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Revolutionizing Alcohol Use Counseling with Virtual Agents: The Power of LLMs

Natural Language Processing Generative Pretrained Transformers Large Language Models Artificial Intelligence

Published on July 18, 2024 by Daniel Hofheinz

In today's fast-paced world, access to effective counseling services, particularly for issues like alcohol use, can be a challenge. Many people struggle with substance abuse but find it difficult to seek help due to stigma, limited resources, or even geographical barriers. However, recent advancements in technology are opening new doors for support. One exciting development comes from the use of large language models (LLMs) in creating virtual agents that can conduct motivational interviewing (MI) for alcohol use counseling.

So, what exactly is motivational interviewing, and how can a virtual agent help? Motivational interviewing is a client-centered counseling style that encourages individuals to explore and resolve their ambivalence about changing their behavior. It’s designed to facilitate conversations that empower individuals, making them feel understood and supported. Imagine having a conversation with someone who truly listens, empathizes, and encourages you to reflect on your choices. That’s...

Revolutionizing Language Model Alignment: The Power of Iterative Nash Policy Optimization

Natural Language Processing Machine Learning Generative Pretrained Transformers Large Language Models Artificial Intelligence Reinforcement Learning

Published on July 18, 2024 by Daniel Hofheinz

In an age where artificial intelligence increasingly shapes our daily lives, ensuring that large language models (LLMs) align with human preferences is more critical than ever. Enter Iterative Nash Policy Optimization (INPO), a groundbreaking approach that promises to refine how we teach machines to communicate effectively and ethically with humans.

Traditional methods of Reinforcement Learning with Human Feedback (RLHF) have made significant strides in aligning LLMs to better understand and meet human needs. Most of these methods rely on reward-based systems, often following the Bradley-Terry (BT) model. While this has worked to some extent, these systems may not fully capture the intricate nature of human preferences. Imagine trying to describe your favorite dish: it’s not just about the ingredients, but also the ambiance, the memories associated with it, and much more. Similarly, the preferences we hold are mu...

Revolutionizing Alcohol Use Counseling with Virtual Agents: The Power of LLMs

Revolutionizing Language Model Alignment: The Power of Iterative Nash Policy Optimization

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