@inproceedings{kurdiukov-etal-2024-nlp,
title = "nlp{\_}enjoyers at {T}ext{G}raphs-17 Shared Task: Text-Graph Representations for Knowledge Graph Question Answering using all-{MPN}et",
author = "Kurdiukov, Nikita and
Zinkovich, Viktoriia and
Karpukhin, Sergey and
Tikhomirov, Pavel",
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-1.10",
pages = "126--130",
abstract = "This paper presents a model for solving the Multiple Choice Question Answering (MCQA) problem, focusing on the impact of subgraph extraction from a Knowledge Graph on model performance. The proposed method combines textual and graph information by adding linearized subgraphs directly into the main question prompt with separate tokens, enhancing the performance of models working with each modality separately. The study also includes an examination of Large Language Model (LLM) backbones and the benefits of linearized subgraphs and sequence length, with efficient training achieved through fine-tuning with LoRA. The top benchmark, using subgraphs and MPNet, achieved an F1 score of 0.3887. The main limitation of the experiments is the reliance on pre-generated subgraphs/triplets from the graph, and the lack of exploration of in-context learning and prompting strategies with decoder-based architectures.",
}
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<abstract>This paper presents a model for solving the Multiple Choice Question Answering (MCQA) problem, focusing on the impact of subgraph extraction from a Knowledge Graph on model performance. The proposed method combines textual and graph information by adding linearized subgraphs directly into the main question prompt with separate tokens, enhancing the performance of models working with each modality separately. The study also includes an examination of Large Language Model (LLM) backbones and the benefits of linearized subgraphs and sequence length, with efficient training achieved through fine-tuning with LoRA. The top benchmark, using subgraphs and MPNet, achieved an F1 score of 0.3887. The main limitation of the experiments is the reliance on pre-generated subgraphs/triplets from the graph, and the lack of exploration of in-context learning and prompting strategies with decoder-based architectures.</abstract>
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%0 Conference Proceedings
%T nlp_enjoyers at TextGraphs-17 Shared Task: Text-Graph Representations for Knowledge Graph Question Answering using all-MPNet
%A Kurdiukov, Nikita
%A Zinkovich, Viktoriia
%A Karpukhin, Sergey
%A Tikhomirov, Pavel
%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 kurdiukov-etal-2024-nlp
%X This paper presents a model for solving the Multiple Choice Question Answering (MCQA) problem, focusing on the impact of subgraph extraction from a Knowledge Graph on model performance. The proposed method combines textual and graph information by adding linearized subgraphs directly into the main question prompt with separate tokens, enhancing the performance of models working with each modality separately. The study also includes an examination of Large Language Model (LLM) backbones and the benefits of linearized subgraphs and sequence length, with efficient training achieved through fine-tuning with LoRA. The top benchmark, using subgraphs and MPNet, achieved an F1 score of 0.3887. The main limitation of the experiments is the reliance on pre-generated subgraphs/triplets from the graph, and the lack of exploration of in-context learning and prompting strategies with decoder-based architectures.
%U https://aclanthology.org/2024.textgraphs-1.10
%P 126-130
Markdown (Informal)
[nlp_enjoyers at TextGraphs-17 Shared Task: Text-Graph Representations for Knowledge Graph Question Answering using all-MPNet](https://aclanthology.org/2024.textgraphs-1.10) (Kurdiukov et al., TextGraphs-WS 2024)
ACL