Summary of Onlysportslm: Optimizing Sports-domain Language Models with Sota Performance Under Billion Parameters, by Zexin Chen et al.
OnlySportsLM: Optimizing Sports-Domain Language Models with SOTA Performance under Billion Parameters
by Zexin Chen, Chengxi Li, Xiangyu Xie, Parijat Dube
First submitted to arxiv on: 30 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the potential of small language models trained on sports-related data to overcome model size constraints. It introduces the OnlySports collection, comprising dataset, benchmark, and a 196M parameters model with optimized architecture. The study achieves a 37.62%/34.08% accuracy improvement over previous state-of-the-art models in the sports domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows that small language models can be trained to excel at specific tasks, like understanding sports. It uses lots of data and special techniques to make a model that’s really good at this job. The result is a model that performs as well as bigger models in the same area. |
