2 papers
cs.CL2025
Surprise Calibration for Better In-Context Learning
Zhihang Tan, Jingrui Hou, Ping Wang +2
In-context learning (ICL) has emerged as a powerful paradigm for task adaptation in large language models (LLMs), where models infer underlying task structures from a few demonstra…
cs.IR2024
Advancing continual lifelong learning in neural information retrieval: definition, dataset, framework, and empirical evaluation
Jingrui Hou, Georgina Cosma, Axel Finke
Continual learning refers to the capability of a machine learning model to learn and adapt to new information, without compromising its performance on previously learned tasks. Alt…