Gosh. Can somebody help me to understand how a LLM has achieved this capability.
I had thought that a LLM was essentially only doing completions under the hood using statistical likelihood of next words, wrapped in some lexical sugar/clever prompt mods. But evidently far more is going on here since, in OP’s example, some of its output (eg future time stamps) will not be present in its training data. Even with several billion parameters that seems impossible to me. (Clearly not!)
Could somebody join the dots for me on the nature of whatever framework the LLM is embedded inside that allows it to achieve these behaviours that emulate logic and allow generation of unique content.
I had thought that a LLM was essentially only doing completions under the hood using statistical likelihood of next words, wrapped in some lexical sugar/clever prompt mods. But evidently far more is going on here since, in OP’s example, some of its output (eg future time stamps) will not be present in its training data. Even with several billion parameters that seems impossible to me. (Clearly not!)
Could somebody join the dots for me on the nature of whatever framework the LLM is embedded inside that allows it to achieve these behaviours that emulate logic and allow generation of unique content.