FollowEval: A Multi-Dimensional Benchmark for Assessing the Instruction-Following Capability of Large Language Models
作者: Yimin Jing, Renren Jin, Jiahao Hu, Huishi Qiu, Xiaohua Wang, Peng Wang, Deyi Xiong
分类: cs.CL
发布日期: 2023-11-16
备注: Work in progress
💡 一句话要点
提出FollowEval基准以评估大型语言模型的指令遵循能力
🎯 匹配领域: 支柱一：机器人控制 (Robot Control) 支柱九：具身大模型 (Embodied Foundation Models)
关键词: 指令遵循 大型语言模型 评估基准 多语言 人类设计 推理能力 性能评估
📋 核心要点
- 现有的指令遵循评估基准多为单一语言且依赖自动生成，限制了其适用性和示例质量。
- FollowEval基准通过人类专家设计的多语言实例，评估LLMs在多个维度的指令遵循能力。
- 实验结果显示，LLMs在指令遵循能力上显著落后于人类，表明其改进空间巨大。
📝 摘要（中文）
有效评估大型语言模型（LLMs）遵循指令的能力至关重要。现有的评估基准多限于单一语言且多为自动生成，影响了其适用性和测试示例的质量。为此，本文提出FollowEval基准，包含英文和中文实例，所有测试示例均由人类专家精心设计。该基准从字符串操作、常识推理、逻辑推理、空间推理和响应约束五个维度评估LLMs的指令遵循能力，并且每个测试示例设计为评估多个维度。通过对多种LLMs的评估，发现其表现显著低于人类，显示出这些模型在指令遵循能力上有很大的提升空间。
🔬 方法详解
问题定义：本文旨在解决现有指令遵循评估基准的局限性，包括语言单一性和自动生成导致的示例质量低下的问题。
核心思路：FollowEval基准通过人类专家设计的多语言测试示例，评估LLMs在多个维度的指令遵循能力，增强了评估的全面性和准确性。
技术框架：FollowEval基准包括五个关键维度的评估模块，分别为字符串操作、常识推理、逻辑推理、空间推理和响应约束，每个测试示例设计为评估多个维度。
关键创新：最重要的创新在于基准的多语言性和人类设计的测试示例，这与现有基准的自动生成和单一语言特性形成鲜明对比。
关键设计：在设计过程中，确保每个测试示例能够同时评估多个维度，增加了测试的复杂性和挑战性。
🖼️ 关键图片
📊 实验亮点
实验结果表明，使用FollowEval基准评估的多种LLMs，其指令遵循能力显著低于人类，显示出LLMs在该领域的表现有待提升。这一发现强调了对模型进行针对性改进的必要性。
🎯 应用场景
FollowEval基准可广泛应用于大型语言模型的开发与评估，帮助研究人员和开发者更好地理解模型在指令遵循方面的能力与不足。未来，该基准可能推动更高效的模型训练和优化，提升人机交互的质量与可靠性。
📄 摘要（原文）
The effective assessment of the instruction-following ability of large language models (LLMs) is of paramount importance. A model that cannot adhere to human instructions might be not able to provide reliable and helpful responses. In pursuit of this goal, various benchmarks have been constructed to evaluate the instruction-following capacity of these models. However, these benchmarks are limited to a single language and are constructed using automated approaches, which restricts their applicability and the quality of the test examples they contain. To bridge this gap, we introduce the FollowEval benchmark in this paper. This benchmark is composed of instances in both English and Chinese, and all test examples are crafted by human experts. Furthermore, the FollowEval benchmark is designed to assess LLMs across five critical dimensions of instruction following: string manipulation, commonsense reasoning, logical reasoning, spatial reasoning, and response constraints. To enhance the complexity and present a sufficient challenge, each test example is designed to evaluate more than one dimension. We have evaluated various LLMs using the FollowEval benchmark and found that their performance significantly lags behind that of humans. This highlights the considerable room for improvement in the instruction-following ability of these models.