Search Results for author:
Found 5 papers, 3 papers with code
Tell me about yourself: LLMs are aware of their learned behaviors
Note that while we finetune models to exhibit behaviors like writing insecure code, we do not finetune them to articulate their own behaviors -- models do this without any special training or examples.
Inference-Time-Compute: More Faithful? A Research Note
We refer to these models as Inference-Time-Compute (ITC) models.
Looking Inward: Language Models Can Learn About Themselves by Introspection
We study introspection by finetuning LLMs to predict properties of their own behavior in hypothetical scenarios.
Failures to Find Transferable Image Jailbreaks Between Vision-Language Models
These results stand in stark contrast to existing evidence of universal and transferable text jailbreaks against language models and transferable adversarial attacks against image classifiers, suggesting that VLMs may be more robust to gradient-based transfer attacks.
Bias-Augmented Consistency Training Reduces Biased Reasoning in Chain-of-Thought
Moreover, this model generalizes to other forms of bias, reducing biased reasoning on held-out biases by an average of 37%.
