@inproceedings{moses-etal-2024-nlpeople,
title = "{NLP}eople at {T}ext{G}raphs-17 Shared Task: Chain of Thought Questioning to Elicit Decompositional Reasoning",
author = "Moses, Movina and
Kuruvanthodi, Vishnudev and
Elkaref, Mohab and
Tanaka, Shinnosuke and
Barry, James and
Mel, Geeth and
Watson, Campbell",
editor = "Ustalov, Dmitry and
Gao, Yanjun and
Pachenko, Alexander and
Tutubalina, Elena and
Nikishina, Irina and
Ramesh, Arti and
Sakhovskiy, Andrey and
Usbeck, Ricardo and
Penn, Gerald and
Valentino, Marco",
booktitle = "Proceedings of TextGraphs-17: Graph-based Methods for Natural Language Processing",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.textgraphs-1.13",
pages = "142--148",
abstract = "This paper presents the approach of the NLPeople team for the Text-Graph Representations for KGQA Shared Task at TextGraphs-17. The task involved selecting an answer for a given question from a list of candidate entities. We show that prompting Large Language models (LLMs) to break down a natural language question into a series of sub-questions, allows models to understand complex questions. The LLMs arrive at the final answer by answering the intermediate questions using their internal knowledge and without needing additional context. Our approach to the task uses an ensemble of prompting strategies to guide how LLMs interpret various types of questions. Our submission achieves an F1 score of 85.90, ranking 1st among the other participants in the task.",
}
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<abstract>This paper presents the approach of the NLPeople team for the Text-Graph Representations for KGQA Shared Task at TextGraphs-17. The task involved selecting an answer for a given question from a list of candidate entities. We show that prompting Large Language models (LLMs) to break down a natural language question into a series of sub-questions, allows models to understand complex questions. The LLMs arrive at the final answer by answering the intermediate questions using their internal knowledge and without needing additional context. Our approach to the task uses an ensemble of prompting strategies to guide how LLMs interpret various types of questions. Our submission achieves an F1 score of 85.90, ranking 1st among the other participants in the task.</abstract>
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%0 Conference Proceedings
%T NLPeople at TextGraphs-17 Shared Task: Chain of Thought Questioning to Elicit Decompositional Reasoning
%A Moses, Movina
%A Kuruvanthodi, Vishnudev
%A Elkaref, Mohab
%A Tanaka, Shinnosuke
%A Barry, James
%A Mel, Geeth
%A Watson, Campbell
%Y Ustalov, Dmitry
%Y Gao, Yanjun
%Y Pachenko, Alexander
%Y Tutubalina, Elena
%Y Nikishina, Irina
%Y Ramesh, Arti
%Y Sakhovskiy, Andrey
%Y Usbeck, Ricardo
%Y Penn, Gerald
%Y Valentino, Marco
%S Proceedings of TextGraphs-17: Graph-based Methods for Natural Language Processing
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F moses-etal-2024-nlpeople
%X This paper presents the approach of the NLPeople team for the Text-Graph Representations for KGQA Shared Task at TextGraphs-17. The task involved selecting an answer for a given question from a list of candidate entities. We show that prompting Large Language models (LLMs) to break down a natural language question into a series of sub-questions, allows models to understand complex questions. The LLMs arrive at the final answer by answering the intermediate questions using their internal knowledge and without needing additional context. Our approach to the task uses an ensemble of prompting strategies to guide how LLMs interpret various types of questions. Our submission achieves an F1 score of 85.90, ranking 1st among the other participants in the task.
%U https://aclanthology.org/2024.textgraphs-1.13
%P 142-148
Markdown (Informal)
[NLPeople at TextGraphs-17 Shared Task: Chain of Thought Questioning to Elicit Decompositional Reasoning](https://aclanthology.org/2024.textgraphs-1.13) (Moses et al., TextGraphs-WS 2024)
ACL