Deep Learning

The paper comprehensively evaluates ChatGPT's performance on various academic tasks, covering 140 tasks across diverse fields, highlighting strengths and weaknesses, and introducing a new ability to follow multi-query instructions, ultimately paving the way for practical applications of ChatGPT-like models.

This talk discusses the evolving field of transfer learning, from LSTMs to large language models, and shows new direction on the transferability in large language model.

We introduce xCodeEval, the largest executable multilingual multitask benchmark to date consisting of 25M document-level coding examples from about 7.5 K unique problems covering up to 17 programming languages with execution-level parallelism.

Shows multitask multilingual generalization in language model.

The crystallization of modeling methods around the Transformer architecture has been a boon for practitioners. Simple, well-motivated architectural variations that transfer across tasks and scale, increasing the impact of modeling research. However, …

A talk on T0++ paper.

Over 2,000 prompts for roughly 170 datasets are available through PromptSource framework.

T0 shows zero-shot task generalization on English natural language prompts, outperforming GPT-3 on many tasks, while being 16x smaller!

We propose a trasductive approach for few shot cross-lingual classification.

This talk summarizes the paper [`Finetuned Language Models Are Zero-Shot Learners`](https://arxiv.org/abs/2109.01652).