In fact, the log loss is not perfectly correlated with accuracy in classification tasks. the model’s capability is high, but poor accuracy on the test set. For a simple concrete example, say we train a bird classifier to classify birds as either "sparrows" or "robins" and we use log loss (which measures the difference between the predicted probability distribution of the model and the true distribution) as the training objective, even though our ultimate goal is a high classification accuracy. It asks the question “is that objective function consistent with our intentions?” and refers to the extent to which a model's goals and behavior align with human values and expectations. If the model is able to accurately predict the movement of stock prices over time, it would be considered to have a high level of capability for this task.Īlignment, on the other hand, is concerned with what we actually want the model to do versus what it is being trained to do. For example, a model designed to predict stock market prices might have an objective function that measures the accuracy of the model's predictions. A model's capability is typically evaluated by how well it is able to optimize its objective function, the mathematical expression that defines the goal of the model. In the context of machine learning, the term capability refers to a model's ability to perform a specific task or set of tasks. Capability vs Alignment in Large Language Models "alignment vs capability" can be thought of as a more abstract analogue of "accuracy vs precision" We will conclude by looking at some of the limitations of this methodology. We are going to examine GPT-3's limitations and how they stem from its training process, before learning how RLHF works and understand how ChatGPT uses RLHF to overcome these issues. The creators use a particular technique called Reinforcement Learning from Human Feedback (RLHF), which uses human feedback in the training loop to minimize harmful, untruthful, and/or biased outputs. The creators have used a combination of both Supervised Learning and Reinforcement Learning to fine-tune ChatGPT, but it is the Reinforcement Learning component specifically that makes ChatGPT unique. It represents the next generation in OpenAI's line of Large Language Models, and it is designed with a strong focus on interactive conversations. Similarly to many Large Language Models, ChatGPT is capable of generating text in a wide range of styles and for different purposes, but with remarkably greater precision, detail, and coherence. ChatGPT is the latest language model from OpenAI and represents a significant improvement over its predecessor GPT-3.
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