Predicting Emergent Abilities with Infinite Resolution Evaluation

📄 arXiv: 2310.03262v3 📥 PDF

作者: Shengding Hu, Xin Liu, Xu Han, Xinrong Zhang, Chaoqun He, Weilin Zhao, Yankai Lin, Ning Ding, Zebin Ou, Guoyang Zeng, Zhiyuan Liu, Maosong Sun

分类: cs.CL

发布日期: 2023-10-05 (更新: 2024-04-17)

备注: After revision

💡 一句话要点

提出PassUntil评估策略以预测大语言模型的任务性能

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 大语言模型 任务性能 评估策略 涌现能力 扩展规律 PassUntil

📋 核心要点

  1. 现有研究对大语言模型的扩展特性理解不足，任务性能在模型规模增加时难以预测。
  2. 本文提出PassUntil评估策略，通过大规模采样实现理论上的无限分辨率，捕捉小模型的性能提升。
  3. 研究结果表明，能够在训练前以仅0.05%的偏差预测2.4B模型的代码生成性能，并发现加速涌现现象。

📝 摘要（中文）

大规模语言模型（LLMs）的科学扩展需要全面理解其扩展特性。然而，现有文献对扩展特性的研究仅提供了不完整的答案：优化损失随着模型规模的增加而可预测地下降，但任务性能在扩展过程中远未可预测。本文发现小模型虽然表现较差，但在任务性能上有显著提升，这些提升未被传统评估策略捕捉。为此，本文提出了PassUntil评估策略，通过在解码阶段进行大规模采样，理论上实现无限分辨率的评估。通过PassUntil，我们定量研究了任务性能的扩展规律，发现了一种严格的任务扩展规律，并首次系统性验证了GPT-4报告提出的可预测扩展。此外，我们还定量研究了“涌现能力”，发现加速涌现的现象，其扩展曲线无法用标准扩展规律函数拟合。

🔬 方法详解

问题定义：本文旨在解决大语言模型在扩展过程中任务性能不可预测的问题。现有方法未能有效捕捉小模型的性能提升，导致对任务性能的理解不全面。

核心思路：论文提出了PassUntil评估策略，通过在解码阶段进行大规模采样，提升评估的分辨率，从而更准确地捕捉小模型的任务性能变化。

技术框架：整体架构包括数据采样、解码过程和性能评估三个主要模块。首先，通过大量样本生成候选输出，然后对这些输出进行分析，最后评估模型在特定任务上的表现。

关键创新：最重要的技术创新是PassUntil评估策略的提出，它实现了理论上的无限分辨率评估，能够捕捉到传统方法无法识别的细微性能变化。

关键设计：在PassUntil中，采用了大量的解码样本进行性能评估，确保了评估结果的准确性和可靠性。具体的参数设置和损失函数设计未在摘要中详细说明，需参考论文的具体内容。

📊 实验亮点

实验结果显示，使用PassUntil评估策略后，能够在训练前以仅0.05%的偏差准确预测2.4B模型的代码生成性能。此外，研究还发现了一种加速涌现的现象，其扩展曲线无法用标准扩展规律函数拟合，展示了模型性能提升的新特征。

🎯 应用场景

该研究的潜在应用领域包括自然语言处理、代码生成和其他需要大规模语言模型的任务。通过提升对模型性能的预测能力，研究成果有助于优化模型设计和训练策略，推动人工智能技术的进一步发展。

📄 摘要（原文）

The scientific scale-up of large language models (LLMs) necessitates a comprehensive understanding of their scaling properties. However, the existing literature on the scaling properties only yields an incomplete answer: optimization loss decreases predictably as the model size increases, in line with established scaling law; yet no scaling law for task has been established and the task performances are far from predictable during scaling. Task performances typically show minor gains on small models until they improve dramatically once models exceed a size threshold, exemplifying the emergent abilities''. In this study, we discover that small models, although they exhibit minor performance, demonstrate critical and consistent task performance improvements that are not captured by conventional evaluation strategies due to insufficient measurement resolution. To measure such improvements, we introduce PassUntil, an evaluation strategy with theoretically infinite resolution, through massive sampling in the decoding phase. With PassUntil, we conduct a quantitative investigation into the scaling law of task performance. The investigation contains two parts. Firstly, a strict task scaling law that is not conventionally known to exist, is identified, enhancing the predictability of task performances. Remarkably, we are able to predict the performance of the 2.4B model on code generation with merely 0.05\% deviation before training starts, which is the first systematic attempt to verify predictable scaling proposed by GPT-4's report. Secondly, we are able to study emergent abilities quantitatively. We identify a kind of accelerated emergence whose scaling curve cannot be fitted by standard scaling law function and has a increasing speed. We then examine two hypothesis and imply that themultiple circuits hypothesis'' might be responsible for the accelerated emergence.