Alpaca-COT

美洲驼是一部伟大的作品，展示了惊人的零射和少射能力。它大大降低了训练、精细操作和使用竞争性大型语言模型的成本，即LLaMA-13B优于GPT-3(175B)，LLaMA-65B具有PaLM-540B竞争力。最近，为了提高美洲驼的指令跟随能力，斯坦福羊驼对由自我指令技术产生的52k指令跟随数据进行了LLaMA-7B。然而，目前，LLM研究界仍然面临三个挑战: 1。即使是LLaMA-7b，对计算资源的要求仍然很高; 2.很少有用于指令精整的开源数据集; 以及3.缺乏关于各种类型的教学对模型能力的影响的实证研究，例如对汉语教学的反应能力和CoT推理。

LLaMA is an exceptional work that demonstrates remarkable zero-shot and few-shot capabilities. It significantly reduces the costs of training, fine-tuning, and deploying competing large language models: LLaMA-13B outperforms GPT-3 (175B), while LLaMA-65B is competitive with PaLM-540B. Recently, to improve LLaMA's instruction-following ability, Stanford Alpaca fine-tuned LLaMA-7B on 52K instruction-following datasets generated via the self-instruction technique. However, the LLM research community still faces three core challenges at present: 1. Even LLaMA-7B still has high computational resource requirements; 2. There are very few open-source datasets available for instruction fine-tuning; and 3. There is a lack of empirical research on the impact of various types of instructional prompts on model capabilities, such as the ability to respond to Chinese instructions and chain-of-thought (CoT) reasoning.

搜集汇总

背景与挑战

背景概述

Alpaca-COT是一个指令微调平台，针对大型语言模型研究中计算资源要求高、开源指令数据集稀缺及教学类型影响实证研究不足的挑战而设计。它基于LLaMA模型，通过收集指令数据来提升模型的指令跟随和推理能力，支持包括中文和CoT在内的多种教学类型。