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Leetcode (hard) from 0/45 (GPT-3.5) to 3/45 (GPT-4). The lack of progress here, says a lot more about is NOT happening as an AI paradigm change. Still a glorified pattern matching and pattern creation engine, even if a very impressive one.
In case anyone missed this part of the article: The livestream of the GPT-4 demo will be on the OpenAI YouTube page in three hours. [0] [0]: https://www.youtube.com/openai Edit - Direct link to the livestream: https://www.youtube.com/watch?v=outcGtbnMuQ
A class of problem that GPT-4 appears to still really struggle with is variants of common puzzles. For example: >Suppose I have a cabbage, a goat and a lion, and I need to get them across a river. I have a boat that can only carry myself and a single other item. I am not allowed to leave the cabbage and lion alone together, and I am not allowed to leave the lion and goat alone together. How can I safely get all three…
It's a good observation. Although on the flip side, I almost went to type up a reply to you explaining why you were wrong and why bringing the goat first is the right solution. Until I realized I misread what your test was when I skimmed your comment. Likely the same type of mistake GPT-4 made when "seeing" it. Intuitively, I think the answer is that we do have two types of thinking. The pattern matching fast thinkin…
GPT does not model subjects. GPT does not even model words! It models tokens.
The structure of GPT's model is semantic, not logical. It's a model of how each token in the text that is present in GPT's training corpus relates to the rest of the tokens in that text.
The correct answer to a familiar logic problem just happens to be the text that is already present in the corpus. The answer GPT gives is the text from GPT's model that is semantically closest to the text in your prompt.
Knowing that, it is no longer a mystery how GPT "gets confused": the text in your "misleading prompt" was still semantically closest to the familiar answer.
The result is subjectively and logically wrong, because subjects and logic were never involved in the process!
In order to resolve this, ChatGPT's training corpus needs to contain a "correct answer" next to every unique permutation of every question. We can't expect that to be the case, so we should instead expect GPT to generate false, yet familiar, responses.
Access is invite only for the API, and rate limited for paid GPT+. > gpt-4 has a context length of 8,192 tokens. We are also providing limited access to our 32,768–context (about 50 pages of text) version, gpt-4-32k, which will also be updated automatically over time (current version gpt-4-32k-0314, also supported until June 14). Pricing is $0.06 per 1K prompt tokens and $0.12 per 1k completion tokens. The context le…
Will any of the profits be shared with original authors whose work powers the model?
https://cdn.openai.com/papers/gpt-4.pdf >Given both the competitive landscape and the safety implications of large-scale models like GPT-4, this report contains no further details about the architecture (including model size), hardware, training compute, dataset construction, training method, or similar. At that point, why bother putting out a paper?
as youtuber CGP Grey says, "shenanigans beget shenanigans"
A class of problem that GPT-4 appears to still really struggle with is variants of common puzzles. For example: >Suppose I have a cabbage, a goat and a lion, and I need to get them across a river. I have a boat that can only carry myself and a single other item. I am not allowed to leave the cabbage and lion alone together, and I am not allowed to leave the lion and goat alone together. How can I safely get all three…
LLMs aren’t reasoning about the puzzle. They’re predicting the most likely text to print out, based on the input and the model/training data. If the solution is logical but unlikely (i.e. unseen in the training set and not mapped to an existing puzzle), then the probability of the puzzle answer appearing is very low.
This technology has been a true blessing to me. I have always wished to have a personal PhD in a particular subject whom I could ask endless questions until I grasped the topic. Thanks to recent advancements, I feel like I have my very own personal PhDs in multiple subjects, whom I can bombard with questions all day long. Although I acknowledge that the technology may occasionally produce inaccurate information, the…