224 citations · 1.1k across the 33 of their papers we have counts for
6 papers · 1 filter
GPT-3-driven pedagogical agents for training children's curious question-asking skills
Rania Abdelghani, Yen-Hsiang Wang, Xingdi Yuan +4
In order to train children's ability to ask curiosity-driven questions, previous research has explored designing specific exercises relying on providing semantic and linguistic cue…
Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generation
Xingdi Yuan, Tong Wang, Yen-Hsiang Wang +5
Large Language Models (LLMs) have in recent years demonstrated impressive prowess in natural language generation. A common practice to improve generation diversity is to sample mul…
General-to-Specific Transfer Labeling for Domain Adaptable Keyphrase Generation
Rui Meng, Tong Wang, Xingdi Yuan +2
Training keyphrase generation (KPG) models require a large amount of annotated data, which can be prohibitively expensive and often limited to specific domains. In this study, we f…
Automatic Exploration of Textual Environments with Language-Conditioned Autotelic Agents
Laetitia Teodorescu, Eric Yuan, Marc-Alexandre Côté +1
In this extended abstract we discuss the opportunities and challenges of studying intrinsically-motivated agents for exploration in textual environments. We argue that there is imp…
Asking for Knowledge: Training RL Agents to Query External Knowledge Using Language
Iou-Jen Liu, Xingdi Yuan, Marc-Alexandre Côté +2
To solve difficult tasks, humans ask questions to acquire knowledge from external sources. In contrast, classical reinforcement learning agents lack such an ability and often resor…
One-Shot Learning from a Demonstration with Hierarchical Latent Language
Nathaniel Weir, Xingdi Yuan, Marc-Alexandre Côté +5
Humans have the capability, aided by the expressive compositionality of their language, to learn quickly by demonstration. They are able to describe unseen task-performing procedur…