In-context learning (ICL) performs tasks by prompting a large language m...
Semantic textual similarity (STS) has been a cornerstone task in NLP tha...
Anthropomorphization is the tendency to attribute human-like traits to
n...
Large language models (LLMs) have shown incredible capabilities and
tran...
Data multiplexing is a recently proposed method for improving a model's
...
Extreme classification (XC) involves predicting over large numbers of cl...
Fine-tuning pre-trained language models (PLMs) achieves impressive
perfo...
Multilingual pre-trained models exhibit zero-shot cross-lingual transfer...
In this paper, we propose Semantic Supervision (SemSup) - a unified para...
While recent work on multilingual language models has demonstrated their...
In this paper, we propose a simple and effective technique to allow for
...
While reinforcement learning (RL) has been successful in natural languag...
Recent advances in Generative Adversarial Networks (GANs) have resulted ...
The CLEVR dataset has been used extensively in language grounded visual
...
Learning options that allow agents to exhibit temporally higher order
be...
Sparse reward problems are one of the biggest challenges in Reinforcemen...
Deep Learning has managed to push boundaries in a wide variety of tasks....
Semantic Similarity is an important application which finds its use in m...