The prevalence of large language models (LLM) has necessitated an efficient method of customizing these systems to align with organizational values and provide reliable and accurate customer experiences. However, with customization comes the challenge of obtaining diverse, subjective human feedback to refine the model’s performance, which can be time-consuming and unscalable.
To overcome these hurdles, companies leverage reward modeling, a technique that encodes subjective quality standards into a model that guides the LLM to produce preferable outcomes. This is done by training the model to predict a human preference score from the LLM’s response and utilizing this score to evaluate the model’s performance against organizational standards.
To initiate reward modeling, companies train an LLM to generate multiple responses to diverse prompts. These responses are ranked by humans, thus capturing the subjective preferences of model behavior. Over time, patterns emerge from these subjective ratings, which can then be used to train a reward model that appeals to the majority.
Organizations use language tools such as Amazon SageMaker to collect data, store the dataset, run distributed training, and provide an environment for model training. A specialized trainer, or CustomTrainer, calculates the model’s performance based on the subjective feedback embedded on the reward model.
Once the reward model is trained, it is used to evaluate the LLM’s responses. The numerical score the model produces can be used as a threshold for deciding whether to share the response from the LLM with the end-user. In this way, companies can automate the process of filtering out content that doesn’t align with their values, such as harmful, inappropriate, or “toxic” content.
As organizations develop and change, the reward functions and models must also evolve, reflecting the changing values and priorities of the company. Through reward modeling, companies harness a powerful tool for shaping AI systems that resonate with their brand identity and meet customer expectations. The methods described in this article are further detailed in an accompanying coding notebook.
The use of such refined AI models is set to grow in businesses aiming to deliver a more sophisticated and personalized customer experience. These models empower organizations to ensure that their AI solutions align with their values and deliver the unique experiences that their customers demand.
