SpringerSSAT

Fine-tuned PEGASUS-large outperforms LLaMA-3 and GPT-3.5 for automatic research paper title generation

Researchers propose a system for generating research paper titles from abstracts using pre-trained and large language models, evaluated on CSPubSum, LREC-COLING-2024, and a new dataset SpringerSSAT. Fine-tuned PEGASUS-large outperforms fine-tuned LLaMA-3-8B and zero-shot GPT-3.5-turbo across most metrics including ROUGE, METEOR, BERTScore, and SciBERTScore. The work is a narrow NLP application study with limited broader implications for the AI/ML landscape.