{"ID":2840923,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.12472","arxiv_id":"2511.12472","title":"Assessing LLMs for Serendipity Discovery in Knowledge Graphs: A Case for Drug Repurposing","abstract":"Large Language Models (LLMs) have greatly advanced knowledge graph question answering (KGQA), yet existing systems are typically optimized for returning highly relevant but predictable answers. A missing yet desired capacity is to exploit LLMs to suggest surprise and novel (\"serendipitious\") answers. In this paper, we formally define the serendipity-aware KGQA task and propose the SerenQA framework to evaluate LLMs' ability to uncover unexpected insights in scientific KGQA tasks. SerenQA includes a rigorous serendipity metric based on relevance, novelty, and surprise, along with an expert-annotated benchmark derived from the Clinical Knowledge Graph, focused on drug repurposing. Additionally, it features a structured evaluation pipeline encompassing three subtasks: knowledge retrieval, subgraph reasoning, and serendipity exploration. Our experiments reveal that while state-of-the-art LLMs perform well on retrieval, they still struggle to identify genuinely surprising and valuable discoveries, underscoring a significant room for future improvements. Our curated resources and extended version are released at: https://cwru-db-group.github.io/serenQA.","short_abstract":"Large Language Models (LLMs) have greatly advanced knowledge graph question answering (KGQA), yet existing systems are typically optimized for returning highly relevant but predictable answers. A missing yet desired capacity is to exploit LLMs to suggest surprise and novel (\"serendipitious\") answers. In this paper, we...","url_abs":"https://arxiv.org/abs/2511.12472","url_pdf":"https://arxiv.org/pdf/2511.12472v1","authors":"[\"Mengying Wang\",\"Chenhui Ma\",\"Ao Jiao\",\"Tuo Liang\",\"Pengjun Lu\",\"Shrinidhi Hegde\",\"Yu Yin\",\"Evren Gurkan-Cavusoglu\",\"Yinghui Wu\"]","published":"2025-11-16T06:19:53Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\",\"LoRA\"]","has_code":false}