We focus on personalized news headline generation, where we aim to improve headline generation by extending the generation context to incorporate the news reading history of users. In particular, we study a RAG-LLM-based system that customizes news headlines with user histories to improve news headline personalization. Our experiments show that our approach not only produces better headlines for specific users, but also makes the generated headlines closer to the original headlines. We experiment with different retrievers and analyze the generated outputs through systematic comparisons with both original and rewritten headlines. These analyses provide insights into the role of retrieval and personalization in headline generation, highlighting how the user history contributes to meaningful improvement while remaining aligned with original headlines.

This paper presents our strategies in SemEval 2020 Task 4: Commonsense Validation and Explanation. We propose a novel way to search for evidence and choose the different large-scale pre-trained models as the backbone for three subtasks. The results show that our evidence-searching approach improves model performance on commonsense explanation task. Our team ranks 2nd in subtask C according to human evaluation score.