用自然语言指令自动完成眼动数据分析，无需编程。
Lazy or Efficient? Towards Accessible Eye-Tracking Event Detection Using LLMs
- 输入自然语言指令，自动生成数据清洗和检测代码
- 在公开数据集上达到传统方法相当的准确率
- 适合无编程背景的研究者快速开展眼动研究
眼动事件检测是视觉科学、人机交互和应用分析的基础。然而，现有工作流通常需要专业编程知识并处理异构原始数据格式。经典检测器如 I-VT 和 I-DT 效果良好，但对预处理和参数设置极为敏感，限制了其在非专业实验室的应用。本文提出一种无需编码的大型语言模型（LLM）驱动流程，将自然语言指令转化为端到端分析。系统（1）分析原始眼动文件以推断结构与元数据；（2）从简洁用户提示生成可执行的数据清洗与检测代码；（3）应用生成的检测器标注注视与扫视；（4）返回结果与解释性报告，并支持通过修改提示迭代优化。在公开基准测试中，该方法实现与传统方法相当的准确性，同时显著降低技术门槛。该框架降低了眼动研究的入门难度，为代码密集型工作流提供了灵活且易用的替代方案。
原文摘要 · Abstract (English)
Gaze event detection is fundamental to vision science, human-computer interaction, and applied analytics. However, current workflows often require specialized programming knowledge and careful handling of heterogeneous raw data formats. Classical detectors such as I-VT and I-DT are effective but highly sensitive to preprocessing and parameterization, limiting their usability outside specialized laboratories. This work introduces a code-free, large language model (LLM)-driven pipeline that converts natural language instructions into an end-to-end analysis. The system (1) inspects raw eye-tracking files to infer structure and metadata, (2) generates executable routines for data cleaning and detector implementation from concise user prompts, (3) applies the generated detector to label fixations and saccades, and (4) returns results and explanatory reports, and allows users to iteratively optimize their code by editing the prompt. Evaluated on public benchmarks, the approach achieves accuracy comparable to traditional methods while substantially reducing technical overhead. The framework lowers barriers to entry for eye-tracking research, providing a flexible and accessible alternative to code-intensive workflows.
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