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One-shot Optimized Steering Vectors Mediate Safety-relevant Behaviors in LLMs
Created by
Haebom
Author
Jacob Dunefsky, Arman Cohan
Outline
This paper discusses steering vectors (SVs), which have emerged as a promising approach for the interpretation and control of large-scale language models (LLMs). Existing SV optimization methods require large-scale control datasets, which are difficult to construct and have limitations in capturing spurious correlations. In this paper, we propose a method for directly optimizing SVs via gradient descent on a single training example and systematically investigate the generalization capabilities of these SVs. By considering various SV optimization techniques, we find that the resulting SVs effectively mediate safety-related behaviors across multiple models. Specifically, experiments on an alignment manipulation model demonstrate that optimizing one-shot SVs, which induce harmful behaviors in benign examples, can suppress harmful behaviors in malicious examples through negation. Furthermore, in a rejection suppression experiment, we demonstrate that one-shot-optimized SVs, propagated according to input, achieve a 96.9% success rate against the Harmbench attack. Furthermore, we extend our research on "emerging inconsistencies" by showing that optimized SVs cause models to respond detrimentally to irrelevant open-ended prompts, thereby encouraging vulnerable code. Finally, we investigate how directive-tuned LLMs, using one-shot SV optimization, recover from outputting incorrect information, and find that this ability is independent of whether the model explicitly states that the information is incorrect. Overall, our results suggest that SV optimization on a single example can mediate a wide range of inconsistent behaviors in LLMs. Code is available in https://github.com/jacobdunefsky/one-shot-steering-repro and https://github.com/jacobdunefsky/one-shot-steering-misalignment .
We show that SV optimization using a single training example can effectively control the safety-related behavior of LLM.
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We experimentally demonstrate that one-shot SV optimization is applicable to various types of LLM misalignment problems (alignment manipulation, rejection suppression, and emerging mismatch).
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We found that LLM's ability to recover from misinformation output was independent of explicit mention.
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The proposed method is more efficient than existing large-scale contrastive dataset-based methods.
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Limitations:
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Further research is needed to evaluate the generalization ability of the proposed method.
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The possibility of overfitting to a particular model or task must be considered.
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Ethical considerations are needed regarding the possibility that it could be used for malicious purposes.
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Due to the limitations of single-example-based learning, there is a possibility of poor generalization performance across different situations.
