반응형 논문572 [2023-05-03] 오늘의 자연어처리 Automated Paper Screening for Clinical Reviews Using Large Language Models Objective: To assess the performance of the OpenAI GPT API in accurately and efficiently identifying relevant titles and abstracts from real-world clinical review datasets and compare its performance against ground truth labelling by two independent human reviewers. Methods: We introduce a novel workflow using the OpenAI .. 2023. 5. 3. [2023-05-02] 오늘의 자연어처리 A logical word embedding for learning grammar We introduce the logical grammar emdebbing (LGE), a model inspired by pregroup grammars and categorial grammars to enable unsupervised inference of lexical categories and syntactic rules from a corpus of text. LGE produces comprehensible output summarizing its inferences, has a completely transparent process for producing novel sentences, and can lea.. 2023. 5. 2. [2023-05-01] 오늘의 자연어처리 mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality Large language models (LLMs) have demonstrated impressive zero-shot abilities on a variety of open-ended tasks, while recent research has also explored the use of LLMs for multi-modal generation. In this study, we introduce mPLUG-Owl, a novel training paradigm that equips LLMs with multi-modal abilities through modulariz.. 2023. 5. 1. [2023-04-30] 오늘의 자연어처리 AI, write an essay for me: A large-scale comparison of human-written versus ChatGPT-generated essays Background: Recently, ChatGPT and similar generative AI models have attracted hundreds of millions of users and become part of the public discourse. Many believe that such models will disrupt society and will result in a significant change in the education system and information generation in the.. 2023. 4. 30. 이전 1 ··· 63 64 65 66 67 68 69 ··· 143 다음 반응형