NLP/논문이해 (62) 썸네일형 리스트형 [논문이해] RA-CLIP: Retrieval Augmented Contrastive Language-Image Pre-Training 논문명: RA-CLIP: Retrieval Augmented Contrastive Language-Image Pre-Training논문 링크: https://paperswithcode.com/paper/ra-clip-retrieval-augmented-contrastive Papers with Code - RA-CLIP: Retrieval Augmented Contrastive Language-Image Pre-TrainingNo code available yet.paperswithcode.com 핵심만 정리합니다문제점CLIP은 대성공한 방법론인데, 데이터를 많이 필요로 하고 모든 데이터를 파라미터로 기억하기엔 방대함online retrieval 을 통해서 지식을 보강하면 되는 방법론 'RA-CLIP'을.. [논문이해] RETRIEVAL-ENHANCED CONTRASTIVE VISION-TEXT MODELS 논문명: RETRIEVAL-ENHANCED CONTRASTIVE VISION-TEXT MODELS논문 링크: https://arxiv.org/abs/2306.07196 Retrieval-Enhanced Contrastive Vision-Text ModelsContrastive image-text models such as CLIP form the building blocks of many state-of-the-art systems. While they excel at recognizing common generic concepts, they still struggle on fine-grained entities which are rare, or even absent from the pre-trainin.. [논문이해] Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned 논문명: Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned논문 링크: https://arxiv.org/abs/1905.09418 Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be PrunedMulti-head self-attention is a key component of the Transformer, a state-of-the-art architecture for neural machine translation. In this work we evaluate t.. [논문이해] DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models 논문명: DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models논문 링크: https://arxiv.org/abs/2309.03883 DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language ModelsDespite their impressive capabilities, large language models (LLMs) are prone to hallucinations, i.e., generating content that deviates from facts seen during pretraining. We propose a simple.. [논문이해] Contrastive Decoding: Open-ended Text Generation as Optimization 논문명: Contrastive Decoding: Open-ended Text Generation as Optimization논문 링크: https://arxiv.org/abs/2210.15097 Contrastive Decoding: Open-ended Text Generation as OptimizationGiven a language model (LM), maximum probability is a poor decoding objective for open-ended generation, because it produces short and repetitive text. On the other hand, sampling can often produce incoherent text that drifts.. [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] LORA-FA: MEMORY-EFFICIENT LOW-RANK ADAPTATION FOR LARGE LANGUAGE MODELS FINE-TUNING 논문명: LORA-FA: MEMORY-EFFICIENT LOW-RANK ADAPTATION FOR LARGE LANGUAGE MODELS FINE-TUNING논문 링크: https://arxiv.org/abs/2308.03303 LoRA-FA: Memory-efficient Low-rank Adaptation for Large Language Models Fine-tuningThe low-rank adaptation (LoRA) method can largely reduce the amount of trainable parameters for fine-tuning large language models (LLMs), however, it still requires expensive activation m.. [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 .. 이전 1 2 3 4 ··· 8 다음
