3 papers
cs.LG2026
Investigation into In-Context Learning Capabilities of Transformers
Rushil Chandrupatla, Leo Bangayan, Sebastian Leng
Transformers have demonstrated a strong ability for in-context learning (ICL), enabling models to solve previously unseen tasks using only example input output pairs provided at in…
cs.AI2025
SensorChat: Answering Qualitative and Quantitative Questions during Long-Term Multimodal Sensor Interactions
Xiaofan Yu, Lanxiang Hu, Benjamin Reichman +5
Natural language interaction with sensing systems is crucial for addressing users' personal concerns and providing health-related insights into their daily lives. When a user asks…
cs.CL2025
SensorQA: A Question Answering Benchmark for Daily-Life Monitoring
Benjamin Reichman, Xiaofan Yu, Lanxiang Hu +5
With the rapid growth in sensor data, effectively interpreting and interfacing with these data in a human-understandable way has become crucial. While existing research primarily f…