自我专精：发掘大型语言模型中的潜在专长
Self-Specialization: Uncovering Latent Expertise within Large Language Models
AI总结：
本文提出自我专精方法，仅用少量标注种子数据即可从通用预训练大模型中高效地雕琢出专家模型，在生物医学和金融领域显著超越基础模型及更大规模的指令微调或领域适配模型。
摘要：
近期研究已经证明了自我对齐的有效性，即大型语言模型利用从少量人工编写的种子数据开始、由模型自身生成的指令数据，来对齐以遵循通用指令。与通用对齐不同，在本工作中，我们专注于面向专家领域专精（例如生物医学、金融）的自我对齐。作为初步工作，我们定量展示了通用指令遵循训练对下游专家领域性能的边际效应。为弥补这一不足，我们提出了自我专精（self-specialization）——仅利用少量标注种子数据，在实现跨任务泛化的同时进行有效的模型专精。自我专精提供了一种数据和参数高效的方式，从通用预训练大型语言模型中“雕琢”出专家模型。我们探索了多种流行的开放大型模型作为专精基础，在生物医学和金融领域的实验结果表明，我们的自我专精模型大幅优于其基础模型，甚至优于那些通常经过指令微调或通过其他方式适配到目标领域的更大模型。
英文摘要：
Recent works have demonstrated the effectiveness of self-alignment in which a large language model is aligned to follow general instructions using instructional data generated from the model itself starting from a handful of human-written seeds. Instead of general alignment, in this work, we focus on self-alignment for expert domain specialization (e.g., biomedicine, finance). As a preliminary, we quantitively show the marginal effect that generic instruction-following training has on downstream expert domains' performance. To remedy this, we propose self-specialization - allowing for effective model specialization while achieving cross-task generalization by leveraging only a few labeled seeds. Self-specialization offers a data- and parameter-efficient way of "carving out" an expert model out of a generalist pre-trained LLM. Exploring a variety of popular open large models as a base for specialization, our experimental results in both biomedical and financial domains show that our self-specialized models outperform their base models by a large margin, and even larger models that are generally instruction-tuned or that have been adapted to the target domain by other means.
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。
