Context Learning
In-context learning (ICL) is a paradigm shift in machine learning, focusing on enabling models to adapt to new tasks using only a few examples provided within the input, without requiring parameter updates. Current research emphasizes understanding ICL's mechanisms, particularly within transformer-based large language models, and improving its effectiveness through techniques like enhanced example selection, chain-of-thought prompting, and addressing issues such as spurious correlations and copy bias. This research is significant because ICL offers a more efficient and adaptable approach to many machine learning problems, impacting fields ranging from natural language processing and computer vision to scientific computing and beyond.
Papers
Timeline-based Sentence Decomposition with In-Context Learning for Temporal Fact Extraction
Jianhao Chen, Haoyuan Ouyang, Junyang Ren, Wentao Ding, Wei Hu, Yuzhong Qu
Many-Shot In-Context Learning in Multimodal Foundation Models
Yixing Jiang, Jeremy Irvin, Ji Hun Wang, Muhammad Ahmed Chaudhry, Jonathan H. Chen, Andrew Y. Ng
Human-centric Reward Optimization for Reinforcement Learning-based Automated Driving using Large Language Models
Ziqi Zhou, Jingyue Zhang, Jingyuan Zhang, Yangfan He, Boyue Wang, Tianyu Shi, Alaa Khamis
Locally Differentially Private In-Context Learning
Chunyan Zheng, Keke Sun, Wenhao Zhao, Haibo Zhou, Lixin Jiang, Shaoyang Song, Chunlai Zhou
Evaluation of Few-Shot Learning for Classification Tasks in the Polish Language
Tsimur Hadeliya, Dariusz Kajtoch
Recall, Retrieve and Reason: Towards Better In-Context Relation Extraction
Guozheng Li, Peng Wang, Wenjun Ke, Yikai Guo, Ke Ji, Ziyu Shang, Jiajun Liu, Zijie Xu
Meta In-Context Learning Makes Large Language Models Better Zero and Few-Shot Relation Extractors
Guozheng Li, Peng Wang, Jiajun Liu, Yikai Guo, Ke Ji, Ziyu Shang, Zijie Xu
XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference
João Monteiro, Étienne Marcotte, Pierre-André Noël, Valentina Zantedeschi, David Vázquez, Nicolas Chapados, Christopher Pal, Perouz Taslakian
Bayesian Example Selection Improves In-Context Learning for Speech, Text, and Visual Modalities
Siyin Wang, Chao-Han Huck Yang, Ji Wu, Chao Zhang