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The bibliographic database (without full-text dissertations) is known as Dissertation Abstracts or Dissertation Abstracts International. PQDT annually publishes more than 90% of all dissertations submitted from accredited institutions of higher learning in North America as well as from colleges and universities in Europe and Asia.
A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation.LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text.
An open thesis, also known as an open dissertation, is a thesis that is freely available for members of the public to access upon publication, and often also during the planning and writing process. The decision to write an open thesis is made by the author, who will usually explain their rationale for creating an open thesis as part of the ...
A thesis as a collection of articles [1] or series of papers, [2] also known as thesis by published works, [1] or article thesis, [3] is a doctoral dissertation that, as opposed to a coherent monograph, is a collection of research papers with an introductory section consisting of summary chapters. Other less used terms are "sandwich thesis" and ...
The Pile is an 886.03 GB diverse, open-source dataset of English text created as a training dataset for large language models (LLMs). It was constructed by EleutherAI in 2020 and publicly released on December 31 of that year. [1] [2] It is composed of 22 smaller datasets, including 14 new ones. [1]
This page in a nutshell: Avoid using large language models (LLMs) to write original content or generate references. LLMs can be used for certain tasks (like copyediting, summarization, and paraphrasing) if the editor has substantial prior experience in the intended task and rigorously scrutinizes the results before publishing them.
Apart from the a possibility that saving an LLM output may cause verbatim non-free content to be carried over to the article, these models can produce derivative works. For example, an LLM can rephrase a copyrighted text using fewer, the same, or more words than the original – editors should mind the distinction between a summary and an ...
CoT examples can be generated by LLM themselves. In "auto-CoT", [46] a library of questions are converted to vectors by a model such as BERT. The question vectors are clustered. Questions nearest to the centroids of each cluster are selected. An LLM does zero-shot CoT on each question. The resulting CoT examples are added to the dataset.
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