"Researchers Win Award for Study on Text Embedding Privacy Risks"

Four researchers from Cornell Tech won the Outstanding Paper Award at the 2023 Empirical Methods in Natural Language Processing (EMNLP) Conference for their paper titled "Text Embeddings Reveal (Almost) As Much As Text." Their paper delves into privacy concerns regarding text embeddings, a Natural Language Processing (NLP) technique that addresses the challenges posed by the nuanced and ambiguity of words and phrases. Machines can quickly and efficiently understand numbers, but human language is more complicated. Text data is converted to numerical data so that a Machine Learning (ML) algorithm can effectively process it. In some cases, such as with systems involving Large Language Models (LLMs), auxiliary data is stored in a vector database of dense embeddings until it must be retrieved. There are concerns about how private these vector databases are and how much private information about the original text someone with malicious intent could reveal if they tried to reverse engineer text embeddings. This article continues to discuss the award-winning study on text embedding privacy risks.

Cornell Tech reports "Researchers Win Award for Study on Text Embedding Privacy Risks"

Submitted by grigby1