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Found 5 papers, 5 papers with code
Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs
In our experiment, a model is finetuned to output insecure code without disclosing this to the user.
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.
Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs
Chat models, which are finetuned to serve as AI assistants, outperform their corresponding base models on SAD but not on general knowledge tasks.
Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data
As a step towards answering this question, we study inductive out-of-context reasoning (OOCR), a type of generalization in which LLMs infer latent information from evidence distributed across training documents and apply it to downstream tasks without in-context learning.
BridgeHand2Vec Bridge Hand Representation
Contract bridge is a game characterized by incomplete information, posing an exciting challenge for artificial intelligence methods.
