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Gecko: Versatile Text Embeddings Distilled from Large Language Models
On the Massive Text Embedding Benchmark (MTEB), Gecko with 256 embedding dimensions outperforms all existing entries with 768 embedding size.
Content Conditional Debiasing for Fair Text Embedding
We achieve fairness while maintaining utility trade-off by ensuring conditional independence between sensitive attributes and text embeddings conditioned on the content.
An Investigation of how Label Smoothing Affects Generalization
In this paper, as a step towards understanding why label smoothing is effective, we propose a theoretical framework to show how label smoothing provides in controlling the generalization loss.
Learning Not to Learn in the Presence of Noisy Labels
Learning in the presence of label noise is a challenging yet important task: it is crucial to design models that are robust in the presence of mislabeled datasets.
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