no code implementations • 29 Mar 2024 • Jinhyuk Lee, Zhuyun Dai, Xiaoqi Ren, Blair Chen, Daniel Cer, Jeremy R. Cole, Kai Hui, Michael Boratko, Rajvi Kapadia, Wen Ding, Yi Luan, Sai Meher Karthik Duddu, Gustavo Hernandez Abrego, Weiqiang Shi, Nithi Gupta, Aditya Kusupati, Prateek Jain, Siddhartha Reddy Jonnalagadda, Ming-Wei Chang, Iftekhar Naim
On the Massive Text Embedding Benchmark (MTEB), Gecko with 256 embedding dimensions outperforms all existing entries with 768 embedding size.
no code implementations • ECNLP (ACL) 2022 • Sajjad Beygi, Maryam Fazel-Zarandi, Alessandra Cervone, Prakash Krishnan, Siddhartha Reddy Jonnalagadda
We observe that transformer based models such as UnifiedQA-T5 can be fine-tuned to perform logical reasoning (such as numerical and categorical attributes' comparison) over attributes that been seen in training time (e. g., accuracy of 90\%+ for comparison of smaller than $k_{\max}$=5 values over heldout test dataset).
1 code implementation • NAACL 2022 • Jiacheng Xu, Siddhartha Reddy Jonnalagadda, Greg Durrett
Conditional neural text generation models generate high-quality outputs, but often concentrate around a mode when what we really want is a diverse set of options.