Papers
680 papers found
The Gaps between Fine Tuning and In-context Learning in Bias Evaluation and Debiasing
Masahiro Kaneko, Danushka Bollegala, Timothy Baldwin
Emergence of symbolic abstraction heads for in-context learning in large language models
Ali Al-Saeedi, Aki Harma
Resources and Few-shot Learners for In-context Learning in Slavic Languages
Michal Štefánik, Marek Kadlčík, Piotr Gramacki et al.
Symbol tuning improves in-context learning in language models
Jerry Wei, Le Hou, Andrew Lampinen et al.
Toward Efficient Sparse Autoencoder-Guided Steering for Improved In-Context Learning in Large Language Models
Ikhyun Cho, Julia Hockenmaier
Understanding In-Context Learning in Transformers and LLMs by Learning to Learn Discrete Functions
Satwik Bhattamishra, Arkil Patel, Phil Blunsom et al.
Understanding the Generalization of In-Context Learning in Transformers: An Empirical Study
Xingxuan Zhang, Haoran Wang, Jiansheng Li et al.
HGOT: Hierarchical Graph of Thoughts for Retrieval-Augmented In-Context Learning in Factuality Evaluation
Yihao Fang, Stephen Thomas, Xiaodan Zhu
Learning vs Retrieval: The Role of In-Context Examples in Regression with Large Language Models
Aliakbar Nafar, K. Brent Venable, Parisa Kordjamshidi
PICLe: Pseudo-annotations for In-Context Learning in Low-Resource Named Entity Detection
Sepideh Mamooler, Syrielle Montariol, Alexander Mathis et al.
AMR-RE: Abstract Meaning Representations for Retrieval-Based In-Context Learning in Relation Extraction
Peitao Han, Lis Pereira, Fei Cheng et al.
Beyond Plain Demos: A Demo-Centric Anchoring Paradigm for In-Context Learning in Alzheimer’s Disease Detection
Puzhen Su, Haoran Yin, Miao Yongzhu et al.
Where does In-context Learning Happen in Large Language Models?
Suzanna Sia, David Mueller, Kevin Duh
How Transformers Utilize Multi-Head Attention in In-Context Learning? A Case Study on Sparse Linear Regression
Xingwu Chen, Lei Zhao, Difan Zou
Large Language Models Can be Lazy Learners: Analyze Shortcuts in In-Context Learning
Ruixiang Tang, Dehan Kong, Longtao Huang et al.
Revisiting Demonstration Selection Strategies in In-Context Learning
Keqin Peng, Liang Ding, Yancheng Yuan et al.
Addressing Order Sensitivity of In-Context Demonstration Examples in Causal Language Models
Yanzheng Xiang, Hanqi Yan, Lin Gui et al.
DistillMIKE: Editing Distillation of Massive In-Context Knowledge Editing in Large Language Models
Shanbao Qiao, Xuebing Liu, Seung-Hoon Na
What Makes a Good Order of Examples in In-Context Learning
Qi Guo, Leiyu Wang, Yidong Wang et al.
Delta-KNN: Improving Demonstration Selection in In-Context Learning for Alzheimer’s Disease Detection
Chuyuan Li, Raymond Li, Thalia S. Field et al.
On the Relation between Sensitivity and Accuracy in In-Context Learning
Yanda Chen, Chen Zhao, Zhou Yu et al.
Ethical Reasoning over Moral Alignment: A Case and Framework for In-Context Ethical Policies in LLMs
Abhinav Rao, Aditi Khandelwal, Kumar Tanmay et al.
Generating Demonstrations for In-Context Compositional Generalization in Grounded Language Learning
Sam Spilsbury, Pekka Marttinen, Alexander Ilin
Adapting LLM Predictions in In-Context Learning with Data Priors
Javier Chiyah-Garcia, Prasoon Goyal, Michael Johnston et al.
ICL CIPHERS: Quantifying ”Learning” in In-Context Learning via Substitution Ciphers
Zhouxiang Fang, Aayush Mishra, Muhan Gao et al.