Due to the cumbersome nature of human evaluation and limitations of code-based
evaluation, Large Language Models (LLMs) are increasingly being used to assist humans in …

AI technologies have been incorporated into many end-user applications. However,
expectations of the capabilities of such systems vary among people. Furthermore, bloated …

Model building in machine learning is an iterative process. The performance analysis and
debugging step typically involves a disruptive cognitive switch from model building to error …

How can we design Natural Language Processing (NLP) systems that learn from human
feedback? There is a growing research body of Human-in-the-loop (HITL) NLP frameworks …

The in-context learning capabilities of LLMs like GPT-3 allow annotators to customize an
LLM to their specific tasks with a small number of examples. However, users tend to include …

Applications and systems are constantly faced with decisions that require picking from a set
of actions based on contextual information. Reinforcement-based learning algorithms such …

Tools for Interactive Machine Learning (IML) enable end users to update models in a “rapid,
focused, and incremental”—yet local—manner. In this work, we study the question of local …

We introduce explanatory guided learning (XGL), a novel interactive learning strategy in
which a machine guides a human supervisor toward selecting informative examples for a …

In personal assistant dialog systems, intent models are classifiers that identify the intent of a
user utterance, such as to add a meeting to a calendar or get the director of a stated movie …

In this paper, we present the current state-of-the-art of decision making (DM) and machine
learning (ML) and bridge the two research domains to create an integrated approach of …