mlsanityData to 7 Oct 2026

Large Language Models are Few-Shot Health Learners

arXiv 2305.15525 ↗First posted 24 May 2023cs.CL cs.LGcited by 115

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Abstract (arXiv)

Large language models (LLMs) can capture rich representations of concepts that are useful for real-world tasks. However, language alone is limited. While existing LLMs excel at text-based inferences, health applications require that models be grounded in numerical data (e.g., vital signs, laboratory values in clinical domains; steps, movement in the wellness domain) that is not easily or readily expressed as text in existing training corpus. We demonstrate that with only few-shot tuning, a large language model is capable of grounding various physiological and behavioral time-series data and making meaningful inferences on numerous health tasks for both clinical and wellness contexts. Using data from wearable and medical sensor recordings, we evaluate these capabilities on the tasks of cardiac signal analysis, physical activity recognition, metabolic calculation (e.g., calories burned), and estimation of stress reports and mental health screeners.

Key facts. Cited by 115 papers in the map (data through 7 Oct 2026). Builds on 18 of its 29 references that are in the map. First posted on arXiv 24 May 2023.

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18 of 29 references.

References · 18

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  3. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  4. 2020Scaling Laws for Neural Language Modelscited by 6,411
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  7. 2022Do As I Can, Not As I Say: Grounding Language in Robotic Affordancescited by 2,302
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  12. 2020Domain-Specific Language Model Pretraining for Biomedical Natural Language Processingcited by 996
6 more
  1. 2023How well do Large Language Models perform in Arithmetic tasks?cited by 130
  2. 2021MAD: A Scalable Dataset for Language Grounding in Videos from Movie Audio Descriptionscited by 107
  3. 2018Grounded Video Descriptioncited by 78
  4. 2022Limitations of Language Models in Arithmetic and Symbolic Inductioncited by 71
  5. 2017Image-Grounded Conversations: Multimodal Context for Natural Question and Response Generationcited by 59
  6. 2021Text-to-Audio Grounding: Building Correspondence Between Captions and Sound Eventscited by 16

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 15; 2024: 42; 2025: 38; 2026: 20.

115 papers.

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  1. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 680
  2. 2025HealthBench: Evaluating Large Language Models Towards Improved Human Healthcited by 385
  3. 2024Foundation Models for Time Series Analysis: A Tutorial and Surveycited by 234
  4. 2023TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Seriescited by 165
  5. 2024Are Language Models Actually Useful for Time Series Forecasting?cited by 163
  6. 2023ReviewerGPT? An Exploratory Study on Using Large Language Models for Paper Reviewingcited by 118
  7. 2024Large Language Models for Time Series: A Surveycited by 113
  8. 2023Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlookcited by 109
  9. 2024CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuningcited by 105
  10. 2024From Matching to Generation: A Survey on Generative Information Retrievalcited by 89
  11. 2024Language Models Still Struggle to Zero-shot Reason about Time Seriescited by 81
  12. 2023LLMs Accelerate Annotation for Medical Information Extractioncited by 77
38 more
  1. 2023Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Datacited by 66
  2. 2024Health-LLM: Large Language Models for Health Prediction via Wearable Sensor Datacited by 65
  3. 2023Penetrative AI: Making LLMs Comprehend the Physical Worldcited by 64
  4. 2024Empowering Time Series Analysis with Large Language Models: A Surveycited by 60
  5. 2024Scaling Wearable Foundation Modelscited by 57
  6. 2024FLUX: Fast Software-based Communication Overlap On GPUs Through Kernel Fusioncited by 53
  7. 2025SensorLM: Learning the Language of Wearable Sensorscited by 44
  8. 2023LLMR: Real-time Prompting of Interactive Worlds using Large Language Modelscited by 39
  9. 2025TimeXL: Explainable Multi-modal Time Series Prediction with LLM-in-the-Loopcited by 39
  10. 2024Towards Time Series Reasoning with LLMscited by 36
  11. 2024SensorLLM: Aligning Large Language Models with Motion Sensors for Human Activity Recognitioncited by 35
  12. 2023The first step is the hardest: Pitfalls of Representing and Tokenizing Temporal Data for Large Language Modelscited by 34
  13. 2025Context-Alignment: Activating and Enhancing LLM Capabilities in Time Seriescited by 34
  14. 2025TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agentscited by 33
  15. 2025OpenTSLM: Time-Series Language Models for Reasoning over Multivariate Medical Text- and Time-Series Datacited by 30
  16. 2024LLMSense: Harnessing LLMs for High-level Reasoning Over Spatiotemporal Sensor Tracescited by 28
  17. 2024EEG-GPT: Exploring Capabilities of Large Language Models for EEG Classification and Interpretationcited by 26
  18. 2023TENT: Connect Language Models with IoT Sensors for Zero-Shot Activity Recognitioncited by 25
  19. 2024Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learningcited by 25
  20. 2024Towards a Personal Health Large Language Modelcited by 25
  21. 2025Multi-modal Time Series Analysis: A Tutorial and Surveycited by 24
  22. 2024Empowering Time Series Analysis with Foundation Models: A Comprehensive Surveycited by 23
  23. 2024Prompting Large Language Models for Zero-Shot Clinical Prediction with Structured Longitudinal Electronic Health Record Datacited by 22
  24. 2024Mobility-LLM: Learning Visiting Intentions and Travel Preferences from Human Mobility Data with Large Language Modelscited by 21
  25. 2023Exploring and Characterizing Large Language Models For Embedded System Development and Debuggingcited by 20
  26. 2025How Can Time Series Analysis Benefit From Multiple Modalities? A Survey and Outlookcited by 20
  27. 2024Chain-of-Interaction: Enhancing Large Language Models for Psychiatric Behavior Understanding by Dyadic Contextscited by 19
  28. 2024Guided Profile Generation Improves Personalization with LLMscited by 19
  29. 2023User Modeling in the Era of Large Language Models: Current Research and Future Directionscited by 18
  30. 2025Large language models for artificial general intelligence (AGI): A survey of foundational principles and approachescited by 18
  31. 2024Evaluating Large Language Models as Virtual Annotators for Time-series Physical Sensing Datacited by 17
  32. 2025AutoIOT: LLM-Driven Automated Natural Language Programming for AIoT Applicationscited by 17
  33. 2024Personalized Adapter for Large Meteorology Model on Devices: Towards Weather Foundation Modelscited by 15
  34. 2024MedTsLLM: Leveraging LLMs for Multimodal Medical Time Series Analysiscited by 15
  35. 2024Large Language Models are Zero-Shot Recognizers for Activities of Daily Livingcited by 13
  36. 2024By My Eyes: Grounding Multimodal Large Language Models with Sensor Data via Visual Promptingcited by 12
  37. 2024Tri-Level Navigator: LLM-Empowered Tri-Level Learning for Time Series OOD Generalizationcited by 12
  38. 2023From Classification to Clinical Insights: Towards Analyzing and Reasoning About Mobile and Behavioral Health Data With Large Language Modelscited by 11

50 most cited of 115 All 115 (JSON)