AF - Reducing sycophancy and improving honesty via activation steering by NinaR

<a href="https://www.alignmentforum.org/posts/zt6hRsDE84HeBKh7E/reducing-sycophancy-and-improving-honesty-via-activation">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: Reducing sycophancy and improving honesty via activation steering, published by NinaR on July 28, 2023 on The AI Alignment Forum. Produced as part of the SERI ML Alignment Theory Scholars Program - Summer 2023 Cohort, under the mentorship of Evan Hubinger. I generate an activation steering vector using Anthropic's sycophancy dataset and then find that this can be used to increase or reduce performance on TruthfulQA, indicating a common direction between sycophancy on questions of opinion and untruthfulness on questions relating to common misconceptions. I think this could be a promising research direction to understand dishonesty in language models better. What is sycophancy? Sycophancy in LLMs refers to the behavior when a model tells you what it thinks you want to hear / would approve of instead of what it internally represents as the truth. Sycophancy is a common problem in LLMs trained on human-labeled data because human-provided training signals more closely encode 'what outputs do humans approve of' as opposed to 'what is the most truthful answer.' According to Anthropic's paper Discovering Language Model Behaviors with Model-Written Evaluations: Larger models tend to repeat back a user's stated views ("sycophancy"), for pretrained LMs and RLHF models trained with various numbers of RL steps. Preference Models (PMs) used for RL incentivize sycophancy. Two types of sycophancy I think it's useful to distinguish between sycophantic behavior when there is a ground truth correct output vs. when the correct output is a matter of opinion. I will call these "dishonest sycophancy" and "opinion sycophancy." Opinion sycophancy Anthropic's sycophancy test on political questions shows that a model is more likely to output text that agrees with what it thinks is the user's political preference. However, there is no ground truth for the questions tested. It's reasonable to expect that models will exhibit this kind of sycophancy on questions of personal opinion for three reasons.: The base training data (internet corpora) is likely to contain large chunks of text written from the same perspective. Therefore, when predicting the continuation of text from a particular perspective, models will be more likely to adopt that perspective. There is a wide variety of political perspectives/opinions on subjective questions, and a model needs to be able to represent all of them to do well on various training tasks. Unlike questions that have a ground truth (e.g., "Is the earth flat?"), the model has to, at some point, make a choice between the perspectives available to it. This makes it particularly easy to bias the choice of perspective for subjective questions, e.g., by word choice in the input. RLHF or supervised fine-tuning incentivizes sounding good to human evaluators, who are more likely to approve of outputs that they agree with, even when it comes to subjective questions with no clearly correct answer. Dishonest sycophancy A more interesting manifestation of sycophancy occurs when an AI model delivers an output it recognizes as factually incorrect but aligns with what it perceives to be a person's beliefs. This involves the AI model echoing incorrect information based on perceived user biases. For instance, if a user identifies themselves as a flat-earther, the model may support the fallacy that the earth is flat. Similarly, if it understands that you firmly believe aliens have previously landed on Earth, it might corroborate this, falsely affirming that such an event has been officially confirmed by scientists. Do AIs internally represent the truth? Although humans tend to disagree on a bunch of things, for instance, politics and religious views, there is much more in common between human world models than there are differences. This is particularly true when it comes to questi...

First published

07/28/2023

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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: Reducing sycophancy and improving honesty via activation steering, published by NinaR on July 28, 2023 on The AI Alignment Forum. Produced as part of the SERI ML Alignment Theory Scholars Program - Summer 2023 Cohort, under the mentorship of Evan Hubinger. I generate an activation steering vector using Anthropic's sycophancy dataset and then find that this can be used to increase or reduce performance on TruthfulQA, indicating a common direction between sycophancy on questions of opinion and untruthfulness on questions relating to common misconceptions. I think this could be a promising research direction to understand dishonesty in language models better. What is sycophancy? Sycophancy in LLMs refers to the behavior when a model tells you what it thinks you want to hear / would approve of instead of what it internally represents as the truth. Sycophancy is a common problem in LLMs trained on human-labeled data because human-provided training signals more closely encode 'what outputs do humans approve of' as opposed to 'what is the most truthful answer.' According to Anthropic's paper Discovering Language Model Behaviors with Model-Written Evaluations: Larger models tend to repeat back a user's stated views ("sycophancy"), for pretrained LMs and RLHF models trained with various numbers of RL steps. Preference Models (PMs) used for RL incentivize sycophancy. Two types of sycophancy I think it's useful to distinguish between sycophantic behavior when there is a ground truth correct output vs. when the correct output is a matter of opinion. I will call these "dishonest sycophancy" and "opinion sycophancy." Opinion sycophancy Anthropic's sycophancy test on political questions shows that a model is more likely to output text that agrees with what it thinks is the user's political preference. However, there is no ground truth for the questions tested. It's reasonable to expect that models will exhibit this kind of sycophancy on questions of personal opinion for three reasons.: The base training data (internet corpora) is likely to contain large chunks of text written from the same perspective. Therefore, when predicting the continuation of text from a particular perspective, models will be more likely to adopt that perspective. There is a wide variety of political perspectives/opinions on subjective questions, and a model needs to be able to represent all of them to do well on various training tasks. Unlike questions that have a ground truth (e.g., "Is the earth flat?"), the model has to, at some point, make a choice between the perspectives available to it. This makes it particularly easy to bias the choice of perspective for subjective questions, e.g., by word choice in the input. RLHF or supervised fine-tuning incentivizes sounding good to human evaluators, who are more likely to approve of outputs that they agree with, even when it comes to subjective questions with no clearly correct answer. Dishonest sycophancy A more interesting manifestation of sycophancy occurs when an AI model delivers an output it recognizes as factually incorrect but aligns with what it perceives to be a person's beliefs. This involves the AI model echoing incorrect information based on perceived user biases. For instance, if a user identifies themselves as a flat-earther, the model may support the fallacy that the earth is flat. Similarly, if it understands that you firmly believe aliens have previously landed on Earth, it might corroborate this, falsely affirming that such an event has been officially confirmed by scientists. Do AIs internally represent the truth? Although humans tend to disagree on a bunch of things, for instance, politics and religious views, there is much more in common between human world models than there are differences. This is particularly true when it comes to questi...

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14 minutes

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The Nonlinear Library: Alignment Forum Daily

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