Embeddings for Preferences, Not Semantics
This article introduces new research on "Embeddings for Preferences, Not Semantics," exploring novel applications of AI embeddings beyond their traditional semantic uses. The authors, Carter Blair and 2 others, delve into how these embeddings can capture and represent user preferences more effectively. This could lead to more personalized AI systems and recommendations.
A new research paper titled "Embeddings for Preferences, Not Semantics" has been released by Carter Blair and two co-authors. This work explores a novel application of AI embeddings, moving beyond their traditional use in representing semantic meaning. The focus is on leveraging embeddings to capture and express user preferences.
The paper, submitted to arXiv, is available in PDF format. Readers can also access experimental HTML and TeX Source versions. The research falls under the cs.AI category, indicating its relevance to artificial intelligence.
Various bibliographic and citation tools are available for this paper, including NASA ADS, Google Scholar, and Semantic Scholar. These tools facilitate the exploration of related works and author citations.
Additionally, code, data, and media associated with the article are accessible through platforms such as alphaXiv, CatalyzeX Code Finder, DagsHub, and Huggingface. Experimental demos are also available on Replicate and Hugging Face Spaces.
The arXivLabs framework, which supports such innovative collaborations, allows community collaborators to develop and share new features on the arXiv website. This initiative emphasizes openness, community, excellence, and user data privacy, partnering only with organizations that uphold these values. The "Embeddings for Preferences, Not Semantics" paper exemplifies the kind of cutting-edge research fostered by this environment.
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