ChatGPT and GPT-4 have attracted substantial interest from both academic and
industrial circles, owing to their remarkable few-shot (or even zero-shot)
ability to handle various tasks. Recent work shows that, after being fine-tuned
with a few sets of instruction-driven data, the recently proposed LLM, LLaMa,
exhibits an impressive capability to address a broad range of tasks. However,
the zero-shot performance of LLMs does not consistently outperform that of
models fined-tuned for specific scenarios. To explore whether the capabilities
of LLMs can be further enhanced for specific scenarios, we choose the
writing-assistance scenario as the testbed, including seven writing tasks. We
collect training data for these tasks, reframe them in an instruction-following
format, and subsequently refine LLaMa via instruction tuning. Experimental
results show that continually fine-tuning LLaMa on writing instruction data
significantly improves its ability on writing tasks. We also conduct more
experiments and analyses to offer insights for future work on effectively
fine-tuning LLaMa for specific scenarios.

本文研究了使用指令驱动数据 fine-tune 的 LLMa 模型在写作场景下的性能，结果表明持续地使用写作数据对 LLMa 进行微调，可以显著提高它在写作任务上的表现，并为未来 LlMa 在特定场景中的微调提供了洞见。