4 ms·

But if I set the temperature to 0, the model will pick the highest probable token and the output will be always the same. But we already know that by no mean it

But if I set the temperature to 0, the model will pick the highest probable token and the output will be always the same. But we already know that by no mean it can guarantee a correct answer. So how can multiple runs be better?

phire 3y ago
Yes, but picking the most similar output from a bunch of queries with a higher temperature is not the same thing as the output from a single low temperature query.
- sinuhe69 3y ago
  Possibly, but it stills doesn’t explain why multiple runs will result in better answer. In the work, the authors also hasn’t compared the multiple runs results with the single run using zero temperature. So, maybe all the overhead is just to achieve the same result already encoded in the networks? I don’t know. Also the result is somewhat counterintuitive. We know that by low level of understanding, if we ask a student a hard question and he tried many times, the most accurate answer is often not the most popular one but a single answer. And that by retaining memory, reasoning capacity and continuous learning , which is not the case with LLM. Btw: HN is for discussion. If some just want to vote for the beauty contest, please leave.
  - phire 3y ago
    I found this other paper that tests Temperature: https://arxiv.org/abs/2402.05201 https://arxiv.org/abs/2402.05201 It appears that temperature has no impact on problem solving performance. So this paper isn't getting improved performance because the token for the correct answer is more probable. My theory is that the multiple queries are allowing the whole probability space of possible answers to be sampled. Not just the probabilities of the most likely output token, but the probabilities of all possible internal model states. And sampling that probability space of the whole model state and finding the average is a very different mathematical operation to just picking a single model state at random and then picking the most probable output tokens.
    - mlsu 3y ago
      I wonder if there is a clever/more efficient shortcut that could come from before the sample is taken on each token. We have the logits after all.
    - bt1a 3y ago
      If I'm reading this correctly, they had to discard Llama 2 answers and only use GPT-3.5 given answers to test the hypothesis. GPT-3.5 answering questions through the OAI API alone is not an acceptable method of testing problem solving ability across a range of temperatures. OpenAI does some blackbox wizardry on their end. There are many complex and clever sampling techniques for which temperature is just one (possibly dynamic) component One example from the llama.cpp codebase is dynamic temperature sampling https://github.com/ggerganov/llama.cpp/pull/4972/files https://github.com/ggerganov/llama.cpp/pull/4972/files Not sure what you mean by whole model state given that there are tens of thousands of possible tokens and the models have billions of parameters in XX,XXX-dimensional space. How many queries across how many sampling methods might you need? Err..how much time? :)
  - numpad0 3y ago
    Just from reading comments around, it feels intuitive to me that looking at a heatmap of cascading pendulum would be more “accurate” than looking at just one snapshot, and also that joints on the pendulums don’t necessarily need to be interlinked between iterations of simulations
  - wokwokwok 3y ago
    > Also the result is somewhat counterintuitive. We know that by low level of understanding, if we ask a student a hard question and he tried many times, the most accurate answer is often not the most popular one but a single answer. This is a bad analogy. Here’s what is actually happening with no “common sense but wrong” understanding of it: - You have a set of probabilities per token. - You randomize them. This is not a “bad student being asked multiple times” it is a system with randomized probabilities, creating a probability distribution. If you want to see what a probability distribution looks like (eg. An electron cloud) then sampling only once is the wrong way to do it. You basically have two distributions; the first one is the LLM, the second one is the shape generated by adding the random factor in the temperature. This allows you to escape the “local maxima” encoded in the LLM distribution to find highly probable solutions that are outside the sample space of the “zero temperature”. If you want a better analogy, look up at the night sky full of stars. Draw circle in the sky; that’s the LLM distribution. The result from a zero temperature will be the brightest point in that circle. When you push the temperature up, you blur the sky randomly. Some points become brighter, some dimmer, but the radius of the circle increases. If there is a very bright point outside the sample circle 10x brighter than the brightest point inside it then repeated random samples will repeatedly find it. It makes perfect sense that an expanded probability distribution sampled repeatedly could find a “good average solution” if that solution is significantly better than the best “zero temp” solution. This is the same reason we have 'temp' at all; by widening the solution space probability distribution, you can find better maxima. Turns out, sampling multiple times lets you have more chances to find better maxima. This is more like "well that seems obviously like a good idea" than "somewhat counterintuitive"; it's just slow and expensive to do it. You can also adjust the probability distribution by other existing methods, obviously, what's surprising here is not that it works, but that it seem to work so well; probably (and I note they did not try this in their paper), a multi-sample + voting on the output from other methods would also be highly effective.
sp332 3y ago
According to their code, they used temperature 1. https://anonymous.4open.science/r/more_agent_is_all_you_need/script/run_reasoning_task.sh https://anonymous.4open.science/r/more_agent_is_all_you_need...

phire 3y ago

Yes, but picking the most similar output from a bunch of queries with a higher temperature is not the same thing as the output from a single low temperature query.

sinuhe69 3y ago
Possibly, but it stills doesn’t explain why multiple runs will result in better answer. In the work, the authors also hasn’t compared the multiple runs results with the single run using zero temperature. So, maybe all the overhead is just to achieve the same result already encoded in the networks? I don’t know. Also the result is somewhat counterintuitive. We know that by low level of understanding, if we ask a student a hard question and he tried many times, the most accurate answer is often not the most popular one but a single answer. And that by retaining memory, reasoning capacity and continuous learning , which is not the case with LLM. Btw: HN is for discussion. If some just want to vote for the beauty contest, please leave.
- phire 3y ago
  I found this other paper that tests Temperature: https://arxiv.org/abs/2402.05201 https://arxiv.org/abs/2402.05201 It appears that temperature has no impact on problem solving performance. So this paper isn't getting improved performance because the token for the correct answer is more probable. My theory is that the multiple queries are allowing the whole probability space of possible answers to be sampled. Not just the probabilities of the most likely output token, but the probabilities of all possible internal model states. And sampling that probability space of the whole model state and finding the average is a very different mathematical operation to just picking a single model state at random and then picking the most probable output tokens.
  - mlsu 3y ago
    I wonder if there is a clever/more efficient shortcut that could come from before the sample is taken on each token. We have the logits after all.
  - bt1a 3y ago
    If I'm reading this correctly, they had to discard Llama 2 answers and only use GPT-3.5 given answers to test the hypothesis. GPT-3.5 answering questions through the OAI API alone is not an acceptable method of testing problem solving ability across a range of temperatures. OpenAI does some blackbox wizardry on their end. There are many complex and clever sampling techniques for which temperature is just one (possibly dynamic) component One example from the llama.cpp codebase is dynamic temperature sampling https://github.com/ggerganov/llama.cpp/pull/4972/files https://github.com/ggerganov/llama.cpp/pull/4972/files Not sure what you mean by whole model state given that there are tens of thousands of possible tokens and the models have billions of parameters in XX,XXX-dimensional space. How many queries across how many sampling methods might you need? Err..how much time? :)
- numpad0 3y ago
  Just from reading comments around, it feels intuitive to me that looking at a heatmap of cascading pendulum would be more “accurate” than looking at just one snapshot, and also that joints on the pendulums don’t necessarily need to be interlinked between iterations of simulations
- wokwokwok 3y ago
  > Also the result is somewhat counterintuitive. We know that by low level of understanding, if we ask a student a hard question and he tried many times, the most accurate answer is often not the most popular one but a single answer. This is a bad analogy. Here’s what is actually happening with no “common sense but wrong” understanding of it: - You have a set of probabilities per token. - You randomize them. This is not a “bad student being asked multiple times” it is a system with randomized probabilities, creating a probability distribution. If you want to see what a probability distribution looks like (eg. An electron cloud) then sampling only once is the wrong way to do it. You basically have two distributions; the first one is the LLM, the second one is the shape generated by adding the random factor in the temperature. This allows you to escape the “local maxima” encoded in the LLM distribution to find highly probable solutions that are outside the sample space of the “zero temperature”. If you want a better analogy, look up at the night sky full of stars. Draw circle in the sky; that’s the LLM distribution. The result from a zero temperature will be the brightest point in that circle. When you push the temperature up, you blur the sky randomly. Some points become brighter, some dimmer, but the radius of the circle increases. If there is a very bright point outside the sample circle 10x brighter than the brightest point inside it then repeated random samples will repeatedly find it. It makes perfect sense that an expanded probability distribution sampled repeatedly could find a “good average solution” if that solution is significantly better than the best “zero temp” solution. This is the same reason we have 'temp' at all; by widening the solution space probability distribution, you can find better maxima. Turns out, sampling multiple times lets you have more chances to find better maxima. This is more like "well that seems obviously like a good idea" than "somewhat counterintuitive"; it's just slow and expensive to do it. You can also adjust the probability distribution by other existing methods, obviously, what's surprising here is not that it works, but that it seem to work so well; probably (and I note they did not try this in their paper), a multi-sample + voting on the output from other methods would also be highly effective.

sp332 3y ago

According to their code, they used temperature 1. https://anonymous.4open.science/r/more_agent_is_all_you_need/script/run_reasoning_task.sh https://anonymous.4open.science/r/more_agent_is_all_you_need...