
graph-llm-asynchow-plan
Code and dataset repo for ICML-2024 paper Graph-enhanced Large Language Models in Asynchronous Plan Reasoning.
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Graph-enhanced Large Language Models in Asynchronous Plan Reasoning is a repository containing code and datasets for the ICML-2024 paper. It includes naturalistic datasets, code for generating data, benchmarking experiments, and prototypical experiments. The repository also offers a train/test-split version of the dataset on huggingface. The paper focuses on utilizing large language models with graph enhancements for asynchronous plan reasoning.
README:
Code and dataset repo for ICML-2024 paper Graph-enhanced Large Language Models in Asynchronous Plan Reasoning.
Naturalistic datasets used in the paper is in the folder data
You can use the code to generate naturalistic data in folder data_gen
The code for the benchmarking experiment is in folder benchmark_llm
The code and data for prototypical experiment can be found in prototypical
You can also see a quick preview of our recent train/test-split version of this dataset on huggingface.
If you find this repo useful, please cite our paper as
@inproceedings{lingraph,
title={Graph-enhanced Large Language Models in Asynchronous Plan Reasoning},
author={Lin, Fangru and La Malfa, Emanuele and Hofmann, Valentin and Yang, Elle Michelle and Cohn, Anthony G and Pierrehumbert, Janet B},
booktitle={Forty-first International Conference on Machine Learning}
}
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