QuestionAnsweringModelFactory.cs 初始化 AnswersResult 的新实例。 C# publicstaticAzure.AI.Language.QuestionAnswering.AnswersResultAnswersResult(System.Collections.Generic.IEnumerable<Azure.AI.Language.QuestionAnswering.KnowledgeBaseAnswer> answers =default); ...
NamePaperTypeModalities MS-COCO Microsoft COCO: Common Objects in Context Caption Image-Text SBU Captions Im2Text: Describing Images Using 1 Million Captioned Photographs Caption Image-Text Conceptual Captions Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning...
2. "Supported": Does the quotation convince you that the response is accurate? 3. "True": If the response does not contain false information.We think this failure mode and others discussed in our paper can be avoided by enriching the setting, moving from a “single-shot” reply to a ...
two testing sessions at T1, about 1 week apart. Each session lasted approximately 2 h and focused on cognitive assessments using computer-based tasks. In the last part of the second session, a number of paper and pencil tests were also administered, including the CRT (see measures below). ...
They were instructed to fill in the blanks in each text presented in pen and paper format. All participants resided in the US at the time of participation (average duration of US residence was 2.1 years). Answers were scored using the Appropriate Criterion method (Brown 1980) discussed in ...
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There are many language-related tasks such as entering text on your phone, finding news articles you enjoy, or discovering answers to questions that you may have. All these tasks are powered by NLP models. To decide which model to invoke at a particular point in time, we must...
By converting specific tasks to textual instructions, the T5 model can be trained on a variety of tasks during fine-tuning. This method of fine-tuning was extended in the paper “Scaling instruction-finetuned language models”, which introduced more than a thousand tasks during fine-tuning that...
input sequence in one direction. This ultimately allows the gathering of both forward (the past) and backward (the future) information about the sequence at each time step by using the two hidden states combined. For a more detailed explanation, please refer to the original paper on LSTMs[17...
The repository for the survey paper "Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity" Cunxiang Wang1,7*, Xiaoze Liu2*, Yuanhao Yue3*, Qipeng Guo4, Xiangkun Hu4, Xiangru Tang5, Tianhang Zhang6, Cheng Jiayang7, Yunzhi Yao8, Wenyang Gao1,8, Xumin...