mlsanityData to 14 Oct 2026

SPORTSQL: An Interactive System for Real-Time Sports Reasoning and Visualization

arXiv 2508.17157 ↗First posted 23 Aug 2025cs.CLcited by 0

On the map: LLM reasoning → Multimodal reasoning

Abstract (arXiv)

We present a modular, interactive system, SPORTSQL, for natural language querying and visualization of dynamic sports data, with a focus on the English Premier League (EPL). The system translates user questions into executable SQL over a live, temporally indexed database constructed from real-time Fantasy Premier League (FPL) data. It supports both tabular and visual outputs, leveraging the symbolic reasoning capabilities of Large Language Models (LLMs) for query parsing, schema linking, and visualization selection. To evaluate system performance, we introduce the Dynamic Sport Question Answering benchmark (DSQABENCH), comprising 1,700+ queries annotated with SQL programs, gold answers, and database snapshots. Our demo highlights how non-expert users can seamlessly explore evolving sports statistics through a natural, conversational interface.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Multimodal reasoning, one of 17 directions in LLM reasoning. Builds on 13 of its 14 references that are in the map. First posted on arXiv 23 Aug 2025.

Builds on

13 of 14 references.

References · 13

  1. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,139
  2. 2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 688
  3. 2018SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Taskcited by 74
  4. 2023ACT-SQL: In-Context Learning for Text-to-SQL with Automatically-Generated Chain-of-Thoughtcited by 65
  5. 2025Answer Matching Outperforms Multiple Choice for Language Model Evaluationcited by 41
  6. 2023Evaluating Cross-Domain Text-to-SQL Models and Benchmarkscited by 32
  7. 2024SQLBench: A Comprehensive Evaluation for Text-to-SQL Capabilities of Large Language Modelscited by 32
  8. 2024Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow Paradigmcited by 31
  9. 2020LiveQA: A Question Answering Dataset over Sports Livecited by 15
  10. 2025Sparks of Tabular Reasoning via Text2SQL Reinforcement Learningcited by 9
  11. 2024Is This a Bad Table? A Closer Look at the Evaluation of Table Generation from Textcited by 8
  12. 2025Reinforcing Code Generation: Improving Text-to-SQL with Execution-Based Learningcited by 5
1 more
  1. 2025LLM-Symbolic Integration for Robust Temporal Tabular Reasoningcited by 4

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  1. 2024SportQA: A Benchmark for Sports Understanding in Large Language Models
  2. 2024Sports Intelligence: Assessing the Sports Understanding Capabilities of Language Models through Question Answering from Text to Video
  3. 2024Demo: Soccer Information Retrieval via Natural Queries using SoccerRAG
  4. 2024SoccerRAG: Multimodal Soccer Information Retrieval via Natural Queries
  5. 2025SportR: A Benchmark for Multimodal Large Language Model Reasoning in Sports
  6. 2024SportsMetrics: Blending Text and Numerical Data to Understand Information Fusion in LLMs
  7. 2025GridMind: A Multi-Agent NLP Framework for Unified, Cross-Modal NFL Data Insights
  8. 2024SPORTU: A Comprehensive Sports Understanding Benchmark for Multimodal Large Language Models