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This is the take I thought I'd have, but in the last example, the guesser model reaches the correct conclusion using a different reasoning than the clue giver m
by deredede 2y ago
This is the take I thought I'd have, but in the last example, the guesser model reaches the correct conclusion using a different reasoning than the clue giver model.
The clue giver justifies the link of Paper and Log as "written records", and between Paper and Line as "lines of text". But the guesser model connects Paper and Log because "paper is made from logs" (reaching the conclusion through a different meaning of Log), and connects Paper and Line because "'lined paper' is a common type of paper".
Similarly, in the first example, the clue giver connects Monster and Lion because lions are "often depicted as a mythical beast or monster in legends" (a tenuous connection if you ask me), whereas the guesser model thought about King because of King Kong (which I also prefer to Lion).
- unlikelymordant 2y agogenerally there is a "temperature" parameter that can be used to add some randomness or variety to the LLMs outputs by changing the likelihood of the next word being selected. This means you could just keep regenerating the same response and get different answers each time. each time it will give different plausible responses, and this is all from the same model. This doesn't mean it believes any of them, it just keeps hallucinating likely text, some of which will fit better than others. It is still very much the same brain (or set of trained parameters) playing with itself.
- suveen_ellawela 2y agoI wanted to play around with the temperature, but unfortunately o1 only supports '1' as the value.
- wizzwizz4 2y ago> But the guesser model connects Paper and Log because "paper is made from logs" (reaching the conclusion through a different meaning of Log) No, it doesn't. It reaches the conclusion because of vector similarity (simplified explanation): these explanations are post-hoc.
- Angostura 2y agoSorry, I’m uninformed. Do you mean thaw the explanation could be completely unrelated to the actual “reason”
- DiscourseFan 2y agoYes, the reason is that the model assigns words positions in an ever-changing vector space and evaluates relation by their correspondence in that space—the reply it gives is also a certain index of that space, with the “why” in the question giving it the weight of producing an “answer.” Video series on the topic: https://www.3blue1brown.com/topics/neural-networks https://www.3blue1brown.com/topics/neural-networks Which is to say that “why” it gives those answers is because its statistically likely within its training data that when there are the words, “why did you connect line and log with paper” the text which follows could be “logs are made of wood and lines are in paper.” But that is not the specific relation of the 3 words in the model itself, which is just a complex vector space.
- jprete 2y agoI definitely think it's doing more than that here (at least inside of the vector-space computations). The model probably directly contains the paper-wood-log association.
- jncfhnb 2y agoIf an LLM states an answer and then provides a justification for that answer, the justification is entirely irrelevant to the reasoning the bot used. It might be that the semantics of the justification happen to align with the implied logic of the internal vector space, but it is best case a manufactured coincidence. It’s not different from you stating an answer and then telling the bot to justify it. If an LLM is told to do reasoning and then state the answer, it follows that the answer is basically guaranteed to be derived from the previously generated reasoning.
- ActivePattern 2y ago