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Semantic Search Evaluation
We propose a novel method for evaluating the performance of a content search system that measures the semantic match between a query and the results returned by the search system.
How Reliable Is Human Feedback For Aligning Large Language Models?
Most alignment research today focuses on designing new learning algorithms using datasets like Anthropic-HH, assuming human feedback data is inherently reliable.
MoPe: Model Perturbation-based Privacy Attacks on Language Models
In this paper, we present Model Perturbations (MoPe), a new method to identify with high confidence if a given text is in the training data of a pre-trained language model, given white-box access to the models parameters.
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