A single safety demonstration appended at inference time mitigates many-shot jailbreak attacks by counteracting implicit malicious fine-tuning on harmful examples.
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Fine-tuning shows higher proficiency than in-context learning on in-distribution generalization in formal languages, with equal out-of-distribution performance and diverging inductive biases at high proficiency.
The paper surveys definitions, techniques, applications, and challenges in in-context learning for large language models.
citing papers explorer
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Mitigating Many-shot Jailbreak Attacks with One Single Demonstration
A single safety demonstration appended at inference time mitigates many-shot jailbreak attacks by counteracting implicit malicious fine-tuning on harmful examples.
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Fine-tuning vs. In-context Learning in Large Language Models: A Formal Language Learning Perspective
Fine-tuning shows higher proficiency than in-context learning on in-distribution generalization in formal languages, with equal out-of-distribution performance and diverging inductive biases at high proficiency.
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A Survey on In-context Learning
The paper surveys definitions, techniques, applications, and challenges in in-context learning for large language models.