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Model alignment with human preferences is an essential step in making LargeLanguage Models (LLMs) helpful and consistent with human values. It typicallyconsists of supervised fine-tuning (SFT) and reinforcement learning from humanfeedback (RLHF) stages. However, RLHF faces inherent limitations stemming froma complex training setup and its tendency to align the model with implicitvalues that end users cannot control at run-time. Moreover, reward models inRLHF stage commonly rely on single-dimensional feedback as opposed to explicit,multifaceted signals that indicate attributes such as helpfulness, humor, andtoxicity. To address these limitations, we propose SteerLM, a supervisedfine-tuning method that empowers end-users to control responses duringinference. SteerLM conditions responses to conform to an explicitly definedmulti-dimensional set of attributes, thereby empowering a steerable AI capableof generating helpful and high-quality responses while maintainingcustomizability. Experiments show that SteerLM trained on open source datasetsgenerates responses that are preferred by human and automatic evaluators tomany state-of-the-art baselines trained with RLHF while being much easier totrain. Try SteerLM at https://huggingface.co/nvidia/SteerLM-llama2-13B

SteerLM: Attribute Conditioned SFT as an (User-Steerable) Alternative to RLHF