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Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: New GPT3 Impressive Capabilities - InstructGPT3 [1/2], published by simeon c on March 13, 2022 on The Effective Altruism Forum. Summary InstructGPT3 (hereafter IGPT3), a better version of GPT3 has recently been released by OpenAI. This post explores its new capabilities. IGPT3 has new impressive capabilities and many potential uses. Among others, it can help users: Brainstorm Summarize the main claims made by a scientific field, an author or a school of thought. Find an analogy or a metaphor for something hard to explain I emphasize some of IGPT3's limits, especially situations where It provides very plausible fake answers. Twitter more entertaining version of the summary: Introduction Epistemic status: I spent about ~12h with IGPT3. So I’d say that I now have a pretty good sense of some of its key features. I tried several examples to ensure that I was not overfitting on a single example for the most important claims I made. That said, this is a huge model so there is probably a lot more to be discovered. FYI I had spent a decent amount of time playing with the past GPT3, especially with the Davinci (175b params) and the Curie (6b params) models, so I had a clear idea of "what it is like to try to get nice completions from GPT3". That may be one reason why I’m so amazed by this one. Here's the first post of a series of 2 blog posts exploring some of the IGPT3 (I) new capabilities and (II) epistemic biases. This first post will focus on some interesting uses I had of IGPT3. It also gives a sense of how good it is in various domains. Let me tell you: I'm amazed by its new capabilities. I find it really impressive that most of the time, the first result I get, without any tuning (either of the parameters or of the prompt) is great. You can try it yourself here. The blogpost is organized as follow: A few general observations 2 mains parts (Examples of Potential Uses / Limits) The last part entitled “Many more prompts than you wanted to read” where I put robustness checks, I test how sensitive IGPT3 is to unique words variations, I compare IGPT3 and GPT3 and I show how you can have fun with IGPT3. Acknowledgment: Thanks to JS and Florent Berther for the proofreading, to Ozzie Gooen for the suggestion to make a post out of my comments on his post and to Charbel-Raphaël Segerie for some nice ideas. General Features Here are some general features of IGPT3: Compared to GPT3, you need to spend much less time prompt-tuning on IGPT3. You just need to be clear enough. When the temperature (a parameter to control the randomness of the completion) is greater than 0, you need to try less than 2 completions to find a satisfactory answer when IGPT3 has one. And most of the time, a single completion is enough. A temperature of 0 also works very well, so I personally use that for most uses. IGPT3 now knows when to stop so you can put a huge maximum limit of tokens and he will generally only use a small part of it to answer your question. Given this new feature, you just have to ask him if you want something specific. Here are some examples: If you want many suggestions, you can ask for it explicitly: "Give me the five best arguments". If you want something more specific, you can explicitly ask for it: "I don't understand X, can you elaborate ?" Examples of Potential Uses Brainstorming IGPT3 is very useful to brainstorm. I personally use it more and more because it enables me to quickly generate a lot of ideas on anything I want to think about. Project Names IGPT3 is useful to sometimes suggest associations of concepts you hadn’t thought of. That way, it can help find good names. Differences and Similarities between Concepts Rapidly Accessing Information I use IGPT3 more and more to make sure that I didn’t miss a big argument on a topic because IGPT3 is very good to tell the most common...