# Spatio-temporal Storytelling? Leveraging Generative Models for Semantic Trajectory Analysis

arXiv: https://arxiv.org/abs/2306.13905 · HTML: https://mlsanity.com/paper/2306.13905/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 24 Jun 2023.

## Builds on (most cited first, 3 of 3)

- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [Galactica: A Large Language Model for Science](https://mlsanity.com/paper/2211.09085/) (2022)
- [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://mlsanity.com/paper/1906.05317/) (2019)

## Built on by (most cited first, 1 of 1)

- [Position: What Can Large Language Models Tell Us about Time Series Analysis](https://mlsanity.com/paper/2402.02713/) (2024)

## Abstract (arXiv)

In this paper, we lay out a vision for analysing semantic trajectory traces and generating synthetic semantic trajectory data (SSTs) using generative language model. Leveraging the advancements in deep learning, as evident by progress in the field of natural language processing (NLP), computer vision, etc. we intend to create intelligent models that can study the semantic trajectories in various contexts, predicting future trends, increasing machine understanding of the movement of animals, humans, goods, etc. enhancing human-computer interactions, and contributing to an array of applications ranging from urban-planning to personalized recommendation engines and business strategy.