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Generative AI

Does entity abstraction help generative Transformers reason?
We study the utility of incorporating entity type abstractions into pre-trained Transformers and test these methods on four NLP tasks …
A Planning based Neural-Symbolic Approach for Embodied Instruction Following
The ALFRED environment features an embodied agent following instructions and accomplishing tasks in simulated home environments. …
Data Augmentation for Intent Classification with Off-the-shelf Large Language Models
Data augmentation is a widely employed technique to alleviate the problem of data scarcity. In this work, we propose a prompting-based …
Explainable, Sensible and Virtuous Workplace Chatbots
We outline three research directions towards the practical implementation of explainable, sensible and virtuous chatbots for the …
S-LLM: Semi-Supervised Large Language Model for Chat Summarization
As producing high-quality summaries of chat dialogues currently requires large labeled datasets, we propose a method to efficiently …
TACTiS: Transformer-Attentional Copulas for Time Series
The estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. …
RaVAEn: Unsupervised Change Detection of Extreme Events Using ML On-Board Satellites
Applications such as disaster management enormously benefit from rapid availability of satellite observations. Traditionally, data …
Overcoming challenges in leveraging GANs for few-shot data augmentation
In this paper, we explore the use of GAN-based few-shot data augmentation as a method to improve few-shot classification performance. …
TACTiS: Transformer-Attentional Copulas for Time Series
The estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. …