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How Can Large Language Models Understand Spatial-Temporal Data?

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arxiv 2401.14192 v2 pith:VKT5FH2U submitted 2024-01-25 cs.LG cs.CL

Lei Liu , Shuo Yu , Runze Wang , Zhenxun Ma , Yanming Shen This is my paper
classification cs.LGcs.CL
keywords spatial-temporaldatalanguageforecastingllmsapproachgraphlarge
verification ladder T0 review T1 audit T2 compute T3 formal
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While Large Language Models (LLMs) dominate tasks like natural language processing and computer vision, harnessing their power for spatial-temporal forecasting remains challenging. The disparity between sequential text and complex spatial-temporal data hinders this application. To address this issue, this paper introduces STG-LLM, an innovative approach empowering LLMs for spatial-temporal forecasting. We tackle the data mismatch by proposing: 1) STG-Tokenizer: This spatial-temporal graph tokenizer transforms intricate graph data into concise tokens capturing both spatial and temporal relationships; 2) STG-Adapter: This minimalistic adapter, consisting of linear encoding and decoding layers, bridges the gap between tokenized data and LLM comprehension. By fine-tuning only a small set of parameters, it can effectively grasp the semantics of tokens generated by STG-Tokenizer, while preserving the original natural language understanding capabilities of LLMs. Extensive experiments on diverse spatial-temporal benchmark datasets show that STG-LLM successfully unlocks LLM potential for spatial-temporal forecasting. Remarkably, our approach achieves competitive performance on par with dedicated SOTA methods.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Reprogramming Vision Foundation Models for Spatio-Temporal Forecasting

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    ST-VFM reprograms frozen vision foundation models with temporal-aware token adapters and cross-prompt coordination, and reports state-of-the-art results on ten spatio-temporal forecasting benchmarks.

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    Reinforcement learning trains an LLM to generate improved text from time series, improving multimodal forecasting on Time-MMD.

  3. T-GRAG: A Dynamic GraphRAG Framework for Resolving Temporal Conflicts and Redundancy in Knowledge Retrieval

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    A temporal GraphRAG framework that partitions knowledge graphs by timestamp and retrieves at subgraph, node, and knowledge levels outperforms RAG baselines on a new Audi annual-report QA benchmark.

  4. CoMaPOI: A Collaborative Multi-Agent Framework for Next POI Prediction Bridging the Gap Between Trajectory and Language

    cs.CL 2025-05 conditional novelty 5.0 of 10

    CoMaPOI uses three LLM agents (Profiler, Forecaster, Predictor) with reverse-reasoning fine-tuning to achieve state-of-the-art next-POI prediction on NYC, TKY, and CA.

  5. Large Foundation Models for Trajectory Prediction in Autonomous Driving: A Comprehensive Survey

    cs.RO 2025-09 conditional novelty 4.0 of 10

    A structured survey of LLM-based trajectory prediction methods, organized into trajectory-language mapping, multimodal fusion, and constraint-based reasoning, with benchmarks, metrics, and future directions.

  6. Unraveling Spatio-Temporal Foundation Models via the Pipeline Lens: A Comprehensive Review

    cs.LG 2025-06 conditional novelty 4.0 of 10

    Spatio-temporal foundation models are organized into a pipeline of data harmonization, model design, training, and adaptation, with a data property taxonomy for model selection.

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