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**SportsMetrics
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# SportsMetrics
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Benchmark data to evaluate numerical reasoning and information fusion of LLMs.
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**SportsMetrics: Blending Text and Numerical Data to Understand Information Fusion in LLMs** \
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Yebowen Hu, Kaiqiang Song, Sangwoo Cho, Xiaoyang Wang, Hassan Foroosh, Dong Yu, Fei Liu \
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[*In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL'24), Bangkok, Thailand.*](https://2024.aclweb.org/program/main_conference_papers/) \
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[Arxiv Paper](https://arxiv.org/abs/2402.10979)
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## Benchmark Tasks
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The LLM is mandatorily required to generate responses in JSON format.
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### Reasoning Task
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- **reasoning-team_points_tracking**: Tracking team points in one match.
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- **reasoning-key_stats_tracking**: Tracking the key statistics for sports analytics.
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### Conflicts Task
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- **conflict-one_point_rule**: All scoring actions in the competition are set to be worth only one point.
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- **conflict-swap_{num}_players**: Swap {num} of spalyer between two teams.
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### Robustness Task
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- **robustness-duplicate_{prob}**: Replicate the non-scoring move with a probability of {prob}.
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- **robustness-remove_{prob}**: Remove the non-scoring move with a probability of {prob}.
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- **robustness-shuffled_pbp**: Shuffle the order of all moves in play-by-play descriptions while maintain the original order of timestamps.
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- **robustness-{num}_fiction_names**: Randomly select {num} of players from both teams and replace them with names from fiction movies.
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**Bibtex**
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```
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@misc{hu2024sportsmetricsblendingtextnumerical,
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title={SportsMetrics: Blending Text and Numerical Data to Understand Information Fusion in LLMs},
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author={Yebowen Hu and Kaiqiang Song and Sangwoo Cho and Xiaoyang Wang and Hassan Foroosh and Dong Yu and Fei Liu},
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year={2024},
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eprint={2402.10979},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2402.10979},
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}
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