  The potential of large language models (LLMs) to simultaneously perform a
wide range of natural language processing (NLP) tasks has been the subject of
extensive research. Although instruction tuning has proven to be a
data-efficient method for transforming LLMs into such generalist models, their
performance still lags behind specialist models trained exclusively for
specific tasks. In this paper, we investigate whether incorporating
broad-coverage generalist instruction tuning can contribute to building a
specialist model. We hypothesize that its efficacy depends on task specificity
and skill requirements. Our experiments assess four target tasks with distinct
coverage levels, revealing that integrating generalist instruction tuning
consistently enhances model performance when the task coverage is broad. The
effect is particularly pronounced when the amount of task-specific training
data is limited. Further investigation into three target tasks focusing on
different capabilities demonstrates that generalist instruction tuning improves
understanding and reasoning abilities. However, for tasks requiring factual
knowledge, generalist data containing hallucinatory information may negatively
affect the model's performance. Overall, our work provides a systematic guide
for developing specialist models with general instruction tuning. Our code and
other related resources can be found at
https://github.com/DavidFanzz/Generalist_or_Specialist.
