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Notes on machine learning evaluation
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Revealed Rationality: Label-Free Evaluation and Regularization from Representation Theorems
The paper suggests using representation theorems in decision theory to evaluate and regularize Large Language Models (LLMs) and other AI systems. This approach checks if the model's behavior complies with certain…
Auditing the Risk Claims of Distributional Reinforcement Learning
In a study titled 'Auditing the Risk Claims of Distributional Reinforcement Learning', Hari Prasad investigates whether the risk claims made by distributional reinforcement learning agents are accurate. The study finds…
Inherited Circuits, Learned Semantics: How Fine-Tuning Creates Evasion Vulnerabilities Invisible to Standard Evaluation
The study reveals that Language Models (LLMs) fine-tuned for security classification can overlook vulnerabilities introduced during fine-tuning itself. These models may learn specific token-level indicators which…
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