Large-scale language modeling and natural language prompting have demonstrated exciting capabilities for few and zero shot learning in NLP.
Conversational agents and assistants have been used for decades to facilitate learning.
High-fidelity, AI-based simulated classroom systems enable teachers to rehearse effective teaching strategies.
Using recent advances in uncertainty quantification, we evaluated our conversational agent for usability and analyzed the practicality of incorporating a human-in-the-loop approach for data collection and system evaluation for a mathematical questioning scenario.
3 code implementations • • Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Fevry, Jason Alan Fries, Ryan Teehan, Tali Bers, Stella Biderman, Leo Gao, Thomas Wolf, Alexander M. Rush
Large language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020).
We propose using probabilistic modeling of annotator labeling to generate active learning examples to further label the data.
Meta-learning researchers face two fundamental issues in their empirical work: prototyping and reproducibility.