@inproceedings{logeswaran-etal-2024-code,
title = "Code Models are Zero-shot Precondition Reasoners",
author = "Logeswaran, Lajanugen and
Sohn, Sungryull and
Lyu, Yiwei and
Liu, Anthony and
Kim, Dong-Ki and
Shim, Dongsub and
Lee, Moontae and
Lee, Honglak",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-long.317",
pages = "5681--5697",
abstract = "One of the fundamental skills required for an agent acting in an environment to complete tasks is the ability to understand what actions are plausible at any given point. This work explores a novel use of code representations to reason about action preconditions for sequential decision making tasks. Code representations offer the flexibility to model procedural activities and associated constraints as well as the ability to execute and verify constraint satisfaction. Leveraging code representations, we extract action preconditions from demonstration trajectories in a zero-shot manner using pre-trained code models. Given these extracted preconditions, we propose a precondition-aware action sampling strategy that ensures actions predicted by a policy are consistent with preconditions. We demonstrate that the proposed approach enhances the performance of few-shot policy learning approaches across task-oriented dialog and embodied textworld benchmarks.",
}
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%0 Conference Proceedings
%T Code Models are Zero-shot Precondition Reasoners
%A Logeswaran, Lajanugen
%A Sohn, Sungryull
%A Lyu, Yiwei
%A Liu, Anthony
%A Kim, Dong-Ki
%A Shim, Dongsub
%A Lee, Moontae
%A Lee, Honglak
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F logeswaran-etal-2024-code
%X One of the fundamental skills required for an agent acting in an environment to complete tasks is the ability to understand what actions are plausible at any given point. This work explores a novel use of code representations to reason about action preconditions for sequential decision making tasks. Code representations offer the flexibility to model procedural activities and associated constraints as well as the ability to execute and verify constraint satisfaction. Leveraging code representations, we extract action preconditions from demonstration trajectories in a zero-shot manner using pre-trained code models. Given these extracted preconditions, we propose a precondition-aware action sampling strategy that ensures actions predicted by a policy are consistent with preconditions. We demonstrate that the proposed approach enhances the performance of few-shot policy learning approaches across task-oriented dialog and embodied textworld benchmarks.
%U https://aclanthology.org/2024.naacl-long.317
%P 5681-5697
Markdown (Informal)
[Code Models are Zero-shot Precondition Reasoners](https://aclanthology.org/2024.naacl-long.317) (Logeswaran et al., NAACL 2024)
ACL
- Lajanugen Logeswaran, Sungryull Sohn, Yiwei Lyu, Anthony Liu, Dong-Ki Kim, Dongsub Shim, Moontae Lee, and Honglak Lee. 2024. Code Models are Zero-shot Precondition Reasoners. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 5681–5697, Mexico City, Mexico. Association for Computational Linguistics.