@inproceedings{tang-etal-2024-hw,
title = "{HW}-{TSC} at {T}ext{G}raphs-17 Shared Task: Enhancing Inference Capabilities of {LLM}s with Knowledge Graphs",
author = "Tang, Wei and
Qiao, Xiaosong and
Zhao, Xiaofeng and
Zhang, Min and
Su, Chang and
Li, Yuang and
Li, Yinglu and
Liu, Yilun and
Yao, Feiyu and
Tao, Shimin and
Yang, Hao and
Xianghui, He",
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-1.11",
pages = "131--136",
abstract = "In this paper, we present an effective method for TextGraphs-17 Shared Task. This task requires selecting an entity from the candidate entities that is relevant to the given question and answer. The selection process is aided by utilizing the shortest path graph in the knowledge graph, connecting entities in the query to the candidate entity. This task aims to explore how to enhance LLMs output with KGs, although current LLMs have certain logical reasoning capabilities, they may not be certain about their own outputs, and the answers they produce may be correct by chance through incorrect paths. In this case, we have introduced a LLM prompt design strategy based on self-ranking and emotion. Specifically, we let the large model score its own answer choices to reflect its confidence in the answer. Additionally, we add emotional incentives to the prompts to encourage the model to carefully examine the questions. Our submissions was conducted under zero-resource setting, and we achieved the second place in the task with an F1-score of 0.8321.",
}
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<abstract>In this paper, we present an effective method for TextGraphs-17 Shared Task. This task requires selecting an entity from the candidate entities that is relevant to the given question and answer. The selection process is aided by utilizing the shortest path graph in the knowledge graph, connecting entities in the query to the candidate entity. This task aims to explore how to enhance LLMs output with KGs, although current LLMs have certain logical reasoning capabilities, they may not be certain about their own outputs, and the answers they produce may be correct by chance through incorrect paths. In this case, we have introduced a LLM prompt design strategy based on self-ranking and emotion. Specifically, we let the large model score its own answer choices to reflect its confidence in the answer. Additionally, we add emotional incentives to the prompts to encourage the model to carefully examine the questions. Our submissions was conducted under zero-resource setting, and we achieved the second place in the task with an F1-score of 0.8321.</abstract>
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%0 Conference Proceedings
%T HW-TSC at TextGraphs-17 Shared Task: Enhancing Inference Capabilities of LLMs with Knowledge Graphs
%A Tang, Wei
%A Qiao, Xiaosong
%A Zhao, Xiaofeng
%A Zhang, Min
%A Su, Chang
%A Li, Yuang
%A Li, Yinglu
%A Liu, Yilun
%A Yao, Feiyu
%A Tao, Shimin
%A Yang, Hao
%A Xianghui, He
%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 tang-etal-2024-hw
%X In this paper, we present an effective method for TextGraphs-17 Shared Task. This task requires selecting an entity from the candidate entities that is relevant to the given question and answer. The selection process is aided by utilizing the shortest path graph in the knowledge graph, connecting entities in the query to the candidate entity. This task aims to explore how to enhance LLMs output with KGs, although current LLMs have certain logical reasoning capabilities, they may not be certain about their own outputs, and the answers they produce may be correct by chance through incorrect paths. In this case, we have introduced a LLM prompt design strategy based on self-ranking and emotion. Specifically, we let the large model score its own answer choices to reflect its confidence in the answer. Additionally, we add emotional incentives to the prompts to encourage the model to carefully examine the questions. Our submissions was conducted under zero-resource setting, and we achieved the second place in the task with an F1-score of 0.8321.
%U https://aclanthology.org/2024.textgraphs-1.11
%P 131-136
Markdown (Informal)
[HW-TSC at TextGraphs-17 Shared Task: Enhancing Inference Capabilities of LLMs with Knowledge Graphs](https://aclanthology.org/2024.textgraphs-1.11) (Tang et al., TextGraphs-WS 2024)
ACL
- Wei Tang, Xiaosong Qiao, Xiaofeng Zhao, Min Zhang, Chang Su, Yuang Li, Yinglu Li, Yilun Liu, Feiyu Yao, Shimin Tao, Hao Yang, and He Xianghui. 2024. HW-TSC at TextGraphs-17 Shared Task: Enhancing Inference Capabilities of LLMs with Knowledge Graphs. In Proceedings of TextGraphs-17: Graph-based Methods for Natural Language Processing, pages 131–136, Bangkok, Thailand. Association for Computational Linguistics.