1 citations · 1 across the 4 of their papers we have counts for
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RELOOP: Recursive Retrieval with Multi-Hop Reasoner and Planners for Heterogeneous QA
Ruiyi Yang, Hao Xue, Imran Razzak +2
Retrieval-augmented generation (RAG) remains brittle on multi-step questions and heterogeneous evidence sources, trading accuracy against latency and token/tool budgets. This paper…
ZARA: Training-Free Motion Time-Series Reasoning via Evidence-Grounded LLM Agents
Zechen Li, Baiyu Chen, Hao Xue +1
Motion sensor time-series are central to Human Activity Recognition (HAR), yet conventional approaches are constrained to fixed activity sets and typically require costly parameter…
Evaluating the Bias in LLMs for Surveying Opinion and Decision Making in Healthcare
Yonchanok Khaokaew, Flora D. Salim, Andreas Züfle +5
Generative agents have been increasingly used to simulate human behaviour in silico, driven by large language models (LLMs). These simulacra serve as sandboxes for studying human b…
RIDE: Enhancing Large Language Model Alignment through Restyled In-Context Learning Demonstration Exemplars
Yuncheng Hua, Lizhen Qu, Zhuang Li +3
Alignment tuning is crucial for ensuring large language models (LLMs) behave ethically and helpfully. Current alignment approaches require high-quality annotations and significant…
SensorLLM: Aligning Large Language Models with Motion Sensors for Human Activity Recognition
Zechen Li, Shohreh Deldari, Linyao Chen +2
We introduce SensorLLM, a two-stage framework that enables Large Language Models (LLMs) to perform human activity recognition (HAR) from sensor time-series data. Despite their stro…