面向特定场景的LLaMA多任务指令调优：写作辅助初步研究
Multi-Task Instruction Tuning of LLaMa for Specific Scenarios: A Preliminary Study on Writing Assistance
AI总结：
本研究以写作辅助场景（含七项写作任务）为测试平台，通过收集重构指令格式训练数据对LLaMA进行多任务指令调优，验证了该方法可显著提升模型写作任务能力，并为特定场景LLM微调提供参考。
摘要：
专有大语言模型（LLM）如ChatGPT，因在处理各类任务方面的卓越能力受到广泛关注。近期研究表明，开源的小型基础模型，例如7B规模的LLaMA，在使用指令驱动数据进行微调后，也能在处理多样化任务时展现出出色的能力。在本研究中，我们探讨了一个实际问题场景：核心聚焦于一项或少数几项特定任务，而非通用指令遵循，并探究大语言模型能否在此类定向场景中发挥作用并得到进一步提升。我们选取写作辅助场景作为测试平台，该场景包含七项写作任务。我们收集了这些任务的训练数据，将其重构为指令遵循格式，随后通过指令调优对大语言模型（具体为LLaMA）进行优化。实验结果表明，在写作指令数据上微调LLaMA可显著提升其在写作任务上的能力。我们还开展了更多实验与分析，为未来针对特定场景有效微调LLaMA的工作提供思路。最后，我们结合调优所需的工作量与部署过程中消耗的资源，讨论了仅针对一项定向任务使用大语言模型的必要性。
英文摘要：
Proprietary Large Language Models (LLMs), such as ChatGPT, have garnered significant attention due to their exceptional capabilities in handling a diverse range of tasks. Recent studies demonstrate that open-sourced smaller foundational models, such as 7B-size LLaMA, can also display remarkable proficiency in tackling diverse tasks when fine-tuned using instruction-driven data. In this work, we investigate a practical problem setting where the primary focus is on one or a few particular tasks rather than general-purpose instruction following, and explore whether LLMs can be beneficial and further improved for such targeted scenarios. We choose the writing-assistant scenario as the testbed, which includes seven writing tasks. We collect training data for these tasks, reframe them in an instruction-following format, and subsequently refine the LLM, specifically LLaMA, via instruction tuning. Experimental results show that 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. Finally, we initiate a discussion regarding the necessity of employing LLMs for only one targeted task, taking into account the efforts required for tuning and the resources consumed during deployment.
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