mlsanityData to 14 Oct 2026

Large Scale Generative AI Text Applied to Sports and Music

arXiv 2402.15514 ↗First posted 31 Jan 2024cs.CL cs.AIcited by 4

Not on the map. Why?

Abstract (arXiv)

We address the problem of scaling up the production of media content, including commentary and personalized news stories, for large-scale sports and music events worldwide. Our approach relies on generative AI models to transform a large volume of multimodal data (e.g., videos, articles, real-time scoring feeds, statistics, and fact sheets) into coherent and fluent text. Based on this approach, we introduce, for the first time, an AI commentary system, which was deployed to produce automated narrations for highlight packages at the 2023 US Open, Wimbledon, and Masters tournaments. In the same vein, our solution was extended to create personalized content for ESPN Fantasy Football and stories about music artists for the Grammy awards. These applications were built using a common software architecture achieved a 15x speed improvement with an average Rouge-L of 82.00 and perplexity of 6.6. Our work was successfully deployed at the aforementioned events, supporting 90 million fans around the world with 8 billion page views, continuously pushing the bounds on what is possible at the intersection of sports, entertainment, and AI.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 29 of its 38 references that are in the map. First posted on arXiv 31 Jan 2024.

Builds on

29 of 38 references.

References · 29

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020Language Models are Few-Shot Learnerscited by 32,419
  4. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  5. 2023GPT-4 Technical Reportcited by 18,396
  6. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  7. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  8. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  9. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  10. 2022Generative Adversarial Networkscited by 5,627
  11. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  12. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,915
17 more
  1. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,780
  2. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,724
  3. 2020The Pile: An 800GB Dataset of Diverse Text for Language Modelingcited by 2,411
  4. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,218
  5. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  6. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,984
  7. 2021Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolationcited by 1,103
  8. 2014Deep Learning in Neural Networks: An Overviewcited by 770
  9. 2023Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizescited by 737
  10. 2021Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LMcited by 632
  11. 2023Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyondcited by 411
  12. 2022Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizerscited by 204
  13. 2022Pretrained Language Models for Text Generation: A Surveycited by 122
  14. 2023Learning to Retrieve In-Context Examples for Large Language Modelscited by 60
  15. 2021Varuna: Scalable, Low-cost Training of Massive Deep Learning Modelscited by 40
  16. 2022MiCS: Near-linear Scaling for Training Gigantic Model on Public Cloudcited by 22
  17. 2023A Comprehensive Performance Study of Large Language Models on Novel AI Acceleratorscited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025Beyond the Hype: Embeddings vs. Prompting for Multiclass Classification Taskscited by 0
  2. 2025Automated Meta Prompt Engineering for Alignment with the Theory of Mindcited by 0
  3. 2025Match Chat: Real Time Generative AI and Generative Computing for Tenniscited by 0
  4. 2026A Survey of Large Models in Sportscited by 0