@inproceedings{eppalapally-etal-2024-kapqa,
title = "{K}a{PQA}: Knowledge-Augmented Product Question-Answering",
author = "Eppalapally, Swetha and
Dangi, Daksh and
Bhat, Chaithra and
Gupta, Ankita and
Zhang, Ruiyi and
Agarwal, Shubham and
Bagga, Karishma and
Yoon, Seunghyun and
Lipka, Nedim and
Rossi, Ryan and
Dernoncourt, Franck",
editor = "Yu, Wenhao and
Shi, Weijia and
Yasunaga, Michihiro and
Jiang, Meng and
Zhu, Chenguang and
Hajishirzi, Hannaneh and
Zettlemoyer, Luke and
Zhang, Zhihan",
booktitle = "Proceedings of the 3rd Workshop on Knowledge Augmented Methods for NLP",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.knowledgenlp-1.2",
pages = "15--29",
abstract = "Question-answering for domain-specific applications has recently attracted much interest due to the latest advancements in large language models (LLMs). However, accurately assessing the performance of these applications remains a challenge, mainly due to the lack of suitable benchmarks that effectively simulate real-world scenarios. To address this challenge, we introduce two product question-answering (QA) datasets focused on Adobe Acrobat and Photoshop products to help evaluate the performance of existing models on domain-specific product QA tasks. Additionally, we propose a novel knowledge-driven RAG-QA framework to enhance the performance of the models in the product QA task. Our experiments demonstrated that inducing domain knowledge through query reformulation allowed for increased retrieval and generative performance when compared to standard RAG-QA methods. This improvement, however, is slight, and thus illustrates the challenge posed by the datasets introduced.",
}
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%0 Conference Proceedings
%T KaPQA: Knowledge-Augmented Product Question-Answering
%A Eppalapally, Swetha
%A Dangi, Daksh
%A Bhat, Chaithra
%A Gupta, Ankita
%A Zhang, Ruiyi
%A Agarwal, Shubham
%A Bagga, Karishma
%A Yoon, Seunghyun
%A Lipka, Nedim
%A Rossi, Ryan
%A Dernoncourt, Franck
%Y Yu, Wenhao
%Y Shi, Weijia
%Y Yasunaga, Michihiro
%Y Jiang, Meng
%Y Zhu, Chenguang
%Y Hajishirzi, Hannaneh
%Y Zettlemoyer, Luke
%Y Zhang, Zhihan
%S Proceedings of the 3rd Workshop on Knowledge Augmented Methods for NLP
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F eppalapally-etal-2024-kapqa
%X Question-answering for domain-specific applications has recently attracted much interest due to the latest advancements in large language models (LLMs). However, accurately assessing the performance of these applications remains a challenge, mainly due to the lack of suitable benchmarks that effectively simulate real-world scenarios. To address this challenge, we introduce two product question-answering (QA) datasets focused on Adobe Acrobat and Photoshop products to help evaluate the performance of existing models on domain-specific product QA tasks. Additionally, we propose a novel knowledge-driven RAG-QA framework to enhance the performance of the models in the product QA task. Our experiments demonstrated that inducing domain knowledge through query reformulation allowed for increased retrieval and generative performance when compared to standard RAG-QA methods. This improvement, however, is slight, and thus illustrates the challenge posed by the datasets introduced.
%U https://aclanthology.org/2024.knowledgenlp-1.2
%P 15-29
Markdown (Informal)
[KaPQA: Knowledge-Augmented Product Question-Answering](https://aclanthology.org/2024.knowledgenlp-1.2) (Eppalapally et al., KnowledgeNLP-WS 2024)
ACL
- Swetha Eppalapally, Daksh Dangi, Chaithra Bhat, Ankita Gupta, Ruiyi Zhang, Shubham Agarwal, Karishma Bagga, Seunghyun Yoon, Nedim Lipka, Ryan Rossi, and Franck Dernoncourt. 2024. KaPQA: Knowledge-Augmented Product Question-Answering. In Proceedings of the 3rd Workshop on Knowledge Augmented Methods for NLP, pages 15–29, Bangkok, Thailand. Association for Computational Linguistics.