KTO: Model Alignment as Prospect Theoretic Optimization
<|reference_start|>KTO: Model Alignment as Prospect Theoretic Optimization: Kahneman & Tversky's $\textit{prospect theory}$ tells us that humans perceive random variables in a biased but well-defined manner (1992); for example, humans are famously loss-averse. We show that objectives for aligning LLMs with human feedback implicitly incorporate many of these biases -- the success of these objectives (e.g., DPO) over cross-entropy minimization can partly be ascribed to them belonging to a family of loss functions that we call $\textit{human-aware losses}$ (HALOs). However, the utility functions these methods attribute to humans still differ from those in the prospect theory literature. Using a Kahneman-Tversky model of human utility, we propose a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, as current methods do. We call this approach KTO, and it matches or exceeds the performance of preference-based methods at scales from 1B to 30B, despite only learning from a binary signal of whether an output is desirable. More broadly, our work suggests that there is no one HALO that is universally superior; the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.<|reference_end|>
@article{ethayarajh2024kto:,
    title={KTO: Model Alignment as Prospect Theoretic Optimization},
    author={Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff, Dan Jurafsky, Douwe
  Kiela},
    journal={arXiv preprint arXiv:2402.01306},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.01306},
    primaryClass={cs.LG cs.AI}
}