meanings if “like” is a subwords (e.g., unlike vs. alike vs. businesslike).Thats cool how tokens can be different depending on…

LLM breakdown 1/6: Tokenization (words to integers) hypothes.is/a/M51uijyJEfGhrPMwpVpnwg Through training, the models eventually learn that tokens 2339 and 588 can be conceptually identical, or they can have distinct meanings if “like” is a subwords (e.g., unlike vs. alike vs. businesslike).Thats cool how tokens can be different depending on…