NVIDIA AI Unveils SteerLM: A New Artificial Intelligence Method that Allows Users to Customize the Responses of Large Language Models (LLMs) During Inference

In the ever-evolving landscape of artificial intelligence, there has long been a challenge that plagues developers and users alike: the need for more customized and nuanced responses from large language models. While these models, such as Llama 2, can generate human-like text, they often need to provide answers genuinely tailored to individual users’ unique requirements. The existing approaches, such as supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF), have their limitations, leading to responses that could be more mechanical and complex.

NVIDIA Research has unveiled SteerLM, a groundbreaking technique that promises to address these challenges. SteerLM provides a novel and user-centric approach to customizing the responses of large language models, offering more control over their outputs by allowing users to define key attributes that guide the model’s behavior.

SteerLM operates through a four-step supervised fine-tuning process that simplifies the customization of large language models. First, it trains an Attribute Prediction Model using human-annotated datasets to evaluate qualities like helpfulness, humor, and creativity. Next, it utilizes this model to annotate diverse datasets, enhancing the variety of data accessible to the language model. Then, SteerLM employs attribute-conditioned supervised fine-tuning, training the model to generate responses based on specified attributes, such as perceived quality. Finally, it refines the model through bootstrap training, rendering diverse responses and fine-tuning for optimal alignment.

One of the standout features of SteerLM is its real-time adjustability, allowing users to fine-tune attributes during inference, catering to their specific needs on the fly. This remarkable flexibility opens the door to various potential applications, from gaming and education to accessibility. With SteerLM, companies can serve multiple teams with personalized capabilities from a single model, avoiding the need to rebuild models for each distinct application.

SteerLM’s simplicity and user-friendliness are evident in its metrics and performance. SteerLM 43B outperformed existing RLHF models like ChatGPT-3.5 and Llama 30B RLHF on the Vicuna benchmark in experiments. By offering a straightforward fine-tuning process that requires minimal changes to infrastructure and code, SteerLM delivers exceptional results with less hassle, making it a formidable advancement in the field of AI customization.

NVIDIA is taking a significant step forward in democratizing advanced customization by releasing SteerLM as open-source software within its NVIDIA NeMo framework. Developers now have the opportunity to access the code and try out this technique with a customized 13B Llama 2 model, available on platforms like Hugging Face. Detailed instructions are also provided for those interested in training their SteerLM model.

As large language models continue to evolve, the need for solutions like SteerLM becomes increasingly essential to deliver AI that is not just intelligent but also genuinely helpful and aligned with user values. With SteerLM, the AI community takes a significant step forward in the quest for more customized and adaptable AI systems, ushering in a new era of bespoke artificial intelligence.

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