52 citations · 94 across the 4 of their papers we have counts for
5 papers
The Stack: 3 TB of permissively licensed source code
Denis Kocetkov, Raymond Li, Loubna Ben Allal +10
Large Language Models (LLMs) play an ever-increasing role in the field of Artificial Intelligence (AI)--not only for natural language processing but also for code understanding and…
Evaluating the Text-to-SQL Capabilities of Large Language Models
Nitarshan Rajkumar, Raymond Li, Dzmitry Bahdanau
We perform an empirical evaluation of Text-to-SQL capabilities of the Codex language model. We find that, without any finetuning, Codex is a strong baseline on the Spider benchmark…
T3-Vis: a visual analytic framework for Training and fine-Tuning Transformers in NLP
Raymond Li, Wen Xiao, Lanjun Wang +2
Transformers are the dominant architecture in NLP, but their training and fine-tuning is still very challenging. In this paper, we present the design and implementation of a visual…
byteSteady: Fast Classification Using Byte-Level n-Gram Embeddings
Xiang Zhang, Alexandre Drouin, Raymond Li
This article introduces byteSteady -- a fast model for classification using byte-level n-gram embeddings. byteSteady assumes that each input comes as a sequence of bytes. A represe…
DuoRAT: Towards Simpler Text-to-SQL Models
Torsten Scholak, Raymond Li, Dzmitry Bahdanau +2
Recent neural text-to-SQL models can effectively translate natural language questions to corresponding SQL queries on unseen databases. Working mostly on the Spider dataset, resear…