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Found 2 papers, 1 papers with code
More RLHF, More Trust? On The Impact of Human Preference Alignment On Language Model Trustworthiness
The surge in Large Language Models (LLMs) development has led to improved performance on cognitive tasks as well as an urgent need to align these models with human values in order to safely exploit their power.
Improving Prototypical Visual Explanations with Reward Reweighing, Reselection, and Retraining
In recent years, work has gone into developing deep interpretable methods for image classification that clearly attributes a model's output to specific features of the data.
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