An important challenge in interactive machine learning, particularly in subjective or
ambiguous domains, is fostering bi-directional alignment between humans and models …

Ensuring robust safety measures across a wide range of scenarios is crucial for user-facing
systems. While Large Language Models (LLMs) can generate valuable data for safety …

When assisting people in daily tasks, robots need to accurately interpret visual cues and
respond effectively in diverse safety-critical situations, such as sharp objects on the floor. In …

Counterspeech can be an effective method for battling hateful content on social media.
Automated counterspeech generation can aid in this process. Generated counterspeech …

General-purpose automatic speech recognition (ASR) systems do not always perform well in
goal-oriented dialogue. Existing ASR correction methods rely on prior user data or named …

In the rapidly evolving landscape of multimodal interactive technologies, a critical gap
persists in their utility and reach across diverse user populations. These technologies, while …

We present MOCHA, a novel interactive system designed to enhance data annotation in
natural language processing. MOCHA integrates active learning with counterfactual data …

Supervised learning is based in the premise that models can effectively solve tasks by
learning from numerous examples, mapping inputs to outputs through iterative learning …

Supervised learning requires a lot of annotated data, which makes the annotation process
time-consuming and expensive. Active Learning (AL) offers a promising solution by reducing …