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pro vyhledávání: '"HAYES, PETER"'
Retrieval Augmented Generation (RAG) has emerged as an effective solution for mitigating hallucinations in Large Language Models (LLMs). The retrieval stage in RAG typically involves a pre-trained embedding model, which converts queries and passages
Externí odkaz:
http://arxiv.org/abs/2402.12177
As large language models (LLMs) become more capable, fine-tuning techniques for aligning with human intent are increasingly important. A key consideration for aligning these models is how to most effectively use human resources, or model resources in
Externí odkaz:
http://arxiv.org/abs/2402.08114
Autor:
Seeman, Erik R.1 (AUTHOR) seeman@buffalo.edu
Publikováno v:
American Historical Review. Dec2023, Vol. 128 Issue 4, p1876-1877. 2p.
Autor:
Zhang, Mingtian, Key, Oscar, Hayes, Peter, Barber, David, Paige, Brooks, Briol, François-Xavier
Score-based divergences have been widely used in machine learning and statistics applications. Despite their empirical success, a blindness problem has been observed when using these for multi-modal distributions. In this work, we discuss the blindne
Externí odkaz:
http://arxiv.org/abs/2209.07396