Large Language Model (6) 썸네일형 리스트형 [논문이해] SELF-DISCOVER: Large Language Models Self-Compose Reasoning Structures 논문명: SELF-DISCOVER: Large Language Models Self-Compose Reasoning Structures 논문 링크: https://arxiv.org/abs/2402.03620 Self-Discover: Large Language Models Self-Compose Reasoning Structures We introduce SELF-DISCOVER, a general framework for LLMs to self-discover the task-intrinsic reasoning structures to tackle complex reasoning problems that are challenging for typical prompting methods. Core to .. [논문이해] Active Retrieval Augmented Generation 논문명: Active Retrieval Augmented Generation 논문링크: https://arxiv.org/abs/2305.06983 Active Retrieval Augmented Generation Despite the remarkable ability of large language models (LMs) to comprehend and generate language, they have a tendency to hallucinate and create factually inaccurate output. Augmenting LMs by retrieving information from external knowledge resources is one arxiv.org 아이디어만 정리합니다.. [논문이해] SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization 논문명: SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization 논문링크: https://arxiv.org/abs/2212.10465 SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization We present SODA: the first publicly available, million-scale high-quality social dialogue dataset. In contrast to most existing crowdsourced, small-scale dialogue corpora, we distill 1.5.. [논문이해] Query2doc: Query Expansion with Large Language Models 논문명: Query2doc: Query Expansion with Large Language Models 논문링크: https://arxiv.org/abs/2303.07678 Query2doc: Query Expansion with Large Language Models This paper introduces a simple yet effective query expansion approach, denoted as query2doc, to improve both sparse and dense retrieval systems. The proposed method first generates pseudo-documents by few-shot prompting large language models (LLM.. [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] LARGER LANGUAGE MODELS DO IN-CONTEXT LEARNING DIFFERENTLY 논문명: LARGER LANGUAGE MODELS DO IN-CONTEXT LEARNING DIFFERENTLY https://arxiv.org/abs/2303.03846 Larger language models do in-context learning differently We study how in-context learning (ICL) in language models is affected by semantic priors versus input-label mappings. We investigate two setups-ICL with flipped labels and ICL with semantically-unrelated labels-across various model families (GP.. 이전 1 다음
