@inproceedings{bostrom-etal-2024-language,
title = "Language-to-Code Translation with a Single Labeled Example",
author = "Bostrom, Kaj and
Jhamtani, Harsh and
Fang, Hao and
Thomson, Sam and
Shin, Richard and
Xia, Patrick and
Van Durme, Benjamin and
Eisner, Jason and
Andreas, Jacob",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.462",
doi = "10.18653/v1/2024.emnlp-main.462",
pages = "8101--8112",
abstract = "Tools for translating natural language into code promise natural, open-ended interaction with databases, web APIs, and other software systems. However, this promise is complicated by the diversity and continual development of these systems, each with its own interface and distinct set of features. Building a new language-to-code translator, even starting with a large language model (LM), typically requires annotating a large set of natural language commands with their associated programs. In this paper, we describe ICIP (In-Context Inverse Programming), a method for bootstrapping a language-to-code system using mostly (or entirely) unlabeled programs written using a potentially unfamiliar (but human-readable) library or API. ICIP uses a pre-trained LM to assign candidate natural language descriptions to these programs, then iteratively refines the descriptions to ensure global consistency. Across nine different application domains from the Overnight and Spider benchmarks and text-davinci-003 and CodeLlama-7b-Instruct models, ICIP outperforms a number of prompting baselines. Indeed, in a {``}nearly unsupervised{''} setting with only a single annotated program and 100 unlabeled examples, it achieves up to 85{\%} of the performance of a fully supervised system.",
}
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<abstract>Tools for translating natural language into code promise natural, open-ended interaction with databases, web APIs, and other software systems. However, this promise is complicated by the diversity and continual development of these systems, each with its own interface and distinct set of features. Building a new language-to-code translator, even starting with a large language model (LM), typically requires annotating a large set of natural language commands with their associated programs. In this paper, we describe ICIP (In-Context Inverse Programming), a method for bootstrapping a language-to-code system using mostly (or entirely) unlabeled programs written using a potentially unfamiliar (but human-readable) library or API. ICIP uses a pre-trained LM to assign candidate natural language descriptions to these programs, then iteratively refines the descriptions to ensure global consistency. Across nine different application domains from the Overnight and Spider benchmarks and text-davinci-003 and CodeLlama-7b-Instruct models, ICIP outperforms a number of prompting baselines. Indeed, in a “nearly unsupervised” setting with only a single annotated program and 100 unlabeled examples, it achieves up to 85% of the performance of a fully supervised system.</abstract>
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%0 Conference Proceedings
%T Language-to-Code Translation with a Single Labeled Example
%A Bostrom, Kaj
%A Jhamtani, Harsh
%A Fang, Hao
%A Thomson, Sam
%A Shin, Richard
%A Xia, Patrick
%A Van Durme, Benjamin
%A Eisner, Jason
%A Andreas, Jacob
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F bostrom-etal-2024-language
%X Tools for translating natural language into code promise natural, open-ended interaction with databases, web APIs, and other software systems. However, this promise is complicated by the diversity and continual development of these systems, each with its own interface and distinct set of features. Building a new language-to-code translator, even starting with a large language model (LM), typically requires annotating a large set of natural language commands with their associated programs. In this paper, we describe ICIP (In-Context Inverse Programming), a method for bootstrapping a language-to-code system using mostly (or entirely) unlabeled programs written using a potentially unfamiliar (but human-readable) library or API. ICIP uses a pre-trained LM to assign candidate natural language descriptions to these programs, then iteratively refines the descriptions to ensure global consistency. Across nine different application domains from the Overnight and Spider benchmarks and text-davinci-003 and CodeLlama-7b-Instruct models, ICIP outperforms a number of prompting baselines. Indeed, in a “nearly unsupervised” setting with only a single annotated program and 100 unlabeled examples, it achieves up to 85% of the performance of a fully supervised system.
%R 10.18653/v1/2024.emnlp-main.462
%U https://aclanthology.org/2024.emnlp-main.462
%U https://doi.org/10.18653/v1/2024.emnlp-main.462
%P 8101-8112
Markdown (Informal)
[Language-to-Code Translation with a Single Labeled Example](https://aclanthology.org/2024.emnlp-main.462) (Bostrom et al., EMNLP 2024)
ACL
- Kaj Bostrom, Harsh Jhamtani, Hao Fang, Sam Thomson, Richard Shin, Patrick Xia, Benjamin Van Durme, Jason Eisner, and Jacob Andreas. 2024. Language-to-Code Translation with a Single Labeled Example. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 8101–8112, Miami, Florida, USA. Association for Computational Linguistics.
