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LLMs are auto-regressive predictors -- so they take the text given to them (i.e., the prompt) and generate a probability estimate for what the next token should be. Suppose you gave it a quote -- "Once upon a midnight dreary, while I pondered, " and ask it to keep writing, it will generate a probability distribution across various tokens it has been trained on. I'll use words here, rather than tokens, to make the point... Hypothetically, for the quote above, the LLM might estimate the probability of the next word being... "Weak" = 0.80 "Tired" = 0.10 "Slothful" = 0.05 ... and so on. Now, if you are using a temperature of 0.0, the LLM will pick the highest probability word/token. It's possible you had a non-zero temperature setting and the LLM "knew" the right answer but randomly picked the wrong one... Temperature basically randomizes the token choice to make it more diverse/creative/better. Alternatively, based on the prompt (i.e., collection of text you put in), it estimated the probability of the wrong answer to be higher. In your case, the LLM likely had a high probability for "Edgar Allan Poe" but maybe a lower probability for the specific works/titles, and hence chose incorrectly. P.S. If you are using the OpenAI playground, you can actually get the probability estimates, if you want to investigate further! |
The idea of an LLM makes sense: it's ultimately a graph with statistical weights for which node we use to generate text next (my understanding here is still correct, yes?).
My issue is, where does the "learning" part come in? It feels like it's all hard-coded but I know it's not. What allows for flexibility in token generation besides that randomization from temperature that you mentioned?