@inproceedings{liang-etal-2024-bit,
    title = "Bit{\_}numeval at {S}em{E}val-2024 Task 7: Enhance Numerical Sensitivity and Reasoning Completeness for Quantitative Understanding",
    author = "Liang, Xinyue  and
      Li, Jiawei  and
      Yang, Yizhe  and
      Gao, Yang",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Tayyar Madabushi, Harish  and
      Da San Martino, Giovanni  and
      Rosenthal, Sara  and
      Ros{\'a}, Aiala},
    booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.semeval-1.258/",
    doi = "10.18653/v1/2024.semeval-1.258",
    pages = "1830--1841",
    abstract = "In this paper, we describe the methods used for Quantitative Natural Language Inference (QNLI), and Quantitative Question Answering (QQA) in task1 of Semeval2024 NumEval. The challenge{'}s focus is to enhance the model{'}s quantitative understanding consequently improving its performance on certain tasks. We accomplish this task from two perspectives: (1) By integrating real-world numerical comparison data during the supervised fine-tuning (SFT) phase, we enhanced the model{'}s numerical sensitivity. (2) We develop an innovative reward model scoring mechanism, leveraging reinforcement learning from human feedback (RLHF) techniques to improve the model{'}s reasoning completeness."
}