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This paper introduces a pioneering methodology, termed StructTuning, to efficiently transform foundation Large Language Models (LLMs) into domain specialists.
Language models are few-shot learners
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Barack’s wife hillary: Using knowledge-graphs for fact-aware language modeling
Robert L Logan IV, Nelson F Liu, Matthew E Peters, Matt Gardner, and Sameer Singh. 2019 · 1906
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Ctrl: A conditional transformer language model for controllable generation
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A neural probabilistic language model
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Attention is all you need
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Retrieval-augmented generation for knowledge-intensive nlp tasks
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Ernie 2.0: A continual pre-training framework for language understanding
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A dataset of information-seeking questions and answers anchored in research papers
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Domain-specific language model pretraining for biomedical natural language processing
Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon. 2021 · 2021
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Lora: Low-rank adaptation of large language models
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Understanding retrieval augmentation for long-form question answering
A survey of large language models
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Multi-view content-aware indexing for long document retrieval
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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Adapting large language models via reading comprehension
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Unifying large language models and knowledge graphs: A roadmap
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Multilingual large language model: A survey of resources, taxonomy and frontiers
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