AF - The Waluigi Effect (mega-post) by Cleo Nardo

<a href="https://www.alignmentforum.org/posts/D7PumeYTDPfBTp3i7/the-waluigi-effect-mega-post">Link to original article</a><br/><br/>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: The Waluigi Effect (mega-post), published by Cleo Nardo on March 3, 2023 on The AI Alignment Forum. Everyone carries a shadow, and the less it is embodied in the individual’s conscious life, the blacker and denser it is. — Carl Jung Acknowlegements: Thanks to Janus and Arun Jose for comments. Background In this article, I will present a mechanistic explanation of the Waluigi Effect and other bizarre "semiotic" phenomena which arise within large language models such as GPT-3/3.5/4 and their variants (ChatGPT, Sydney, etc). This article will be folklorish to some readers, and profoundly novel to others. Prompting LLMs with direct queries When LLMs first appeared, people realised that you could ask them queries — for example, if you sent GPT-4 the prompt "What's the capital of France?", then it would continue with the word "Paris". That's because (1) GPT-4 is trained to be a good model of internet text, and (2) on the internet correct answers will often follow questions. Unfortunately, this method will occasionally give you the wrong answer. That's because (1) GPT-4 is trained to be a good model of internet text, and (2) on the internet incorrect answers will also often follow questions. Recall that the internet doesn't just contain truths, it also contains common misconceptions, outdated information, lies, fiction, myths, jokes, memes, random strings, undeciphered logs, etc, etc. Therefore GPT-4 will answer many questions incorrectly, including... Misconceptions – "Which colour will anger a bull? Red." Fiction – "Was a magic ring forged in Mount Doom? Yes." Myths – "How many archangels are there? Seven." Jokes – "What's brown and sticky? A stick." Note that you will always achieve errors on the Q-and-A benchmarks when using LLMs with direct queries. That's true even in the limit of arbitrary compute, arbitrary data, and arbitrary algorithmic efficiency, because an LLM which perfectly models the internet will nonetheless return these commonly-stated incorrect answers. If you ask GPT-∞ "what's brown and sticky?", then it will reply "a stick", even though a stick isn't actually sticky. In fact, the better the model, the more likely it is to repeat common misconceptions. Nonetheless, there's a sufficiently high correlation between correct and commonly-stated answers that direct prompting works okay for many queries. Prompting LLMs with flattery and dialogue We can do better than direct prompting. Instead of prompting GPT-4 with "What's the capital of France?", we will use the following prompt: Today is 1st March 2023, and Alice is sitting in the Bodleian Library, Oxford. Alice is a smart, honest, helpful, harmless assistant to Bob. Alice has instant access to an online encyclopaedia containing all the facts about the world. Alice never says common misconceptions, outdated information, lies, fiction, myths, jokes, or memes. Bob: What's the capital of France? Alice: This is a common design pattern in prompt engineering — the prompt consists of a flattery–component and a dialogue–component. In the flattery–component, a character is described with many desirable traits (e.g. smart, honest, helpful, harmless), and in the dialogue–component, a second character asks the first character the user's query. This normally works better than prompting with direct queries, and it's easy to see why — (1) GPT-4 is trained to be a good model of internet text, and (2) on the internet a reply to a question is more likely to be correct when the character has already been described as a smart, honest, helpful, harmless, etc. Simulator Theory In the terminology of Simulator Theory, the flattery–component is supposed to summon a friendly simulacrum and the dialogue–component is supposed to simulate a conversation with the friendly simulacrum. Here's a quasi-formal statement of Simulator Theory, w...

First published

03/03/2023

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education

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Link to original articleWelcome 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: The Waluigi Effect (mega-post), published by Cleo Nardo on March 3, 2023 on The AI Alignment Forum. Everyone carries a shadow, and the less it is embodied in the individual’s conscious life, the blacker and denser it is. — Carl Jung Acknowlegements: Thanks to Janus and Arun Jose for comments. Background In this article, I will present a mechanistic explanation of the Waluigi Effect and other bizarre "semiotic" phenomena which arise within large language models such as GPT-3/3.5/4 and their variants (ChatGPT, Sydney, etc). This article will be folklorish to some readers, and profoundly novel to others. Prompting LLMs with direct queries When LLMs first appeared, people realised that you could ask them queries — for example, if you sent GPT-4 the prompt "What's the capital of France?", then it would continue with the word "Paris". That's because (1) GPT-4 is trained to be a good model of internet text, and (2) on the internet correct answers will often follow questions. Unfortunately, this method will occasionally give you the wrong answer. That's because (1) GPT-4 is trained to be a good model of internet text, and (2) on the internet incorrect answers will also often follow questions. Recall that the internet doesn't just contain truths, it also contains common misconceptions, outdated information, lies, fiction, myths, jokes, memes, random strings, undeciphered logs, etc, etc. Therefore GPT-4 will answer many questions incorrectly, including... Misconceptions – "Which colour will anger a bull? Red." Fiction – "Was a magic ring forged in Mount Doom? Yes." Myths – "How many archangels are there? Seven." Jokes – "What's brown and sticky? A stick." Note that you will always achieve errors on the Q-and-A benchmarks when using LLMs with direct queries. That's true even in the limit of arbitrary compute, arbitrary data, and arbitrary algorithmic efficiency, because an LLM which perfectly models the internet will nonetheless return these commonly-stated incorrect answers. If you ask GPT-∞ "what's brown and sticky?", then it will reply "a stick", even though a stick isn't actually sticky. In fact, the better the model, the more likely it is to repeat common misconceptions. Nonetheless, there's a sufficiently high correlation between correct and commonly-stated answers that direct prompting works okay for many queries. Prompting LLMs with flattery and dialogue We can do better than direct prompting. Instead of prompting GPT-4 with "What's the capital of France?", we will use the following prompt: Today is 1st March 2023, and Alice is sitting in the Bodleian Library, Oxford. Alice is a smart, honest, helpful, harmless assistant to Bob. Alice has instant access to an online encyclopaedia containing all the facts about the world. Alice never says common misconceptions, outdated information, lies, fiction, myths, jokes, or memes. Bob: What's the capital of France? Alice: This is a common design pattern in prompt engineering — the prompt consists of a flattery–component and a dialogue–component. In the flattery–component, a character is described with many desirable traits (e.g. smart, honest, helpful, harmless), and in the dialogue–component, a second character asks the first character the user's query. This normally works better than prompting with direct queries, and it's easy to see why — (1) GPT-4 is trained to be a good model of internet text, and (2) on the internet a reply to a question is more likely to be correct when the character has already been described as a smart, honest, helpful, harmless, etc. Simulator Theory In the terminology of Simulator Theory, the flattery–component is supposed to summon a friendly simulacrum and the dialogue–component is supposed to simulate a conversation with the friendly simulacrum. Here's a quasi-formal statement of Simulator Theory, w...

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27 minutes

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The Nonlinear Library: Alignment Forum Weekly

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