전체 글 (116) 썸네일형 리스트형 [논문이해] Block-Skim: Efficient Question Answering for Transformer 논문명: Block-Skim: Efficient Question Answering for Transformer 논문링크: https://arxiv.org/abs/2112.08560 Block-Skim: Efficient Question Answering for Transformer Transformer models have achieved promising results on natural language processing (NLP) tasks including extractive question answering (QA). Common Transformer encoders used in NLP tasks process the hidden states of all input tokens in the c.. [논문이해] What learning algorithm is in-context learning? Investigations with linear models 논문명: What learning algorithm is in-context learning? Investigations with linear models 논문링크: https://arxiv.org/abs/2211.15661 What learning algorithm is in-context learning? Investigations with linear models Neural sequence models, especially transformers, exhibit a remarkable capacity for in-context learning. They can construct new predictors from sequences of labeled examples $(x, f(x))$ prese.. [논문이해] Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers 논문명: Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers 논문링크: https://arxiv.org/abs/2212.10559 Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers Large pretrained language models have shown surprising in-context learning (ICL) ability. With a few demonstration input-label pairs, they can predict t.. [논문이해] Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations 논문명: Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations 논문링크: https://arxiv.org/abs/2205.12685 Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations Despite recent explosion of interests in in-context learning, the underlying mechanism and the precise impact of the quality of demonstrations remain elusive. Intuitively, ground-truth labels should have as.. [개발하며 깨닫는 것] 라이브러리 버전의 중요성 Github 에선 requirements.txt 를 통해서 라이브러리 버전을 알려준다. https://github.com/ArrowLuo/CLIP4Clip GitHub - ArrowLuo/CLIP4Clip: An official implementation for "CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Ret An official implementation for "CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval" - GitHub - ArrowLuo/CLIP4Clip: An official implementation for "CLIP4Clip: A.. [논문이해] Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations 논문명:Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations 논문링크: https://arxiv.org/abs/2212.09865 Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations Although large language models can be prompted for both zero- and few-shot learning, performance drops significantly when no demonstrations are available. In this paper, we introduce Z-ICL, a new zero-shot method that closes th.. [논문이해] Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? 논문명: Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?논문 링크: https://arxiv.org/abs/2202.12837 Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?Large language models (LMs) are able to in-context learn -- perform a new task via inference alone by conditioning on a few input-label pairs (demonstrations) and making predictions for new inputs. Howev.. [논문이해] Noisy Channel Language Model Prompting for Few-Shot Text Classification 논문명: Noisy Channel Language Model Prompting for Few-Shot Text Classification https://arxiv.org/abs/2108.04106 Noisy Channel Language Model Prompting for Few-Shot Text Classification We introduce a noisy channel approach for language model prompting in few-shot text classification. Instead of computing the likelihood of the label given the input (referred as direct models), channel models compute.. 이전 1 ··· 6 7 8 9 10 11 12 ··· 15 다음
