The Nonlinear Library AF - Owain Evans on Situational Awareness and Out-of-Context Reasoning in LLMs by Michaël Trazzi
Aug 24, 2024
08:33
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: Owain Evans on Situational Awareness and Out-of-Context Reasoning in LLMs, published by Michaël Trazzi on August 24, 2024 on The AI Alignment Forum.
Owain Evans is an AI Alignment researcher, research associate at the Center of Human Compatible AI at UC Berkeley, and now leading a new AI safety research group.
In this episode we discuss two of his recent papers, "Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs" (LW) and "Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data" (LW), alongside some Twitter questions.
Below are some highlighted quotes from our conversation (available on Youtube, Spotify, Apple Podcast). For the full context for each of these quotes, you can find the accompanying transcript.
Situational Awareness
Definition
"What is situational awareness? The idea is the model's kind of self-awareness, that is its knowledge of its own identity, and then its awareness of its environment. What are the basic interfaces that it is connected to? [...] And then there's a final point with situational awareness, which is, can the model use knowledge of its identity and environment to take rational actions?"
"Situational awareness is crucial for an AI system acting as an agent, doing long-term planning. If you don't understand what kind of thing you are, your capabilities and limitations, it's very hard to make complicated plans. The risks of AI mostly come from agentic models able to do planning."
Motivation
"We wanted to measure situational awareness in large language models with a benchmark similar to Big Bench or MMLU. The motivation is that situational awareness is important for thinking about AI risks, especially deceptive alignment, and we lacked ways to measure and break it down into components."
"Situational awareness is relevant to any situation where the model needs to do agentic long-term planning. [...] A model confused about itself and its situation would likely struggle to pull off such a strategy."
On Claude 3 Opus Insightful Answers
"Let me explain [the Long Monologue task]. Most of our dataset is typical multiple-choice question answering, but we added a task where models write long answers describing themselves and their situation. The idea is to see if the model can combine different pieces of information about itself coherently and make good inferences about why we're asking these questions.
Claude 3 Opus was particularly insightful, guessing it might be part of a research study testing self-awareness in LLMs. These were true inferences not stated in the question. The model was reading between the lines, guessing this wasn't a typical ChatGPT-style interaction.
I was moderately surprised, but I'd already seen Opus be very insightful and score well on our benchmark. It's worth noting we sample answers with temperature 1, so there's some randomness. We saw these insights often enough that I don't think it's just luck. Anthropic's post-training RLHF seems good at giving the model situational awareness. The GPT-4 base results were more surprising to us."
What Would Saturating The Situational Awareness Benchmark Imply For Safety And Governance
"If models can do as well or better than humans who are AI experts, who know the whole setup, who are trying to do well on this task, and they're doing well on all the tasks including some of these very hard ones, that would be one piece of evidence. [...] We should consider how aligned it is, what evidence we have for alignment. We should maybe try to understand the skills it's using."
"If the model did really well on the benchmark, it seems like it has some of the skills that would help with deceptive alignment. This includes being able to reliably work out when it's being evaluated by humans, when it has a lot of oversight, and when it needs to...
Owain Evans is an AI Alignment researcher, research associate at the Center of Human Compatible AI at UC Berkeley, and now leading a new AI safety research group.
In this episode we discuss two of his recent papers, "Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs" (LW) and "Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data" (LW), alongside some Twitter questions.
Below are some highlighted quotes from our conversation (available on Youtube, Spotify, Apple Podcast). For the full context for each of these quotes, you can find the accompanying transcript.
Situational Awareness
Definition
"What is situational awareness? The idea is the model's kind of self-awareness, that is its knowledge of its own identity, and then its awareness of its environment. What are the basic interfaces that it is connected to? [...] And then there's a final point with situational awareness, which is, can the model use knowledge of its identity and environment to take rational actions?"
"Situational awareness is crucial for an AI system acting as an agent, doing long-term planning. If you don't understand what kind of thing you are, your capabilities and limitations, it's very hard to make complicated plans. The risks of AI mostly come from agentic models able to do planning."
Motivation
"We wanted to measure situational awareness in large language models with a benchmark similar to Big Bench or MMLU. The motivation is that situational awareness is important for thinking about AI risks, especially deceptive alignment, and we lacked ways to measure and break it down into components."
"Situational awareness is relevant to any situation where the model needs to do agentic long-term planning. [...] A model confused about itself and its situation would likely struggle to pull off such a strategy."
On Claude 3 Opus Insightful Answers
"Let me explain [the Long Monologue task]. Most of our dataset is typical multiple-choice question answering, but we added a task where models write long answers describing themselves and their situation. The idea is to see if the model can combine different pieces of information about itself coherently and make good inferences about why we're asking these questions.
Claude 3 Opus was particularly insightful, guessing it might be part of a research study testing self-awareness in LLMs. These were true inferences not stated in the question. The model was reading between the lines, guessing this wasn't a typical ChatGPT-style interaction.
I was moderately surprised, but I'd already seen Opus be very insightful and score well on our benchmark. It's worth noting we sample answers with temperature 1, so there's some randomness. We saw these insights often enough that I don't think it's just luck. Anthropic's post-training RLHF seems good at giving the model situational awareness. The GPT-4 base results were more surprising to us."
What Would Saturating The Situational Awareness Benchmark Imply For Safety And Governance
"If models can do as well or better than humans who are AI experts, who know the whole setup, who are trying to do well on this task, and they're doing well on all the tasks including some of these very hard ones, that would be one piece of evidence. [...] We should consider how aligned it is, what evidence we have for alignment. We should maybe try to understand the skills it's using."
"If the model did really well on the benchmark, it seems like it has some of the skills that would help with deceptive alignment. This includes being able to reliably work out when it's being evaluated by humans, when it has a lot of oversight, and when it needs to...