YNU-HPCC at SemEval-2024 Task 7: Instruction Fine-tuning Models for Numerical Understanding and Generation

Kaiyuan Chen, Jin Wang, Xuejie Zhang


Abstract
This paper presents our systems for Task 7, Numeral-Aware Language Understanding and Generation of SemEval 2024. As participants of Task 7, we engage in all subtasks and implement corresponding systems for each subtask. All subtasks cover three aspects: Quantitative understanding (English), Reading Comprehension of the Numbers in the text (Chinese), and Numeral-Aware Headline Generation (English). Our approach explores employing instruction-tuned models (Flan-T5) or text-to-text models (T5) to accomplish the respective subtasks. We implement the instruction fine-tuning with or without demonstrations and employ similarity-based retrieval or manual methods to construct demonstrations for each example in instruction fine-tuning. Moreover, we reformulate the model’s output into a chain-of-thought format with calculation expressions to enhance its reasoning performance for reasoning subtasks. The competitive results in all subtasks demonstrate the effectiveness of our systems.
Anthology ID:
2024.semeval-1.141
Volume:
Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Atul Kr. Ojha, A. Seza Doğruöz, Harish Tayyar Madabushi, Giovanni Da San Martino, Sara Rosenthal, Aiala Rosá
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
973–979
Language:
URL:
https://aclanthology.org/2024.semeval-1.141
DOI:
10.18653/v1/2024.semeval-1.141
Bibkey:
Cite (ACL):
Kaiyuan Chen, Jin Wang, and Xuejie Zhang. 2024. YNU-HPCC at SemEval-2024 Task 7: Instruction Fine-tuning Models for Numerical Understanding and Generation. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 973–979, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
YNU-HPCC at SemEval-2024 Task 7: Instruction Fine-tuning Models for Numerical Understanding and Generation (Chen et al., SemEval 2024)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-4/2024.semeval-1.141.pdf
Supplementary material:
 2024.semeval-1.141.SupplementaryMaterial.txt
Supplementary material:
 2024.semeval-1.141.SupplementaryMaterial.zip