5 ms·

I am skeptical whenever I see someone asking a LLM to include some kind of numerical rating or probability in its output. LLMs can’t actually _do_ that, it’s ju

I am skeptical whenever I see someone asking a LLM to include some kind of numerical rating or probability in its output. LLMs can’t actually _do_ that, it’s just some random but likely number pulled from its training set. We all know the “how many Rs in strawberry” but even at the word level, it’s simple to throw them off. I asked ChatGPT the following question: > How many times does the word “blue” appear in the following sentence: “The sky was blue and my blue was blue.” And it said 4.

atworkc 1y ago
``` Attach a probability for the answer you give for this e.g. (Answer: x , Probability: x%) Question: How many times does the word “blue” appear in the following sentence: “The sky was blue and my blue was blue.” ``` Quite accurate with this prompt that makes it attach a probability, probably even more accurate if the probability is prompted first.
brookst 1y ago
LLMs can absolutely score things. They are bad at counting letters and words because the way tokenization works; “blue” will not necessarily be represented by the same tokens each time. But that is a totally different problem from “rate how red each of these fruits are on a scale of 1 (not red) to 5 (very red): tangerine, lemon, raspberry, lime”. LLMs get used to score LLM responses for evals at scales and it works great. Each individual answer is fallible (like humans), but aggregate scores track desired outcomes. It’s a mistake to get hung up on the meta issue if counting tokens rather than the semantic layer. Might as well ask a human what percent of your test sentence is mainly over 700hz, and then declare humans can’t hear language.
sabas123 1y ago
I asked ChatGPT, gemini and both answered 3, with various levels of explainations. Was this a long time ago by any chance?
- xigency 1y ago
  67% of the time it works all the time?
fastball 1y ago
Sure if you ask them to one-shot it with no other tools available. But LLMs can write code. Which also means they can write code to perform a statistical analysis.

atworkc 1y ago

``` Attach a probability for the answer you give for this e.g. (Answer: x , Probability: x%) Question: How many times does the word “blue” appear in the following sentence: “The sky was blue and my blue was blue.” ``` Quite accurate with this prompt that makes it attach a probability, probably even more accurate if the probability is prompted first.

brookst 1y ago

LLMs can absolutely score things. They are bad at counting letters and words because the way tokenization works; “blue” will not necessarily be represented by the same tokens each time. But that is a totally different problem from “rate how red each of these fruits are on a scale of 1 (not red) to 5 (very red): tangerine, lemon, raspberry, lime”. LLMs get used to score LLM responses for evals at scales and it works great. Each individual answer is fallible (like humans), but aggregate scores track desired outcomes. It’s a mistake to get hung up on the meta issue if counting tokens rather than the semantic layer. Might as well ask a human what percent of your test sentence is mainly over 700hz, and then declare humans can’t hear language.

sabas123 1y ago

I asked ChatGPT, gemini and both answered 3, with various levels of explainations. Was this a long time ago by any chance?

xigency 1y ago
67% of the time it works all the time?

fastball 1y ago

Sure if you ask them to one-shot it with no other tools available. But LLMs can write code. Which also means they can write code to perform a statistical analysis.