PANDA: Preference Adaptation for Enhancing Domain-Specific Abilities of
  LLMs
An Liu Zonghan Yang Zhenhe Zhang Qingyuan Hu Peng Li Ming Yan Ji Zhang Fei Huang Yang Liu
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 augment the
domain-specific capabilities of LLMs by leveraging insights from the response
preference of expert models without requiring fine-tuning. Our experimental
results reveal that PANDA significantly enhances the domain-specific ability of
LLMs on text classification and interactive decision tasks. Moreover, LLM with
PANDA even outperforms the expert model that being learned on 4 tasks of
ScienceWorld. This finding highlights the potential of exploring tuning-free
approaches to achieve weak-to-strong generalization.