ValerianFourel/geodml-papersize

GEODML（Generative-Engine Optimization Double Machine Learning）是一个用于EMNLP 2026论文提交的数据集，包含端到端管道输出。该数据集专注于生成引擎优化和双重机器学习方法，涉及LLM重排、特征工程、治疗效果估计等阶段。数据集包括280个文件，总大小为5.3G，生成于2026年5月24日。其结构涵盖多个阶段：Stage A（LLM重排输出）、Stage A（顺序敏感性探针）、Stage B（工程特征）、Stage C（主要实验表）、Stage D（DML治疗效果估计）以及可解释性输出。数据集适用于LLM、排名、因果推断、双重机器学习和RAG等任务。GEODML (Generative-Engine Optimization Double Machine Learning) is a dataset submitted for EMNLP 2026, containing end-to-end pipeline outputs. This dataset focuses on generative engine optimization and double machine learning methods, covering stages such as LLM reranking, feature engineering, and treatment effect estimation. It includes 280 files with a total size of 5.3 GB, and was generated on May 24, 2026. Its structure encompasses multiple stages: Stage A (LLM reranking outputs), Stage A (sequential sensitivity probes), Stage B (engineered features), Stage C (main experimental tables), Stage D (DML treatment effect estimation), and interpretability outputs. This dataset is applicable to tasks including LLMs, ranking, causal inference, double machine learning, and RAG.