How-to procedures, such as how to plant a garden, are now used by millions of users, but sometimes need customizing to meet a user's specific needs, e.g., planting a garden without pesticides. Our goal is to measure and improve an LLM's ability to perform such customization. Our approach is to test several simple multi-LLM-agent architectures for customization, as well as an end-to-end LLM, using a new evaluation set, called CustomPlans, of over 200 WikiHow procedures each with a customization need. We find that a simple architecture with two LLM agents used sequentially performs best, one that edits a generic how-to procedure and one that verifies its executability, significantly outperforming (10.5% absolute) an end-to-end prompted LLM. This suggests that LLMs can be configured reasonably effectively for procedure customization. This also suggests that multi-agent editing architectures may be worth exploring further for other customization applications (e.g. coding, creative writing) in the future.
@article{arxiv.2311.09510,
title = {Tailoring with Targeted Precision: Edit-Based Agents for Open-Domain Procedure Customization},
author = {Yash Kumar Lal and Li Zhang and Faeze Brahman and Bodhisattwa Prasad Majumder and Peter Clark and Niket Tandon},
journal= {arXiv preprint arXiv:2311.09510},
year = {2024}
}
Comments
Camera ready version accepted to Findings of ACL 2024