Scaling Data Diversity for Fine-Tuning Language Models in Human Alignment

arXiv:2403.11124v1 Announce Type: new
Abstract: Alignment with human preference prevents large language models (LLMs) from generating misleading or toxic content while requiring high-cost human feedback. Assuming resources of human annotation are limited, there are two different ways of allocating considered: more diverse PROMPTS or more diverse RESPONSES to be labeled. Nonetheless, a straightforward comparison between their impact is absent. In this work, we first control the diversity of both sides according to the number of samples for fine-tuning, which can …

abstract alignment annotation arxiv cost cs.ai cs.cl data data diversity diverse diversity feedback fine-tuning human human feedback language language models large language large language models llms prompts resources responses scaling type