3 papers
cs.CL2025
Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge
Ying Zhang, Benjamin Heinzerling, Dongyuan Li +1
While fine-tuning is the standard for injecting factual knowledge into large language models (LLMs), the mechanisms enabling reliable fact recall via unseen queries remain poorly u…
cs.CL2021
Transformer-based Lexically Constrained Headline Generation
Kosuke Yamada, Yuta Hitomi, Hideaki Tamori +4
This paper explores a variant of automatic headline generation methods, where a generated headline is required to include a given phrase such as a company or a product name. Previo…
cs.CL2019
A Large-Scale Multi-Length Headline Corpus for Analyzing Length-Constrained Headline Generation Model Evaluation
Yuta Hitomi, Yuya Taguchi, Hideaki Tamori +5
Browsing news articles on multiple devices is now possible. The lengths of news article headlines have precise upper bounds, dictated by the size of the display of the relevant dev…