Optimization within Latent Spaces

Abstract: Large language models are good at learning semantic latent spaces, and the resulting contextual embeddings from these models serve as powerful representations of information. In this talk, I present two novel uses of semantic distances in these latent spaces. In the first part, I introduce BERTScore, an algorithm designed to measure the similarity between machine translation outputs and human gold standards. BERTScore approximates a form of transport distance to match tokens in the generated and human text. In the …

abstract algorithm embeddings good information language language models large language large language models machine novel optimization part semantic serve spaces talk