![]() ![]() Meta Quest 3Ĭonsider this a bonus treat for having to wait an extra 30 minutes for the keynote to begin. By that, of course, we mean the metaverse. Are you ready for an update on Meta Quest 3? Didn’t have time to tune in live? That’s okay - we summed up the most important parts from the keynote below. QG-Bench is released along with the fine-tuned models presented in the paper this https URL, which are also available as a demo this https URL.Meta’s annual Connect conference started today, and this means lots of new hardware. ![]() Finally, we analyse both the domain adaptability of these models as well as the effectiveness of multilingual models in languages other than English. Then, we complement automatic evaluation based on standard metrics with an extensive manual evaluation, which in turn sheds light on the difficulty of evaluating QG models. First, we propose robust QG baselines based on fine-tuning generative language models. Using QG-Bench as a reference, we perform an extensive analysis of the capabilities of language models for the task. It includes general-purpose datasets such as SQuAD for English, datasets from ten domains and two styles, as well as datasets in eight different languages. ![]() In this paper, we introduce QG-Bench, a multilingual and multidomain benchmark for QG that unifies existing question answering datasets by converting them to a standard QG setting. However, it is difficult to measure advances in QG research since there are no standardized resources that allow a uniform comparison among approaches. Download a PDF of the paper titled Generative Language Models for Paragraph-Level Question Generation, by Asahi Ushio and Fernando Alva-Manchego and Jose Camacho-Collados Download PDF Abstract:Powerful generative models have led to recent progress in question generation (QG). ![]()
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