tecnologiactc/automl_llm_agent_m2

AutoML-LLM Agent Module 2 Benchmark

This dataset contains the Module 2 benchmark for evaluating an assistant that converts a Module 1 recipe, a user request, and a processed tabular dataset into an auditable autogluon.cloud.TabularCloudPredictor configuration.

The repository is scoped to Module 2 only.

Tables

• module2_cases: one row per Module 2 evaluation case.
• module2_queries: user requests for each case.
• module2_reference_configs: legacy reference JSON files used as expected AutoGluon Cloud intent.
• module2_reference_recipes: Module 1 recipe assets that Module 2 must preserve.
• module2_ablation_variants: LangGraph node-ablation variants used by Module 2 experiments.
• module2_invalid_seed_configs: intentionally invalid configs for repair and robustness evaluation.

File Assets

• Data/raw/AutoML_LLM_agent/dataset/processed_files/*.csv: canonical processed tabular CSVs referenced by module2_cases.data_path.
• Data/raw/AutoML_LLM_agent/reference_json/json_*_reference.json: legacy reference configs referenced by module2_cases.reference_config_path.
• dataset/module2_reference_recipes/*.json: Module 1 recipe contracts referenced by module2_reference_recipes.recipe_path.

Current Coverage

The benchmark currently includes seven reference cases:

• calls_for_service_cloud
• electric_vehicle_cloud
• cholesterol_cloud
• diabetes_cloud
• properties_cloud
• banking_cloud
• avocado_cloud

Usage

from datasets import load_dataset

cases = load_dataset("tecnologiactc/automl_llm_agent_m2", "module2_cases", split="train")
queries = load_dataset("tecnologiactc/automl_llm_agent_m2", "module2_queries", split="train")
variants = load_dataset("tecnologiactc/automl_llm_agent_m2", "module2_ablation_variants", split="train")

CSV and JSON assets referenced by the table path columns can be downloaded from the same dataset repository.