My issue is lack of transparency. If this interpretation is right, it wouldn't be hard to add a line like: "Whether LLMs plagiarize is an emerging topic of discussion, we deliberated and chose to be conservative, we look forward to how things unfold, etc."
LLMS
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Celebrating 13k followers: ML, NLP and GPT-3 explained
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Celebrating 13k followers Thank you for the overwhelming love and support I simplify Machine Learning, NLP, and Large Language Models like GPT-3 for you! Follow me → @Saboo_Shubham_
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Improving ChatGPT Results with Better Prompts and Context
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Weak ChatGPT prompts tend to produce the most average, expected ideas. Instead:
• Be specific
• Provide examples
• Ask for a type of information
• Define your desired outcome
• Define your audience The more context you provide, the better the results. -
Microsoft Bing to Integrate OpenAI’s ChatGPT for Search Functionality
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OpenAI ChatGPT to be added to Microsoft Bing search results, creating a ChatGPT-based version of what http://
Perplexity.ai does today. Unclear if this implies the existence of a ChatGPT API vs. Bing just using GPT-3.5 to create their own chatbot. -
Prompting GPT-3 with Python for Precise Text and JSON Generation
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Prompting GPT-3 to reliably generate text and JSON data in a precise format using Python assertions, f‑strings, and variables declared only in our imaginations.
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Do Large Language Models Constitute Plagiarism?
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I think it is super debatable whether large language models (and more generally, powerful ML models) count as plagiarism. This seems like a big question that we will have to grapple with as a community and society.
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Explaining Prompt Tuning Technique and GPT-3 API Limitations
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Sort of — there’s a technique called prompt tuning where you optimize an internal state that the prompt is internally transformed into, adjusting empirically based on examples. It can’t be used with GPT-3 though because the model is behind an API. (Sorry for double post — typos)
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The Lingering Danger of AI Hallucination and User Trust
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As ChatGPT and systems like it improve, I worry we’ll forget what hallucination looks like. We’ll forget it’s there, lurking in the distributional tails. And we will commit. And upon our servers will be errors. And we will post. And upon our timelines there will be dunks.
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Prompt Engineering Challenges: Avoiding Unreliability and Confabulation in Text-Davinci-002
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But the unreliability, the confabulation, was unmissable. You can’t use text‑davinci‑002 without seeing it. Learning to avoid it in narrow domains is much of the challenge of prompt engineering.
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Evolution of LLM Hallucinations and AI Promise
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I began studying LLMs to understand the content of their hallucinations. Even in text‑davinci‑002, confabulation was oxygen. 003 has less. ChatGPT less still. Every year, we need squint less to see promise.