SUMMIR: A Hallucination-Aware Framework for Ranking Sports Insights from LLMs
With the rapid proliferation of online sports journalism, extracting meaningful pre-game and post-game insights from articles is essential for enhancing user engagement and comprehension. In this paper, we address the task of automatically extracting such insights from articles published before and after matches. We curate a dataset of 7,900 news articles covering 800 matches across four major sports: Cricket, Soccer, Basketball, and Baseball. To ensure contextual relevance, we employ a two-step validation pipeline leveraging both open-source and proprietary large language models (LLMs). We then utilize multiple state-of-the-art LLMs (GPT-4o, Qwen2.5-72B-Instruct, Llama-3.3-70B-Instruct, ...
| Authors | Nitish Kumar, Sannu Kumar, S Akash et al. |
|---|---|
| Primary category | cs.IR |
| Published | 30 March 2026 |
| Last updated at source | 30 March 2026 |
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