PANDA: Preference Adaptation for Enhancing Domain-Specific Abilities of LLMs

arXiv:2402.12835v1 Announce Type: new
Abstract: While Large language models (LLMs) have demonstrated considerable capabilities across various natural language tasks, they often fall short of the performance achieved by domain-specific state-of-the-art models. One potential approach to enhance domain-specific capabilities of LLMs involves fine-tuning them using corresponding datasets. However, this method can be both resource and time-intensive, and not applicable to closed-source commercial LLMs. In this paper, we propose Preference Adaptation for Enhancing Domain-specific Abilities of LLMs (PANDA), a method designed to …

abstract art arxiv capabilities cs.ai cs.cl datasets domain fine-tuning language language models large language large language models llms natural natural language performance state state-of-the-art models tasks them type