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Many classic statistical modelling techniques have ways to produce some measure of confidence for their predictions; perhaps LLMs could incorporate that as well
by RyEgswuCsn 3y ago
Many classic statistical modelling techniques have ways to produce some measure of confidence for their predictions; perhaps LLMs could incorporate that as well, e.g. assign probabilities(/perplexity?) to each of the tokens they generate.
> LLMs are great at natural language comprehension, but also have a lot of neurons dedicated to factoids. Using neurons that way is really inefficient, can we split the "language" capability from the "knowledge" capability and have the former just look things up in a database?
I think the beauty of LLMs is exactly that all we need to do is to feed them raw text --- the hope, I guess, had been that the models will be able to develop human-like insights by learning to understand and "speak" languages on its own.
Introducing "feature engineering" (e.g. the distinction as you suggested) would defeat that goal.
- IanCal 3y ago> perhaps LLMs could incorporate that as well, e.g. assign probabilities(/perplexity?) to each of the tokens they generate. They do, that's how tokens are selected. Locally run models or non-chat ones from openai can return the probabilities and you can do things like modify or filter them.
- RyEgswuCsn 3y agoI was thinking more of the entropy of the distribution over the distribution sort of thing --- one may say a model is uncertain about its statements if all its potential variants are equally probable and vice versa.