用小数据小设备快速优化大模型，专攻西班牙语任务
RigoChat 2: an adapted language model to Spanish using a bounded dataset and reduced hardware
- 基于预训练大模型，用小数据微调提升西班牙语表现
- 在有限算力下实现性能接近主流模型的西班牙语任务表现
- 适合资源受限场景下的多语言模型定制需求
大型语言模型（LLMs）已成为现代人工智能的核心，能在无需收集特定任务数据的情况下，以空前的准确率处理多种语言任务。然而，这些模型在训练和推理过程中需要大量计算资源、时间和内存，因此降低其资源消耗至关重要。本文展示，仅需极少资源和极短时间，即可通过微调一个相对较小的预训练大模型，在不损害其整体能力的前提下，显著提升其在特定语言任务上的表现。我们以RigoChat 2为例，说明如何将语言模型适配到西班牙语任务中，实现更优结果。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have become a key element of modern artificial intelligence, demonstrating the ability to address a wide range of language processing tasks at unprecedented levels of accuracy without the need of collecting problem-specific data. However, these versatile models face a significant challenge: both their training and inference processes require substantial computational resources, time, and memory. Consequently, optimizing this kind of models to minimize these requirements is crucial. In this article, we demonstrate that, with minimal resources and in a remarkably short time, it is possible to enhance a state-of-the-art model, specifically for a given language task, without compromising its overall capabilities using a relatively small pretrained LLM as a basis. Specifically, we present our use case, RigoChat 2, illustrating how LLMs can be adapted to achieve superior results in Spanish-language tasks.
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