为大模型真实应用性能设计更全面的评估框架
Beyond Next Word Prediction: Developing Comprehensive Evaluation Frameworks for measuring LLM performance on real world applications
- 基于游戏与工具架构构建动态评估体系
- 支持供应链、金融推理等多场景能力测量
- 适用于安全、伦理等抽象维度的评估
尽管大语言模型本质上是逐词预测系统，但其实际应用已扩展至自然语言处理、文本生成、对话助手及软件使用等多个领域，并在企业中得到广泛应用。当前评估通常依赖静态数据集，包含一组提示及其对应的标准答案。本文提出一种更全面的评估框架，基于传统游戏与工具化架构，实现对模型能力的全方位衡量。该框架具备通用性，可不加修改地扩展至供应链管理、金融推理等具体场景，也可用于伦理、安全等抽象维度的评估。
原文摘要 · Abstract (English)
While Large Language Models (LLMs) are fundamentally next-token prediction systems, their practical applications extend far beyond this basic function. From natural language processing and text generation to conversational assistants and software use, LLMs have numerous use-cases, and have already acquired a significant degree of enterprise adoption. To evaluate such models, static evaluation datasets, consisting of a set of prompts and their corresponding ground truths, are often used to benchmark the efficacy of the model for a particular task. In this paper, we provide the basis for a more comprehensive evaluation framework, based upon a traditional game and tool-based architecture that enables a more overarching measurement of a model's capabilities. For simplicity, we provide a generalized foundation that can be extended, without significant alteration, to numerous scenarios, from specific use cases such as supply chain management or financial reasoning, to abstract measurements such as ethics or safety.
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