2024
NAACL
NAACL 2024
Team NP_PROBLEM at SemEval-2024 Task 7: Numerical Reasoning in Headline Generation with Preference Optimization
Abstract
AbstractWhile large language models (LLMs) exhibit impressive linguistic abilities, their numerical reasoning skills within real-world contexts re- main under-explored. This paper describes our participation in a headline-generation challenge by Numeval at Semeval 2024, which focused on numerical reasoning. Our system achieved an overall top numerical accuracy of 73.49% on the task. We explore the system’s design choices contributing to this result and analyze common error patterns. Our findings highlight the potential and ongoing challenges of integrat- ing numerical reasoning within large language model-based headline generation.
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning and Natural Language Processing
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Artificial Intelligence > Core AI > Foundation Models
Artificial Intelligence > Core AI > Planning
Artificial Intelligence > Learning Paradigms > Few-Shot Learning
Machine Learning > Core Methods > Regression
Machine Learning > Optimization & Theory > Optimization
Machine Learning > Application Areas > Data Augmentation
Natural Language Processing > Generation > Summarization
Natural Language Processing > Generation > Text Generation
Machine Learning > Learning Types > Few-Shot Learning