A Survey on Human Preference Learning for Large Language Models

They lack a systematic review ARGE language models (LLMs) [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] have posed a groundbreaking impact on artificial and discussion on preference learning, especially preference intelligence (AI), transforming the opinions of people on the modeling methods, which are critical to capturing human potential of AI systems for understanding and applying human intentions for LM alignment [13]. To further explore effective languages. These neural network language models with largescale preference learning approaches for better LLM alignment, parameters (mainly over 10 billion) are initially pretrained we present a comprehensive review of human preference on large corpora collected from a wide range of learning methods applicable to language models, examining sources, a remarkable part of which is on the Internet [11]. After LLM alignment methods from the perspective of preference pre-training by imitating how humans use natural languages learning. By analyzing a wide range of alignment approaches in the text data, the foundation LLMs acquire strong and within the preference learning framework, we outline the general language skills [1, 12]. On the other hand, foundation holistic picture of introducing human preference into LLMs, LLMs are observed to have difficulty in understanding or enabling insights to be drawn from every aspect of human responding to diverse human instruction appropriately [13], preference learning for various domains.