--- title: "Who cites SensorLLM: Aligning Large — 6 citing papers" description: "6 papers of the Elamur Research library cite «SensorLLM: Aligning Large Language Models with Motion Sensors for Human Activity…" canonical: "https://research.elamur.ai/free/en/papers/li-2025-sensorllm-aligning-large-language-models/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/li-2025-sensorllm-aligning-large-language-models/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [SensorLLM: Aligning Large Language Models with…](https://md.research.elamur.ai/free/en/papers/li-2025-sensorllm-aligning-large-language-models/) / cited by # Who cites «SensorLLM: Aligning Large Language Models with Motion Sensors for Human Activity Recognition» 6 papers of our library cite it (of the 149,439 whose bibliographies we read) · 2 with a page here, the most cited first · [← back to «SensorLLM: Aligning Large Language Models with Motion Sensors for Human Activity Recognition»](https://md.research.elamur.ai/free/en/papers/li-2025-sensorllm-aligning-large-language-models/) 1. [**LLaSA: A Sensor-Aware LLM for Natural Language Reasoning of Human Activity from IMU Data arXiv:2406.14498**Sheikh Asif Imran, Mohammad Nur Hossain Khan, Subrata Biswas, et al.*2024 · 11 citations*](https://md.research.elamur.ai/free/en/papers/imran-2024-llasa-sensor-aware-llm-natural/) 2. **[Inferring Events from Time Series using Language Models](https://md.research.elamur.ai/free/en/papers/tan-2026-inferring-events-time-series-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mingtian Tan, Mike A Merrill, Zachary Gottesman, et al.*2026 · 1 citation* 4 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/li-2025-sensorllm-aligning-large-language-models/) — SensorLLM: Aligning Large Language Models with Motion Sensors for Human Activity Recognition