I’ve always wondered with these kinds of weird apparent trivial flaws in LLM behaviour if it doesn’t have something to do with the way the next token is usually randomly sampled from the softmax multinomial distribution rather than taking the argmax (most likely) of the probabilities. Does anyone know if reducing the temperature parameter to zero so that it’s effectively the argmax changes things like this at all?
Also, quickly looking into how LLM token sampling works nowadays, you may also need to set the parameters top_p to 0, and top_k to 1 to get it to actually function like argmax. Looks like these can only be set through the API if you’re using ChatGPT or similar proprietary LLMs. Maybe I’ll try experimenting with this when I find the time, if nothing else to rule out the possibility of such a seemingly obvious thing being missed.
I’ve always wondered with these kinds of weird apparent trivial flaws in LLM behaviour if it doesn’t have something to do with the way the next token is usually randomly sampled from the softmax multinomial distribution rather than taking the argmax (most likely) of the probabilities. Does anyone know if reducing the temperature parameter to zero so that it’s effectively the argmax changes things like this at all?
Also, quickly looking into how LLM token sampling works nowadays, you may also need to set the parameters top_p to 0, and top_k to 1 to get it to actually function like argmax. Looks like these can only be set through the API if you’re using ChatGPT or similar proprietary LLMs. Maybe I’ll try experimenting with this when I find the time, if nothing else to rule out the possibility of such a seemingly obvious thing being missed.