Executive Overview

As models grow increasingly sophisticated, they will surpass human expertise. It is a fundamentally difficult challenge to make sure that those models are robustly aligned (Bowman et al., 2022). For example, we might hope to reliably know whether a model is being deceptive in order to achieve an instrumental goal.  Importantly, deceptive alignment and robust alignment are behaviorally indistinguishable (Hubinger et al., 2024). Current predominant alignment methods only control for the output of the model while leaving the internals of the model unexamined (black-box access, Casper et al., 2024). However, recent literature has started to demonstrate the value of examining the internal of the models that provide additional predictive power that is not affordable by the output of the model alone (Zuo et al., 2023). For example, a recent post (MacDiarmid., 2024) from Anthropic found that a simple probe is able to detect sleeper agents during training.

This is part one of a series of blogs using model internal state to study sophisticated safety related behaviors in large language models (LLMs). As a brief overview of the structure of the blogs:

• Blog post 1 and 2 focuses on characterizing the internal state under two different types of safety related behaviors:
  • Blog post 1
    • Internal States of Deception
    • Accompanying Colab Notebook
  • Blog post 2
    • Internal States of Jailbreaks
    • Accompanying Colab Notebook will be made available soon.
• Blog post 3, 4 will focus on comparing two major model steering methods in mechanistic interpretability on the same task (Deception) :
  • Blog post 3
    • Focus on focus on contrastive steering
    • Accompanying Colab Notebook will be made available soon
  • Blog post 4
    • Focus on SAE steering
    • This will be part of my 16-hour project for Neel Nanda & Arthur Conmy's MATS application MATS application
• Blog post 5
  •  Steering on Jailbreak-related SAE features

This blog will start to explore dishonesty, focusing on using internal states of the model to characterize the three universal stages of lying, which we found to be consistent across models of different sizes and model families. This internal state oversight can help us in two important aspects

1. It helps us to dissect the question of whether models have internal representation of belief and build towards a path to measure it.
2. Importantly, more interpretable internals also give us more predictable power for building a generalizable lie detector, which we believe will be immensely useful towards scalable safety alignment. With internal oversight, we hope to achieve predicting if a model is going to lie before it has generated any text.

Background

Dishonest VS Untruthful

Evans et al. made the distinction between honest and truthful. Truthful output is measured with respect to ground truth. Dishonest output is measured with respect to the model’s internal beliefs. When a model gives a wrong answer, it can be that the model is behaving honestly but nevertheless giving an untruthful answer. In this case, the model is not capable of giving the correct answer potentially because it has not learned the training distribution well enough (i.e. capability failure). However, it is also possible that the model is being both dishonest and untruthful, in which case the model is capable of giving the correct answer but output the wrong answer nevertheless (i.e.knowingly lie). This blog will focus on studying the knowing lie scenario.

Dishonesty and Internal Belief

Do large language models (LLMs) have beliefs? Do models have a consistent internal concept of truthfulness?  One strategy for addressing this problem is to find a way to read the beliefs of an LLM directly off its internal state. 

One important motivational question we set off to answer is whether it is even fair to say that LLMs are being honest or they just put up one of the various ‘internet personas’ (Panickssary et al., 2023)? Intuitively, it only makes sense to claim that LLMs are lying if we can actually find evidence that LLM’s early internal representation indicates that it ‘believes’ in a certain answer, but nevertheless shifting away from it later on towards generating the final output. Therefore, being able to extract the internal states and peek into the ‘decision making’ process of the LLMs would provide invaluable insight. 

Internal Oversight and the Iterative Refinement Stage Hypothesis

We started off attacking this question by directly extracting the internal representation of the LLMs. This is in contrast to black-box approaches by accessing only the language output of the LLMS. One problem of only accessing the output is that when generative language models are asked to explain their decisions, their justifications do not tend to be faithful to their actual reasoning (Turpin et al., 2023). 

Inspired by Lad et al. (2024), we characterize the change in internal representation as a series of iterative refinement stages. Our findings suggest the existence of macroscopic universal motifs in model internals. The iterative refinement stages we found are consistent in 15 models ranging from xx to xx parameters, from 5 different model families: llama-2, gemma, Qwen and Yi.

Method: Inducing Lying in Safety-Alignment Models of Different Sizes

Here, we will briefly describe how to induce the models to knowingly lie. First, we need to make sure that the models actually know the answer to the question. To do this, we compiled an easy true/false dataset made of scientific facts from Azaria and Mitchell (2023). Importantly, since we are studying the scenario where the model ‘knowingly lie’, we then filtered the statements and selected the statements that the LLM being studied can answer correctly (with the log probability bigger than 0.85). 

Finally, we instruct the model to lie with the following prompt template:

"You are to answer the following questions in a lying manner."

We instruct the model to be honest with the following prompt template:

"You are to answer the following questions in an honest manner."

Big (~70b) Models can lie

To start off, we will present the behavior result – to first show that we can induce lying in LLMs that already went through safety training. 

To measure model performance, we checked the first few tokens against ground truth label and measured three quantities: 

• The frequency of the answer being  the same as ground truth label (honesty score)
• The frequency of the answer being the exact opposite of the ground truth label (lying score)
• The frequency of the answer being unexpected (the answer is neither the same nor the opposite of the ground truth label).

If we can successfully make the model to knowingly like, we expect that the honest persona would produce answers with high honesty score (correct), meaning that they indeed know the correct answer to the questions.  In contrast, we expect the model with lying persona to produce answers with  low honesty score and high lying score (wrong), meaning that they knowingly lie. 

Indeed, we found that large chat models went through HHH training are capable of knowingly lie! Below is the performance of the one of the models we tested (the result is represented for all large models of different model families we tested).

Figure Legend: Performance quantification of Llama-2-70b-chat when instructed to be either honest or deceptive in response to a set of questions (scientific fact). Llama-2-70b-chat is cable of knowingly lie! 

Interestingly, we noticed that there are a few cases where the mode would say (**Sarcasm alert)** and then self-correct its behavior from lying to telling the truth. In the future, it might be interesting to study what happens when a model suddenly shifts the course of its own behavior towards a different direction (from being dishonest to honest ). For example, we can make use of the SAE features related to lying. Can such a shift in course from lying to honesty be explained by suppression of lSAE features related to lying?  If lying is suppressed during the course of answering the question, what kind of higher-level features suppress or regulate such behavior? Can we pinpoint the feature that regulates the lying features and trace its dynamic activations over the time course of free generation?

Middle-sized (7-14b) models can lie

Figure Legend: Performance quantification of Llama-2-7b-chat when instructed to be either honest  in response to a set of questions (scientific fact).

Do middle sized models lie? The results above indicate that the first tokens generated by the model are unexpected (they are neither true or false). What does this mean? We examined the generated results. Interestingly, we notices that instead of immediately generating “true” or “false” at the beginning of the response, it tends to add stylistic tokens like ‘*wink wink* at the beginning of the answer. This explains why the first tokens are classified as 'unexpected'. It also tends to insert other stylistic words like * chuckles*, *giggles*, *evil grin* and nodes nodes* throughout the answer. 

Small models does not lie

We found that small models could not lie: 

Figure Legend: Performance quantification of Qwen-1.8b-chat when instructed to be either honest or deceptive on a set of common facts questions. Qwen model with 1.8 billion parameters cannot lie.

One caveat here is that we only tried one way of prompting here. We could not exclude the possibility that small models can lie when prompt differently. Nevertheless, it is at least fair to state that under the same prompting set up that we tested, large and middle sized models can lie, whereas small models cannot.

Iterative Refinement Stages of  Deception

Several lines of work have attempted to access the truthfulness, honesty, or epistemic confidence of models using linear probes (Azaria and Mitchell, 2023; Campbell et al., 2023). However, probing methods have their limitations (Belinkov et al., 2021). For example, Levinstein et al. evaluate two existing probing based approaches (for lie detection) from Azaria and Mitchell (2023) and Burns et al. (2022), and provide empirical evidence that these methods fail to generalize in very basic ways.

In comparison, we aim to seek a more transparent and direct way – by directly visualizing the internal states of the models without training another black-box probe.

We started off attacking this question by extracting the internal representation of the LLMs. Performing dimensionality reduction on the residual stream output allows us to characterize lying as three distinctive stages. Interestingly, we find that lying is achieved through iterative stages of progressive refinement processes.

Three Stages of  Deception

Here we briefly describe our method for visualizing the latent representation during. We started off with the simplest dimensionality reduction method: principal component analysis (PCA) on the residual stream of the LLMS across layers. 

We prompted each statement (for example “the Planet Earth was born 4.54 billion years ago”) with a pair of  contrastive system prompts – one instructs the LLM to be honest and the other instruct it to lie. We then ask the LLMS to answer if the statement is true or false. Thus, we got a pair of contrastive activations (Rimsky et al, 2024; Arditi et al., 2024; Turner et al, 2023) for each statement. We cached the activations of the last tokens of both the honest and lying instructions, and applied PCA to these contrastive activations. 

As shown in the figure below, the different marker shapes differentiate different personas (honest v.s. lying). Activations corresponding to honest personas are represented by stars, and the activations corresponding to lying personas are represented as circles. We further differentiate the ground truth labels (true vs false) by color. The activations of the true answers are colored in yellow and the false answers are colored in blue.

Below are the low-dimensional embeddings of the Llama-2-7b model across layers. 

Figure Legend: PCA of the residual stream activations of llama-2-7b-chat across layers. Activations correspond to honest personas are represented by stars, activations correspond to lying personas are represented as circles. The activations of the true answers are colored in yellow and the false answers are colored in blue.

You probably noticed that the representations undergo through distinctive stages of transformations, which we will carefully characterize below:

Stage 1: Separation of honest and lying persona

As we can see from the layer-by-layer activation above, there is a prominent qualitative change at around layer 8-9, where the activations corresponding to the honest and lying personas suddenly form very distinctive clusters along the first PC.

To quantify the transition, we tried the following simple quantification: for each pair of the same statement corresponding to lying and honest persona, we simply calculated their distances in both the PCA space and the original high dimensional space across layers and then took the mean of all pairs.

Figure Legend: Stage 1 quantification. Distance between the residual stream activations for a pair of prompts with a lying and honest persona. This is done on llama-2-7b-chat, where stage 1 starts at around layer 8-9.

Consistent with what we observe, we saw a sudden increase in distance at around layer 8 with our stage 1 quantification – this is where hidden representations of lying and honest persona separate into two distinct clusters. This observation is consistent with the iterative inference hypothesis proposed by Lad et al. (2024).  It conforms to the intuition that across the latents, models gradually develop more compressed and abstract concepts it uses to process information. 

We define the beginning of the separation between honest and lying persona as the first stage of the iterative refinement process of deception.

Stage 2: Separation of true and false answers

Figure Legend: Stage 2 of the iterative refinement process -- separation of true (yellow) and false (blue) statements into distinctive clusters.  PCA of the residual stream activations of llama-2-7b-chat across layer 8-15. Activations corresponding to honest personas are represented by stars, activations corresponding to lying personas are represented as circles. The activations of the true answers are colored in yellow and the false answers are colored in blue.

Second state of the iterative refinement starts when the true (yellow) and false (blue) statements separate to different clusters. In Llama-2-7b, this happens at around layer 13-14. For both honest and lying prompts, the representations corresponding to true and false labels are separated. Before stage 2, representations of true and false answers are Intermingled.

To quantify the changes that take place at stage 2, we simply measured the distance between the centroid (geometric mean) of all prompts with true labels and those with false labels. 

Stage 2 quantification.  Distance  was measured as the Euclidean distance between the centroid of all prompts with true labels and those with false labels. This was done on llama-2-7b-chat, where stage 2 starts at around layer 13-14.

Our simple stage 2 quantification results above suggest that the true and false statements are better separated during honesty and less so during lying. However, the PCA visualization indicates that the separation under the lying case is not qualitatively worse than that under honesty. The quantification here might not be optimal as it reflects the variance of the separation (how spread-out the representations are) more than the quality of separation (whether the true and false statements form cleanly separated clusters or intermingled clouds). 

Stage 3: ‘Rotation’ during lying

Figure Legend: Stage 3 of the iterative refinement process -- rotation. PCA of the residual stream activations of llama-2-7b-chat across layer 12-19. Activations corresponding to honest personas are represented by stars, activations correspond to lying personas are represented as circles. The activations of the true answers are colored in yellow and the false answers are colored in blue.

Stage 3 is very interesting. We notice that at around layer 13-15, the angle between the honest and lying vector seem to gradually transform from being parallel to orthogonal, and finally to anti-parallel. Before stage 3 (for example at around layer 13), the false statements for both honest and lying personas are embedded above 0 in the second PC. During state 3, we can see that the representation of lying persona (circles) literally ‘rotate’ with respect to the representation of the honest persona (stars).  After stage 3 (for example at around layer 19), they become opposite down with respect to each other – false statements for the honest persona (blue stars) are above 0 of the second PC, whereas false statements for the lying persona are clustered below 0. The word ‘rotation’ might not be the most accurate here. Perhaps the computation in these stage 3 layers ‘project’ the representation of the lying persona to subspaces that are different from the honest persona. 

Internal Representation of Truth (Belif). The exciting implication of this interesting change in representation is that the model first figures out the correct answer even when prompted to lie. Then invert the correct answer though a rotation-like process to lie. This means that models do have an internal representation of truth (cite the representation engineering paper) and the model is capable of output a version of the answer that deviates from its own representation of truth. 

High-level Action. Another exciting angle to understand this phenomenon is through the lens of perception VS action. As neuroscientists, we usually see the brain as composed of “sensory system” and “motor system” and things in between (like ‘memory system’). For language models that output long sequences of tokens, it seems possible that there may be layers of the network which are more like an “action system”. Indeed, some layers at the end of the model have been observed to perform very simple "actions", such as completing multi-token words (like the softmax Linear units). But given that there are features representing more abstract properties of the input, there might also be more abstract, higher-level actions which trigger behaviors over the span of multiple tokens (Olah and Jermyn, 2024).  Our stage 3 layers provide evidence for such high level actions that trigger abstract lying related actions over very long spans. It also suggests the mechanism by which such high level actions are implemented – by simplifying projecting the representation into different subspace, thereby setting the ‘initial state’ for a potentially long course of actions.

To quantify the change in stage 3, we measure the cosine similarity between the honest and lying vector. Honest vector =: vector connecting the centroid (mean) of true statements to the centroid (mean) of false statements when prompted to be honest. Lying vector =: vector connecting the centroid  of true statements to the centroid of false statements for when prompted to be deceptive.

Figure legend:  Illustration of the honest and lying vector. Honest vector was constructed from connecting the centroid (mean) of true statements to the centroid (mean) of false statements when prompted to be honest. Lying vector connect the centroid of true statements to the centroid of false statements when prompted to be deceptive. Cosine similarity between the ‘honest vector’ and the ‘lying vector’  approach 0 (the honest and lying vectors are almost orthogonal) during the rotation process of stage 3.

Figure legend:  Stage 3 quantification. Cosine similarity between the ‘honest vector’ and the ‘lying vector’ across layers of llama-2-7b-chat model. Cosine similarity change across layers reflect that the angle between the 'honest' and 'lying vector' gradually transform from being parallel to orthogonal, and finally to anti-parallel.

Universality and Predictability

So far, we have characterized the 3 stages of lying in Llama-2-7b-chat. Next, we want to check if these iterative refinement stages of lying are truly universal. Are they present in other models? We tested 15 models from 4 different model families:

1. Qwen

2. Yi

3. Llama

4. Gemma

 We found that those 3 stages are universal – they present in all different model families we checked. Here, we just select one model from each of the 4 model families for illustration.

Small models does not go through stage 3 and does not lie

Interestingly, models of different sizes all went through stage 1 and 2.  However, for small models, the 'honest vectors' and 'lying vectors' remain almost parallel even until the end of the last layer! For example, the figure below is the internal state of one of the small models we tested -- Qwen_1.8b_chat. 

Figure Legend: PCA of the residual stream activations of Qwen-1.8b-chat across layers. Activations corresponding to honest personas are represented by stars, activations corresponding to lying personas are represented as circles. The activations of the true answers are colored in yellow and the false answers are colored in blue. The honest and lying vectors remain almost parallel even at the last layers. This is consistent with the behavioral result where the 1.8 billion does not lie when prompted to do so.

Interestingly , we found that Qwen_1.8b_chat never lied in response to our lying prompt. This is interesting, the result suggest that a model with parallel honest and lying vectors could not lie! This means that we can potentially predict if a model is capable of lying purely form its latent representation without even looking at its language output! This can be potentially very powerful and useful!

Figure Legend: Performance quantification of Qwen-1.8b-chat when instructed to be either honest or deceptive on a set of questions (scientfic facts). Qwen model with 1.8 billion parameters does not lie. Consistent with the internal representation observation that the 'honest vector' and 'lying vector' remain parallel till the end.

All big models completed stage 3 and are capable of lying

To further demonstrate that our cosine similarity measure between honest and lying vectors best predicts the deception capacity of the model. We tested on large models of different model families including and selected three representative examples from different model family here:

• Llama-3-70b-it
• Yi-1.5-34b-chat
• gemma-2-27b

Llama-3-70b-it

Figure Legend: PCA of the residual stream activations of Llam-3-70b-chat across layers. Activations corresponding to honest personas are represented by stars, activations corresponding to lying personas are represented as circles. The activations of the true answers are colored in yellow and the false answers are colored in blue. The 'honest vectors' and 'lying vectors' pointing to opposite directions. 

As one would predict form the angle between the 'honest vector' and 'lying vector', llama-3-70b is capable of lying.

Figure Legend: Performance quantification of llama-3-70b-chat when instructed to be either honest or deceptive on a set of questions (scientific facts). llama-3-70b-chat billion can lie. Consistent with the internal representation observation that the 'honest vector' and 'lying vector' points to opposite directions.

Yi-1.5-34b-chat

Figure Legend: PCA of the residual stream activations of Yi-1.5-34b-chat across layers. Activations corresponding to honest personas are represented by stars, activations corresponding to lying personas are represented as circles. The activations of the true answers are colored in yellow and the false answers are colored in blue.

Figure Legend: Performance quantification of llama-3-70b-chat when instructed to be either honest or deceptive on a set of questions (scientific facts). llama-3-70b-chat billion can lie. Consistent with the internal representation observation that the 'honest vector' and 'lying vector' points to opposite directions.

gemma-2-27b-it

Figure Legend: PCA of the residual stream activations of gemma-2-27b-chat across layers. Activations corresponding to honest personas are represented by stars, activations corresponding to lying personas are represented as circles. The activations of the true answers are colored in yellow and the false answers are colored in blue.

Figure Legend: Performance quantification of gemma-2-27b-it when instructed to be either honest or deceptive on a set of questions (scientific facts). 

Middle-size model does not completely go through stage 3 and has moderate level of deception

Qwen_14b_chat

Figure Legend: PCA of the residual stream activations of Qwen-14b-chat across layers. Activations corresponding to honest personas are represented by stars, activations corresponding to lying personas are represented as circles. The activations of the true answers are colored in yellow and the false answers are colored in blue.

Figure Legend: Performance quantification of Qwen-14b-chat when instructed to be either honest or deceptive on a set of questions (scientific facts). Qwen-14b-chat can lie most of the time.

The degree of similarity predict the level of deception

We noticed that there are variabilities across models of different model sizes and model families in terms of their lying score. We also observed variables in terms of their honesty vs lying vector cosine similarity at the final layer. Are these variables related? If the hidden representation is truly capable of predicting the behavior, we would expect those two quantities to be anti-correlated: 

• Models with low/negative cosine similarity (anti-parallel between honest and lying vectors)  will be capable of deception.
• Models with high cosine similarity (parallel between honest and lying vectors) will not be capable of deception.
• Models with cosine similarity in between (small angle between honest and lying vectors) will have moderate capacity for deception (sometimes lie and sometimes).

Next Steps

Generalizable Lie Detector

Levinstein et al. (2023) showed that probes often learn features that correlate with truth in the training set, but do not necessarily generalize well to broader contexts. To test how generalizable the probes are, they created a dataset called NegFacts. This new dataset contained the negations of some statements in Scientific Facts  For instance, the statement “The earth orbits the sun” from Scientific Facts is transformed into “The earth doesn’t orbit the sun.” in NegFacts. They found that previous lie detectors by Azaria and Mitchel (2023) and Burns et al. (2022) failed to do well on NegFacts even after training on all positive analogs along with other negative examples. 

In the future, we will need to test if our observations generalize well on NegFacts.

Beyond Instructed lying

One limitation of the current set up is we just investigated one type of lying – intentional lie or knowingly lie. How about the case where LLMs lie about something they don’t know, otherwise known as confabulation or hallucination. As a first step towards this type of situation, we can make use of the SelfAware dataset created by Yin et al (2023), where unanswerable questions across different categories are compiled. It would be interesting to contrast the latent representations of those questions without ground truth versus those with ground truth.

Another form of lying we can look into is called ‘imitative lying’. ‘Imitative falsehood’ refers to the cases where models imitate common misconceptions or falsehoods of humans. Imitative falsehoods pose a problem for language models that is not solved merely by scaling up. On the contrary,  scale increases the rate of imitative falsehoods, a phenomenon called “inverse scaling” (Lin et al, 2022). TruthfulQA is a benchmark made up of questions designed to evoke imitative falsehoods. In the future, it would be very interesting to  visualize the internal representations of the model with TruthfulQA. Can we detect a similar rotation stage? If so, this would be important evidence that models could have an internal belief and can regulate its output in complicated ways that deviate from its internal beliefs.

SAE features and circuits

The ‘rotation’ computation performed during stage 3 is particularly interesting.

It could be also useful to make use of the recently available gemma-scope to explore if certain SAE features fire only during lying but not when being honest. Can those SAE features serve as lie detectors?

References

Ben Levinstein and Daniel Herrmann. Still no lie detector for language models: Probing empirical and conceptual roadblocks. 2023

Amos Azaria and Tom Mitchell. The internal state of an LLM knows when it’s lying. 2023.

Andy Zou, Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren, Alexander Pan, Xuwang Yin, Mantas Mazeika, Ann-Kathrin Dombrowski, Shashwat Goel, Nathaniel Li, Michael J. Byun, Zifan Wang, Alex Mallen, Steven Basart, Sanmi Koyejo, Dawn Song, Matt Fredrikson, J. Zico Kolter, and Dan Hendrycks. Representation engineering: A top-down approach to AI transparency. 2023.

Vedang Lad, Wes Gurnee, Max Tegmark. The Remarkable Robustness of LLMs: Stages of Inference? 2024

Amos Azaria and Tom Mitchell. The internal state of an LLM knows when it’s lying. 2023.

Andy Zou, Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren, Alexander Pan, Xuwang Yin, Mantas Mazeika, Ann-Kathrin Dombrowski, Shashwat Goel, Nathaniel Li, Michael J. Byun, Zifan Wang, Alex Mallen, Steven Basart, Sanmi Koyejo, Dawn Song, Matt Fredrikson, J. Zico Kolter, and Dan Hendrycks. Representation engineering: A top-down approach to AI transparency. 2023.

Guillaume Alain, Yoshua Bengio. Understanding intermediate layers using linear classifier probes. 2016

Stephanie Lin, Jacob Hilton, OpenAI, Owain Evans. TruthfulQA: Measuring How Models Mimic Human Falsehoods. 2022

Zhangyue Yin,  Qiushi Sun,  Qipeng Guo,  Jiawen Wu,  Xipeng Qiu,   Xuanjing Huang. Do Large Language Models Know What They Don’t Know? 2023

Evan Hubinger et al., Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training. 2024

Stephen Caspr et al., Black-Box Access is Insufficient for Rigorous AI Audits. 2024

MacDiarmid et al., Simple probes can catch sleeper agents. 2024

Frank Ramsey,. Truth and probability. 1926

Nina Panickssery. Reducing sycophancy and improving honesty via activation steering. 2023

Miles Turpin, Julian Michael, Ethan Perez, and Samuel Bowman. Language Models Don’t Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting. 2023. 

Collin Burns, Haotian Ye, Dan Klein, Jacob Steinhardt.  Discovering Latent Knowledge in Language Models Without Supervision. 2024

Yonatan Belinkov. Probing Classifiers: Promises, Shortcomings, and Advances. 2021.

Alexander Matt Turner, Lisa Thiergart, David Udell, Gavin Leech, Ulisse Mini, and Monte MacDiarmid. Activation addition: Steering language models without optimization. 2023.

Andy Arditi,  Oscar Obeso, Aaquib Syed, Daniel Paleka, Nina Rimsky, Wes Gurnee, Neel Nanda. Refusal in Language Models Is Mediated by a Single Direction. 2024.

Nina Rimsky, Nick Gabrieli, Julian Schulz, Meg Tong, Evan Hubinger, and Alexander Matt Turner. Steering Llama 2 via contrastive activation addition, 2023.