Retriever (4) 썸네일형 리스트형 [논문이해] ConvGQR: Generative Query Reformulation for Conversational Search 논문명: ConvGQR: Generative Query Reformulation for Conversational Search논문 링크: https://arxiv.org/abs/2305.15645 ConvGQR: Generative Query Reformulation for Conversational SearchIn conversational search, the user's real search intent for the current turn is dependent on the previous conversation history. It is challenging to determine a good search query from the whole conversation context. To avoi.. [논문이해] REPLUG: Retrieval-Augmented Black-Box Language Models 논문명: REPLUG: Retrieval-Augmented Black-Box Language Models논문링크: https://arxiv.org/abs/2301.12652 REPLUG: Retrieval-Augmented Black-Box Language ModelsWe introduce REPLUG, a retrieval-augmented language modeling framework that treats the language model (LM) as a black box and augments it with a tuneable retrieval model. Unlike prior retrieval-augmented LMs that train language models with special .. [논문이해] Lost in the Middle: How Language Models Use Long Contexts 논문명: Lost in the Middle: How Language Models Use Long Contexts 논문 링크: https://arxiv.org/abs/2307.03172 Lost in the Middle: How Language Models Use Long Contexts While recent language models have the ability to take long contexts as input, relatively little is known about how well they use longer context. We analyze the performance of language models on two tasks that require identifying relevant.. [논문이해] Pre-Training to Learn in Context 논문명: Pre-Training to Learn in Context 논문링크: https://arxiv.org/abs/2305.09137 Pre-Training to Learn in Context In-context learning, where pre-trained language models learn to perform tasks from task examples and instructions in their contexts, has attracted much attention in the NLP community. However, the ability of in-context learning is not fully exploited becau arxiv.org 아이디어만 정리합니다. 아이디어 기존 .. 이전 1 다음
