YNU-HPCC at SemEval-2024 Task 7: Instruction Fine-tuning Models for Numerical Understanding and Generation
Kaiyuan Chen, Jin Wang, Xuejie Zhang · 2024
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 instructiontuned 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 similaritybased 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.1