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Mar 26, 2024 · Our work is the first attempt to systematically compare how text generation models using zero-shot and one-shot learning compare to more ...
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The proposed method requires much less parallel data than what is typically used to build a domain independent system, which makes it easy, cheap and efficient ...
Text classification. Using clustering, LLMs can classify text with similar meanings or sentiments. Uses include measuring customer sentiment, determining the ...
This article presents a comprehensive review of representative work and recent progress in the NLP field and introduces the taxonomy of pre-trained models.
Evolving Domain Adaptation of Pretrained Language Models for Text Classification ... based adaptation strategies: one utilizes self-training with pseudo ...
Domain-adaptive pre-training (or DA-training for short), also known as post-training, aims to train a pre-trained general-purpose language model (LM) using ...
This paper presents an empirical study on four techniques of language model adaptation, including a maximum a posteriori (MAP) method and three ...
May 24, 2024 · Our new framework, AGREE, takes a holistic approach to adapt LLMs for better grounding and citation generation, combining both learning-based ...
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A large language model (LLM) is a computational model capable of language generation or other natural language processing tasks. As language models, LLMs ...
This work investigates the use of natural language to enable zero-shot model adaptation to new tasks, using text and metadata from social commenting platforms.
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