You're talking about a different model — this post is about ChatGPT. text‑davinci‑003 still fails on all of the Hofstadter/Bender questions that I've tried. The prompt you're suggesting does not seem to produce a hallucination in ChatGPT:
LLMS
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Challenge of incorporating all known trivia into training data
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Expecting it to fully integrate every piece of trivia seems unfair — a lot of people would rate the first answer as reasonable. It’s fundamentally difficult to get training data that incorporates everything the model knows, rather than what the human demonstrator knows.
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GPT-3 improvement shows not just pattern-matching, still hallucinates details
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The point isn’t that it’s perfect, just that it’s not narrowly pattern-matching on this specific list of questions — there’s clear improvement vs. GPT-3 across many questions that contain false assumptions. It does still hallucinate details in other ways.
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Tokenization issue: model struggles with letter sequences
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This feels like a tokenization issue — in general, it doesn’t see text as sequences of letters, and struggles with tasks that assume it does.
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OpenAI ChatGPT explains bubble sort complexity in 1940s gangster style
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OpenAI's new ChatGPT explains the worst-case time complexity of the bubble sort algorithm, with Python code examples, in the style of a fast-talkin' wise guy from a 1940's gangster movie:
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GPT-3 succeeds on trick questions, pre-training unchanged since 2021
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No. It also succeeds on questions I’ve written myself that trick GPT-3, as in this screenshot. The pre-training data hasn’t been updated since 2021, and OpenAI specifically denies (in this thread) training on the Hofstadter/Bender questions.
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ChatGPT trained against prompt injection, challenge to break with clever input
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OpenAI’s new ChatGPT seems to be trained against prompt injection. Example shown yields 0 exploit responses out of 10 attempts. See if you can break it with more clever input — include success rate out of 10 trials with screenshot: http://
chat.openai.com -
DeepMind Reduces Math Reasoning Errors Using Process-Based Supervision
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DeepMind Studies Process- vs Outcome-based Model Supervision, Significantly Reducing Reasoning Errors on Math Word Problems https://
syncedreview.com/2022/11/30/dee
pmind-studies-process-vs-outcome-based-model-supervision-significantly-reducing-reasoning-errors-on-math-word-problems/
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OpenAI ChatGPT writes Seinfeld scene on bubble sort
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OpenAI's new ChatGPT writes a Seinfeld scene in which Jerry needs to learn the bubble sort algorithm:
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ChatGPT reduces hallucinations, avoids false-assumption pitfalls
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To be fair, they don’t claim to have solved hallucination fully, and explain why in the announcement. But it’s clearly better, and doesn’t seem to fall for these false-assumption questions that trick GPT-3. https://
openai.com/blog/chatgpt/