@inproceedings{lozoya-etal-2024-generating,
title = "Generating Mental Health Transcripts with {SAPE} ({S}panish Adaptive Prompt Engineering)",
author = "Lozoya, Daniel and
Berazaluce, Alejandro and
Perches, Juan and
L{\'u}a, Eloy and
Conway, Mike and
D{'}Alfonso, Simon",
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.285",
pages = "5096--5113",
abstract = "Large language models have become valuable tools for data augmentation in scenarios with limited data availability, as they can generate synthetic data resembling real-world data. However, their generative performance depends on the quality of the prompt used to instruct the model. Prompt engineering that relies on hand-crafted strategies or requires domain experts to adjust the prompt often yields suboptimal results. In this paper we present SAPE, a Spanish Adaptive Prompt Engineering method utilizing genetic algorithms for prompt generation and selection. Our evaluation of SAPE focuses on a generative task that involves the creation of Spanish therapy transcripts, a type of data that is challenging to collect due to the fact that it typically includes protected health information. Through human evaluations conducted by mental health professionals, our results show that SAPE produces Spanish counselling transcripts that more closely resemble authentic therapy transcripts compared to other prompt engineering techniques that are based on Reflexion and Chain-of-Thought.",
}
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<abstract>Large language models have become valuable tools for data augmentation in scenarios with limited data availability, as they can generate synthetic data resembling real-world data. However, their generative performance depends on the quality of the prompt used to instruct the model. Prompt engineering that relies on hand-crafted strategies or requires domain experts to adjust the prompt often yields suboptimal results. In this paper we present SAPE, a Spanish Adaptive Prompt Engineering method utilizing genetic algorithms for prompt generation and selection. Our evaluation of SAPE focuses on a generative task that involves the creation of Spanish therapy transcripts, a type of data that is challenging to collect due to the fact that it typically includes protected health information. Through human evaluations conducted by mental health professionals, our results show that SAPE produces Spanish counselling transcripts that more closely resemble authentic therapy transcripts compared to other prompt engineering techniques that are based on Reflexion and Chain-of-Thought.</abstract>
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%0 Conference Proceedings
%T Generating Mental Health Transcripts with SAPE (Spanish Adaptive Prompt Engineering)
%A Lozoya, Daniel
%A Berazaluce, Alejandro
%A Perches, Juan
%A Lúa, Eloy
%A Conway, Mike
%A D’Alfonso, Simon
%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 lozoya-etal-2024-generating
%X Large language models have become valuable tools for data augmentation in scenarios with limited data availability, as they can generate synthetic data resembling real-world data. However, their generative performance depends on the quality of the prompt used to instruct the model. Prompt engineering that relies on hand-crafted strategies or requires domain experts to adjust the prompt often yields suboptimal results. In this paper we present SAPE, a Spanish Adaptive Prompt Engineering method utilizing genetic algorithms for prompt generation and selection. Our evaluation of SAPE focuses on a generative task that involves the creation of Spanish therapy transcripts, a type of data that is challenging to collect due to the fact that it typically includes protected health information. Through human evaluations conducted by mental health professionals, our results show that SAPE produces Spanish counselling transcripts that more closely resemble authentic therapy transcripts compared to other prompt engineering techniques that are based on Reflexion and Chain-of-Thought.
%U https://aclanthology.org/2024.naacl-long.285
%P 5096-5113
Markdown (Informal)
[Generating Mental Health Transcripts with SAPE (Spanish Adaptive Prompt Engineering)](https://aclanthology.org/2024.naacl-long.285) (Lozoya et al., NAACL 2024)
ACL
- Daniel Lozoya, Alejandro Berazaluce, Juan Perches, Eloy Lúa, Mike Conway, and Simon D’Alfonso. 2024. Generating Mental Health Transcripts with SAPE (Spanish Adaptive Prompt Engineering). 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 5096–5113, Mexico City, Mexico. Association for Computational Linguistics.