Abstract: Systems and methods for prompt tuning can utilize previously-learned prompts for the initialization of tuning for prompts on different tasks that may differ from the task associated with the previously-learned prompt. The prompt being utilized for initialization can be a generic prompt and/or may be a prompt selected based on a determined similarity between two or more task embeddings.

Abstract: The present disclosure provides a novel sentence-level representation learning method Conditional Masked Language Modeling (CMLM) for training on large scale unlabeled corpora. CMLM outperforms the previous state-of-the-art English sentence embedding models, including those trained with (semi-)supervised signals. For multilingual representations learning, it is shown that co-training CMLM with bitext retrieval and cross-lingual natural language inference (NL) fine-tuning achieves state-of-the-art performance. It is also shown that multilingual representations have the same language bias and principal component removal (PCR) can eliminate the bias by separating language identity information from semantics.

Abstract: The present disclosure provides a novel sentence-level representation learning method Conditional Masked Language Modeling (CMLM) for training on large scale unlabeled corpora. CMLM outperforms the previous state-of-the-art English sentence embedding models, including those trained with (semi-)supervised signals. For multilingual representations learning, it is shown that co-training CMLM with bitext retrieval and cross-lingual NLI fine-tuning achieves state-of-the-art performance. It is also shown that multilingual representations have the same language bias and principal component removal (PCR) can eliminate the bias by separating language identity information from semantics.