README.md

metadata

license: cc-by-nc-4.0
task_categories:
  - question-answering
  - text-generation
  - summarization
language:
  - en
tags:
  - sports
  - nba
  - nfl
  - reasoning
  - long-context
pretty_name: SportsMetrics
size_categories:
  - 1K<n<10K

SportsMetrics

Benchmark data to evaluate numerical reasoning and information fusion of LLMs.

SportsMetrics: Blending Text and Numerical Data to Understand Information Fusion in LLMs
Yebowen Hu, Kaiqiang Song, Sangwoo Cho, Xiaoyang Wang, Hassan Foroosh, Dong Yu, Fei Liu
In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL'24), Bangkok, Thailand.
Arxiv Paper

Usage

  from datasets import load_dataset
  
  def get_task(domain, task):
      bench_data = []
      dataset = load_dataset("huuuyeah/SportsMetrics",split="test")
      for instance in dataset:
          if instance["domain"]==domain and instance["task"]==task:
              bench_data.append(instance)
      return bench_data
  
  def message_iter(domain, task):
      bench_data = get_task(domain, task)
      if len(bench_data) == 0:
          print("No data loaded.")
          return
      
      for instance in bench_data:
          messages = [
              {"role": "system", "content": instance["system"]},
              {"role": "user", "content": instance["user"]}
          ]
          yield messages
  
      return

Benchmark Tasks

The LLM is mandatorily required to generate responses in JSON format.

Reasoning Task

• reasoning-team_points_tracking: (NBA) Tracking team points in one match.
• reasoning-key_stats_tracking: (NBA, NFL) Tracking the key statistics for sports analytics.

Conflicts Task

• conflict-one_point_rule: (NBA) All scoring actions in the competition are set to be worth only one point.
• conflict-swap_{num}_players: (NBA) Swap {num} of spalyer between two teams.

Robustness Task

• robustness-duplicate_{prob}: (NBA) Replicate the non-scoring move with a probability of {prob}.
• robustness-remove_{prob}: (NBA) Remove the non-scoring move with a probability of {prob}.
• robustness-shuffled_pbp: (NBA) Shuffle the order of all moves in play-by-play descriptions while maintain the original order of timestamps.
• robustness-{num}_fiction_names: (NFL) Randomly select {num} of players from both teams and replace them with names from fiction movies.

Bibtex

@misc{hu2024sportsmetricsblendingtextnumerical,
      title={SportsMetrics: Blending Text and Numerical Data to Understand Information Fusion in LLMs}, 
      author={Yebowen Hu and Kaiqiang Song and Sangwoo Cho and Xiaoyang Wang and Hassan Foroosh and Dong Yu and Fei Liu},
      year={2024},
      eprint={2402.10979},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2402.10979}, 
}