5 ms·

I'm on the fence about the scoring system. You're really being tested on (at least) 4 different things: (1) whether you think an LLM can answer the correctly

I'm on the fence about the scoring system. You're really being tested on (at least) 4 different things: (1) whether you think an LLM can answer the correctly (2) whether the answer has appeared in the training set (3) how much non-determinism will play a role in the LLM response (i.e. 0-shot capability) (4) how rational you're feeling that day (or, how well educated you are in statistics) I was familiar with many of these questions in my own experience, and have seen completely different outcomes from what the quiz determined was the correct answer. I agree with others here that non-determinism can really mess things up here and is really assessing a 0-shot score, which IMO understates how LLMs are actually used (iterative enhancements, many-shot Q&A). Finally, the scoring system tickled my ego and encouraged me to try to make up for prior errors, with disastrous effects (I was well aware that I should just go with 0.5 when uncertain): > You answered 53.57% of questions correctly with an average log-loss of 1.233. This means you would have scored better by just leaving your guesses at 50% for every single question. > On average there were 71.22% of people who did better at this game than you... If you had been better calibrated, you could have scored in the top 14.09% [1] The site implicitly acknowledges it's a questionable scoring mechanism when it points out: > there are 78.09% of people who performed worse than if they had never changed the default predictor away from 50-50 for each question If there is a simple way to game the scoring, then you can't know if the score is accurately reflecting people's confidence, or just their rationality/statistical knowledge. [1] https://nicholas.carlini.com/writing/llm-forecast/final/3388d1ab-1948-4d08-9a6a-58703734ecae https://nicholas.carlini.com/writing/llm-forecast/final/3388...

Mathnerd314 3y ago
Well there is this notion of "calibrating" the score. It is well-known that most humans are bad at estimating calibrated probabilities unassisted. The system could have been designed to accommodate this user-interface difficulty. For example, I am sure there is enough data floating around that you could map a simple 5-choice Likert scale to some calibrated probabilities, without making any assumptions. But instead it is just a raw slider, with nothing marked besides the default 50-50, not really great for input. Even a simple "yes/no" choice (translating to fixed calibrated probabilities around 25%/75%) would probably result in better log-loss scores overall.
- kqr 3y ago
  If you're suggesting a Likert scale of "very unlikely" "somewhat likely" etc.: no, the data floating around suggests that does not work. People saying "somewhat likely" mean anything between 10 % and 80 %. There's no way to map fuzzy descriptions to calibrated probabilities. If you're suggesting a Likert scale with alternatives like "10 %", "25 %", "50 %", etc. that are then auto-calibrated against the average human overconfidence (so that an answer of 25 % really means 40 %), then that might work, but what would be the point?
  - Mathnerd314 3y ago
    10% for "somewhat likely" wouldn't make any sense, "likely" by itself means >50%. I was proposing to simply label 5 or 7 points on the slider, like 10% as "very unlikely", 50% as "neutral", and 66% as "somewhat likely". I am sure there is a decent-sized study that asked people to predict events on a Likert scale as likely/unlikely and then one could calibrate the mapping from Likert scale to probability points using this study. There is a study showing that people intuitively map Likert scales to a slider https://link.springer.com/article/10.3758/s13423-017-1344-2/figures/3 https://link.springer.com/article/10.3758/s13423-017-1344-2/... so by properly spacing and positioning the Likert labels, people will at least be somewhat more calibrated than in the absence of any cues.
    - kqr 3y ago
      It does make sense for some people. Some people might say it's somewhat likely Sweden is still not a NATO member at the end of the year – and mean there's a 10 % probability it happens. That's the problem with these fuzzy labels – the variance between individuals (and even within individuals across time) is huge.
ssnistfajen 3y ago
I always tilted to at least 75% or 25% for each question. IMO leaving it at 50-50 defeats the whole point of the "challenge" as it would indicate the user has no real opinion on whether GPT-4 can answer correctly or not. Either GPT-4 is correct or it is wrong. Putting a binary choice on a slider would be very confusing to people not accustomed to measuring everything on a probability scale.
SomewhatLikely 3y ago
You forgot the effects of adjusting your prediction on questions that asked multiple different questions, not just one. I think I can guess okay how well it would do at forming a word with specific letters, but what's the probability of it getting five out of ten? Now we're bringing in unintuitive probabilistic reasoning as well.

Mathnerd314 3y ago

Well there is this notion of "calibrating" the score. It is well-known that most humans are bad at estimating calibrated probabilities unassisted. The system could have been designed to accommodate this user-interface difficulty. For example, I am sure there is enough data floating around that you could map a simple 5-choice Likert scale to some calibrated probabilities, without making any assumptions. But instead it is just a raw slider, with nothing marked besides the default 50-50, not really great for input. Even a simple "yes/no" choice (translating to fixed calibrated probabilities around 25%/75%) would probably result in better log-loss scores overall.

kqr 3y ago
If you're suggesting a Likert scale of "very unlikely" "somewhat likely" etc.: no, the data floating around suggests that does not work. People saying "somewhat likely" mean anything between 10 % and 80 %. There's no way to map fuzzy descriptions to calibrated probabilities. If you're suggesting a Likert scale with alternatives like "10 %", "25 %", "50 %", etc. that are then auto-calibrated against the average human overconfidence (so that an answer of 25 % really means 40 %), then that might work, but what would be the point?
- Mathnerd314 3y ago
  10% for "somewhat likely" wouldn't make any sense, "likely" by itself means >50%. I was proposing to simply label 5 or 7 points on the slider, like 10% as "very unlikely", 50% as "neutral", and 66% as "somewhat likely". I am sure there is a decent-sized study that asked people to predict events on a Likert scale as likely/unlikely and then one could calibrate the mapping from Likert scale to probability points using this study. There is a study showing that people intuitively map Likert scales to a slider https://link.springer.com/article/10.3758/s13423-017-1344-2/figures/3 https://link.springer.com/article/10.3758/s13423-017-1344-2/... so by properly spacing and positioning the Likert labels, people will at least be somewhat more calibrated than in the absence of any cues.
  - kqr 3y ago
    It does make sense for some people. Some people might say it's somewhat likely Sweden is still not a NATO member at the end of the year – and mean there's a 10 % probability it happens. That's the problem with these fuzzy labels – the variance between individuals (and even within individuals across time) is huge.

ssnistfajen 3y ago

I always tilted to at least 75% or 25% for each question. IMO leaving it at 50-50 defeats the whole point of the "challenge" as it would indicate the user has no real opinion on whether GPT-4 can answer correctly or not. Either GPT-4 is correct or it is wrong. Putting a binary choice on a slider would be very confusing to people not accustomed to measuring everything on a probability scale.

SomewhatLikely 3y ago

You forgot the effects of adjusting your prediction on questions that asked multiple different questions, not just one. I think I can guess okay how well it would do at forming a word with specific letters, but what's the probability of it getting five out of ten? Now we're bringing in unintuitive probabilistic reasoning as well.