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Found 6 papers, 4 papers with code
Gradient-Based Language Model Red Teaming
Red teaming is a common strategy for identifying weaknesses in generative language models (LMs), where adversarial prompts are produced that trigger an LM to generate unsafe responses.
Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training
We find that such backdoor behavior can be made persistent, so that it is not removed by standard safety training techniques, including supervised fine-tuning, reinforcement learning, and adversarial training (eliciting unsafe behavior and then training to remove it).
Question Decomposition Improves the Faithfulness of Model-Generated Reasoning
As large language models (LLMs) perform more difficult tasks, it becomes harder to verify the correctness and safety of their behavior.
Measuring Faithfulness in Chain-of-Thought Reasoning
Large language models (LLMs) perform better when they produce step-by-step, "Chain-of-Thought" (CoT) reasoning before answering a question, but it is unclear if the stated reasoning is a faithful explanation of the model's actual reasoning (i. e., its process for answering the question).
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy
However, while some adoption of DP has happened in industry, attempts to apply DP to real world complex ML models are still few and far between.
Private Ad Modeling with DP-SGD
A well-known algorithm in privacy-preserving ML is differentially private stochastic gradient descent (DP-SGD).
