@inproceedings{dumitru-etal-2024-retrieval,
title = "Retrieval Augmented Generation of Subjective Explanations for Socioeconomic Scenarios",
author = "Dumitru, Razvan-Gabriel and
Alexeeva, Maria and
Alcock, Keith and
Ludgate, Nargiza and
Jeong, Cheonkam and
Abdurahaman, Zara Fatima and
Puri, Prateek and
Kirchhoff, Brian and
Sadhu, Santadarshan and
Surdeanu, Mihai",
editor = "Card, Dallas and
Field, Anjalie and
Hovy, Dirk and
Keith, Katherine",
booktitle = "Proceedings of the Sixth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS 2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.nlpcss-1.6",
doi = "10.18653/v1/2024.nlpcss-1.6",
pages = "68--85",
abstract = "We introduce a novel retrieval augmented generation approach that explicitly models causality and subjectivity. We use it to generate explanations for socioeconomic scenarios that capture beliefs of local populations. Through intrinsic and extrinsic evaluation, we show that our explanations, contextualized using causal and subjective information retrieved from local news sources, are rated higher than those produced by other large language models both in terms of mimicking the real population and the explanations quality. We also provide a discussion of the role subjectivity plays in evaluation of this natural language generation task.",
}
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%0 Conference Proceedings
%T Retrieval Augmented Generation of Subjective Explanations for Socioeconomic Scenarios
%A Dumitru, Razvan-Gabriel
%A Alexeeva, Maria
%A Alcock, Keith
%A Ludgate, Nargiza
%A Jeong, Cheonkam
%A Abdurahaman, Zara Fatima
%A Puri, Prateek
%A Kirchhoff, Brian
%A Sadhu, Santadarshan
%A Surdeanu, Mihai
%Y Card, Dallas
%Y Field, Anjalie
%Y Hovy, Dirk
%Y Keith, Katherine
%S Proceedings of the Sixth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS 2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F dumitru-etal-2024-retrieval
%X We introduce a novel retrieval augmented generation approach that explicitly models causality and subjectivity. We use it to generate explanations for socioeconomic scenarios that capture beliefs of local populations. Through intrinsic and extrinsic evaluation, we show that our explanations, contextualized using causal and subjective information retrieved from local news sources, are rated higher than those produced by other large language models both in terms of mimicking the real population and the explanations quality. We also provide a discussion of the role subjectivity plays in evaluation of this natural language generation task.
%R 10.18653/v1/2024.nlpcss-1.6
%U https://aclanthology.org/2024.nlpcss-1.6
%U https://doi.org/10.18653/v1/2024.nlpcss-1.6
%P 68-85
Markdown (Informal)
[Retrieval Augmented Generation of Subjective Explanations for Socioeconomic Scenarios](https://aclanthology.org/2024.nlpcss-1.6) (Dumitru et al., NLP+CSS-WS 2024)
ACL
- Razvan-Gabriel Dumitru, Maria Alexeeva, Keith Alcock, Nargiza Ludgate, Cheonkam Jeong, Zara Fatima Abdurahaman, Prateek Puri, Brian Kirchhoff, Santadarshan Sadhu, and Mihai Surdeanu. 2024. Retrieval Augmented Generation of Subjective Explanations for Socioeconomic Scenarios. In Proceedings of the Sixth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS 2024), pages 68–85, Mexico City, Mexico. Association for Computational Linguistics.