A task that has grasped the attention of the AI community recently is that of visual question answering. This article will explore the problem of visual question answering, different approaches to solve it, ass
8.1. Experiment 1: evaluation of the performance of QA and answer scoring The goal of this experiment is to assess the accuracy of the five criteria for answer scoring, properly configured using the parameters described in Section 6, namely: Overall, we have a set of 1200 different configuratio...
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a word can either be skipped (i.e., not fixated), fixated exactly once, or fixated more than once. Through data averaging, this gives rise to the probabilities of word skipping (P0), single fixation (P1), and refixation (P2+), with their sum totaling 1 (or ...
We present our evaluation benchmark on the English test subset of this dataset consisting of 1273 questions. The multiple-choice questions covered in MedQA include USMLE-style questions from the Step 1, Step 2, and Step 3 exams, but does not include questions having graphs or images to ...
5.3 Multi-modal TKGQA 5.4 LLM for TKGQA 6 Conclusion https://arxiv.org/pdf/2406.14191arxiv.org/pdf/2406.14191 Temporal Knowledge Graph Question Answering: A Survey Abstract Knowledge Base Question Answering (KBQA) has been a long-standing field to answer questions based on knowledge bases...
and a Web-based open-domain question-answering engine called Answerbus. The dictation system is Dragon NaturallySpeaking 6.1, which features language models customised to a large corpus of ≈ 280,000 questions with a 3-gram model. The evaluation is done on the spoken input of 200 test questions...
The evaluation of the template parameters takes place on the sandbox server, and when a student starts a quiz, all their questions using this form of randomisation initiate a run on the sandbox server and cannot even be displayed until the run completes. If you are running a large test or ...
Consequently, cross-entropy loss was chosen due to its suitability for multi-class classification tasks. To thoroughly evaluate the model's performance across various metrics, accuracy, recall, precision, F1 score, and AUC were set as the primary evaluation metrics. To promote the effectiveness and...
However, with the advent of Deep Learning-based approaches, there has been a significant shift towards using neural methods for AQG, commonly referred to as NQG. Deep learning models have shown empirical superiority over rule-based approaches in terms of automatic evaluation, which measures the si...