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Found 4 papers, 2 papers with code
The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning
To measure these risks of malicious use, government institutions and major AI labs are developing evaluations for hazardous capabilities in LLMs.
Empirical Analysis of the Strengths and Weaknesses of PEFT Techniques for LLMs
As foundation models continue to exponentially scale in size, efficient methods of adaptation become increasingly critical.
HiDDeN: Hiding Data With Deep Networks
We show that these encodings are competitive with existing data hiding algorithms, and further that they can be made robust to noise: our models learn to reconstruct hidden information in an encoded image despite the presence of Gaussian blurring, pixel-wise dropout, cropping, and JPEG compression.
Beating Atari with Natural Language Guided Reinforcement Learning
We introduce the first deep reinforcement learning agent that learns to beat Atari games with the aid of natural language instructions.
