# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import datasets _CITATION = """\ @misc{valmeekam2023planbenchextensiblebenchmarkevaluating, title={PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change}, author={Karthik Valmeekam and Matthew Marquez and Alberto Olmo and Sarath Sreedharan and Subbarao Kambhampati}, year={2023}, eprint={2206.10498}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2206.10498}, } """ _DESCRIPTION = """\ PlanBench is a benchmark for evaluating models' capabilities of planning and reasoning by evaluating them on IPC problems""" _HOMEPAGE = "https://github.com/karthikv792/LLMs-Planning/tree/main/plan-bench" _LICENSE = "MIT" _URLS_prefix = { "blocksworld" : "https://raw.githubusercontent.com/karthikv792/LLMs-Planning/main/plan-bench/prompts/blocksworld", "blocksworld_3": "https://raw.githubusercontent.com/karthikv792/LLMs-Planning/main/plan-bench/prompts/blocksworld_3", "mystery_blocksworld": "https://raw.githubusercontent.com/karthikv792/LLMs-Planning/main/plan-bench/prompts/mystery_blocksworld", "mystery_blocksworld_3": "https://raw.githubusercontent.com/karthikv792/LLMs-Planning/main/plan-bench/prompts/mystery_blocksworld_3", "logistics": "https://raw.githubusercontent.com/karthikv792/LLMs-Planning/main/plan-bench/prompts/logistics", } _URLS = { "blocksworld_plan_generation": { "test": _URLS_prefix["blocksworld"] + "/task_1_plan_generation.json" }, "blocksworld_plan_optimality": { "test": _URLS_prefix["blocksworld"] + "/task_2_plan_optimality.json" }, "blocksworld_plan_verification": { "test": _URLS_prefix["blocksworld"] + "/task_3_plan_verification.json" }, "blocksworld_plan_reuse": { "test": _URLS_prefix["blocksworld"] + "/task_4_plan_reuse.json" }, "blocksworld_plan_generalization": { "test": _URLS_prefix["blocksworld"] + "/task_5_plan_generalization.json" }, "blocksworld_replanning": { "test": _URLS_prefix["blocksworld"] + "/task_6_replanning.json" }, "blocksworld_plan_execution": { "test": _URLS_prefix["blocksworld"] + "/task_7_plan_execution.json" }, "blocksworld_goal_shuffling": { "test": _URLS_prefix["blocksworld"] + "/task_8_1_goal_shuffling.json" }, "blocksworld_full_to_partial": { "test": _URLS_prefix["blocksworld"] + "/task_8_2_full_to_partial.json" }, "blocksworld_partial_to_full": { "test": _URLS_prefix["blocksworld"] + "/task_8_3_partial_to_full.json" }, "blocksworld_3_plan_generation": { "test": _URLS_prefix["blocksworld_3"] + "/task_1_plan_generation.json" }, "blocksworld_3_plan_optimality": { "test": _URLS_prefix["blocksworld_3"] + "/task_2_plan_optimality.json" }, "blocksworld_3_plan_verification": { "test": _URLS_prefix["blocksworld_3"] + "/task_3_plan_verification.json" }, "blocksworld_3_plan_reuse": { "test": _URLS_prefix["blocksworld_3"] + "/task_4_plan_reuse.json" }, "blocksworld_3_plan_generalization": { "test": _URLS_prefix["blocksworld_3"] + "/task_5_plan_generalization.json" }, "blocksworld_3_replanning": { "test": _URLS_prefix["blocksworld_3"] + "/task_6_replanning.json" }, "blocksworld_3_plan_execution": { "test": _URLS_prefix["blocksworld_3"] + "/task_7_plan_execution.json" }, "blocksworld_3_goal_shuffling": { "test": _URLS_prefix["blocksworld_3"] + "/task_8_1_goal_shuffling.json" }, "blocksworld_3_full_to_partial": { "test": _URLS_prefix["blocksworld_3"] + "/task_8_2_full_to_partial.json" }, "blocksworld_3_partial_to_full": { "test": _URLS_prefix["blocksworld_3"] + "/task_8_3_partial_to_full.json" }, "mystery_blocksworld_plan_generation": { "test": _URLS_prefix["mystery_blocksworld"] + "/task_1_plan_generation.json" }, "mystery_blocksworld_plan_optimality": { "test": _URLS_prefix["mystery_blocksworld"] + "/task_2_plan_optimality.json" }, "mystery_blocksworld_plan_verification": { "test": _URLS_prefix["mystery_blocksworld"] + "/task_3_plan_verification.json" }, "mystery_blocksworld_plan_reuse": { "test": _URLS_prefix["mystery_blocksworld"] + "/task_4_plan_reuse.json" }, "mystery_blocksworld_plan_generalization": { "test": _URLS_prefix["mystery_blocksworld"] + "/task_5_plan_generalization.json" }, "mystery_blocksworld_replanning": { "test": _URLS_prefix["mystery_blocksworld"] + "/task_6_replanning.json" }, "mystery_blocksworld_plan_execution": { "test": _URLS_prefix["mystery_blocksworld"] + "/task_7_plan_execution.json" }, "mystery_blocksworld_goal_shuffling": { "test": _URLS_prefix["mystery_blocksworld"] + "/task_8_1_goal_shuffling.json" }, "mystery_blocksworld_full_to_partial": { "test": _URLS_prefix["mystery_blocksworld"] + "/task_8_2_full_to_partial.json" }, "mystery_blocksworld_partial_to_full": { "test": _URLS_prefix["mystery_blocksworld"] + "/task_8_3_partial_to_full.json" }, "mystery_blocksworld_3_plan_generation": { "test": _URLS_prefix["mystery_blocksworld_3"] + "/task_1_plan_generation.json" }, "mystery_blocksworld_3_plan_optimality": { "test": _URLS_prefix["mystery_blocksworld_3"] + "/task_2_plan_optimality.json" }, "mystery_blocksworld_3_plan_verification": { "test": _URLS_prefix["mystery_blocksworld_3"] + "/task_3_plan_verification.json" }, "mystery_blocksworld_3_plan_reuse": { "test": _URLS_prefix["mystery_blocksworld_3"] + "/task_4_plan_reuse.json" }, "mystery_blocksworld_3_plan_generalization": { "test": _URLS_prefix["mystery_blocksworld_3"] + "/task_5_plan_generalization.json" }, "mystery_blocksworld_3_replanning": { "test": _URLS_prefix["mystery_blocksworld_3"] + "/task_6_replanning.json" }, "mystery_blocksworld_3_plan_execution": { "test": _URLS_prefix["mystery_blocksworld_3"] + "/task_7_plan_execution.json" }, "mystery_blocksworld_3_goal_shuffling": { "test": _URLS_prefix["mystery_blocksworld_3"] + "/task_8_1_goal_shuffling.json" }, "mystery_blocksworld_3_full_to_partial": { "test": _URLS_prefix["mystery_blocksworld_3"] + "/task_8_2_full_to_partial.json" }, "mystery_blocksworld_3_partial_to_full": { "test": _URLS_prefix["mystery_blocksworld_3"] + "/task_8_3_partial_to_full.json" }, "logistics_plan_generation": { "test": _URLS_prefix["logistics"] + "/task_1_plan_generation.json" }, "logistics_plan_optimality": { "test": _URLS_prefix["logistics"] + "/task_2_plan_optimality.json" }, "logistics_plan_verification": { "test": _URLS_prefix["logistics"] + "/task_3_plan_verification.json" }, "logistics_plan_reuse": { "test": _URLS_prefix["logistics"] + "/task_4_plan_reuse.json" }, "logistics_plan_generalization": { "test": _URLS_prefix["logistics"] + "/task_5_plan_generalization.json" }, "logistics_replanning": { "test": _URLS_prefix["logistics"] + "/task_6_replanning.json" }, "logistics_plan_execution": { "test": _URLS_prefix["logistics"] + "/task_7_plan_execution.json" }, "logistics_goal_shuffling": { "test": _URLS_prefix["logistics"] + "/task_8_1_goal_shuffling.json" }, "logistics_full_to_partial": { "test": _URLS_prefix["logistics"] + "/task_8_2_full_to_partial.json" }, "logistics_partial_to_full": { "test": _URLS_prefix["logistics"] + "/task_8_3_partial_to_full.json" } } class PlanBench(datasets.GeneratorBasedBuilder): """ LMentry is a benchmark for measuring language model performance on tasks that are trivial to humans. LMentry consists of 25 tasks which humans are generally expected to perform perfectly, e.g. writing a sentence containing a specific word, identifying which words in a list belong to a specific category, choosing which of two words is longer, or identifying which of two words rhymes with a third word. """ BUILDER_CONFIGS = [ datasets.BuilderConfig( name=config_name, version=datasets.Version("0.0.1"), description=f"{config_name} task from PlanBench" ) for config_name in _URLS.keys() ] def _info(self): features = { "instance_id": datasets.Value("int32"), "query": datasets.Value("string"), "ground_truth_plan": datasets.Value("string"), } if ("plan_generation" in self.config.name or "plan_optimality" in self.config.name or "plan_generalization" in self.config.name or "replanning" in self.config.name or "plan_execution" in self.config.name): features.update({"example_instance_ids": datasets.Sequence(datasets.Value("string"))}) if "plan_reuse" in self.config.name or "replanning" in self.config.name: features.update({"new_instance": datasets.Value("string")}) if "goal_shuffling" in self.config.name: features.update({"single_goal_instances": datasets.Value("int32")}) features = datasets.Features(features) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, citation=_CITATION, license=_LICENSE, ) def _split_generators(self, dl_manager): urls = _URLS[self.config.name] data_dir = dl_manager.download_and_extract(urls) return [ datasets.SplitGenerator( name = datasets.Split.TEST, gen_kwargs = { "filepath" : data_dir["test"], "split" : "test", } ) ] def _generate_examples(self, filepath, split): with open(filepath, encoding = "utf-8") as fin : data = json.load(fin) for instance in data["instances"]: yield instance["instance_id"], instance