Summary of Transitgpt: a Generative Ai-based Framework For Interacting with Gtfs Data Using Large Language Models, by Saipraneeth Devunuri et al.
TransitGPT: A Generative AI-based framework for interacting with GTFS data using Large Language Models
by Saipraneeth Devunuri, Lewis Lehe
First submitted to arxiv on: 7 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Applications (stat.AP)
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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 proposed framework leverages Large Language Models (LLMs) to answer natural language queries about General Transit Feed Specification (GTFS) data. This is achieved through a chatbot called TransitGPT, with open-source code, which guides LLMs to generate Python code that extracts and manipulates GTFS data relevant to a query. The framework can perform various tasks, such as data retrieval, calculations, and interactive visualizations, without requiring users to have extensive knowledge of GTFS or programming. This is accomplished by guiding LLMs entirely by prompts, without fine-tuning or access to the actual GTFS feeds. The effectiveness and versatility of TransitGPT are demonstrated through an evaluation on a benchmark dataset of 100 tasks using GPT-4o and Claude-3.5-Sonnet LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way for people to ask questions about public transportation data, like bus schedules or routes. It uses special computer models called Large Language Models (LLMs) to answer these questions without needing programming knowledge. The system is called TransitGPT and it can do many things, such as showing maps or calculating travel times. The LLMs are told what to do through simple prompts, not by actually looking at the transportation data. |
Keywords
» Artificial intelligence » Claude » Fine tuning » Gpt
