利用顺序指令微调大语言模型
Fine-tuning Large Language Models with Sequential Instructions
AI总结：
本文提出顺序指令微调方法，通过构建任务链并自动化生成顺序指令，提升大语言模型在编程、数学和开放式生成等复杂多步任务中的指令遵循能力，并推出 SeqEval 基准加以验证。
摘要：
尽管现有指令微调模型取得了成功，我们发现它们通常难以响应包含多条指令的查询。这削弱了它们在解决方案由多个中间任务组成的复杂问题上的表现。因此，我们认为微调数据混合中的一部分应当是顺序性的——包含一系列相互关联的任务链。我们首先从任务驱动的角度处理顺序指令微调，为多语言和视觉问答手动创建可解释的中间任务：即“先翻译后预测”和“先描述后回答”。接下来，我们通过将现有数据集（如 Alpaca 和 FlanCoT）中的指令转化为多样化且复杂的顺序指令来自动化这一过程，使我们的方法具有通用性。经过我们顺序指令微调的模型在编程、数学和开放式生成方面表现出改进的结果。此外，我们提出了一个名为 SeqEval 的新基准，用于评估模型遵循序列中所有指令的能力，这进一步证实了我们微调方法的优势。我们希望我们的努力将为复杂任务的指令微调开辟新的研究途径。
英文摘要：
Despite the success of existing instruction-tuned models, we find that they usually struggle to respond to queries with multiple instructions. This impairs their performance in complex problems whose solution consists of multiple intermediate tasks. Thus, we contend that part of the fine-tuning data mixture should be sequential--containing a chain of interrelated tasks. We first approach sequential instruction tuning from a task-driven perspective, manually creating interpretable intermediate tasks for multilingual and visual question answering: namely "translate then predict" and "caption then answer". Next, we automate this process by turning instructions in existing datasets (e.g., Alpaca and FlanCoT) into diverse and complex sequential instructions, making our method general-purpose. Models that underwent our sequential instruction tuning show improved results in coding, maths, and open-ended generation. Moreover, we put forward a new benchmark named SeqEval to evaluate a model's ability to follow all the instructions in a sequence, which further corroborates the benefits of our fine-tuning method. We hope that our endeavours will open new research avenues on instruction tuning for complex tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
