Papers
5,589 papers found
On the Learn-to-Optimize Capabilities of Transformers in In-Context Sparse Recovery
Renpu Liu, Ruida Zhou, Cong Shen et al.
Rethinking Invariance in In-context Learning
Lizhe Fang, Yifei Wang, Khashayar Gatmiry et al.
Transformers Handle Endogeneity in In-Context Linear Regression
Haodong Liang, Krishna Balasubramanian, Lifeng Lai
REGENT: A Retrieval-Augmented Generalist Agent That Can Act In-Context in New Environments
Kaustubh Sridhar, Souradeep Dutta, Dinesh Jayaraman et al.
Transformers as Algorithms: Generalization and Stability in In-context Learning
Yingcong Li, Muhammed Emrullah Ildiz, Dimitris Papailiopoulos et al.
Understanding and Controlling Repetition Neurons and Induction Heads in In-Context Learning
Nhi Hoai Doan, Tatsuya Hiraoka, Kentaro Inui
Rectifying Demonstration Shortcut in In-Context Learning
Joonwon Jang, Sanghwan Jang, Wonbin Kweon et al.
Token-based Decision Criteria Are Suboptimal in In-context Learning
Hakaze Cho, Yoshihiro Sakai, Mariko Kato et al.
Induction Heads as an Essential Mechanism for Pattern Matching in In-context Learning
Joy Crosbie, Ekaterina Shutova
Mitigating Copy Bias in In-Context Learning through Neuron Pruning
Ameen Ali Ali, Lior Wolf, Ivan Titov
Data Distributional Properties Drive Emergent In-Context Learning in Transformers
Stephanie Chan, Adam Santoro, Andrew Lampinen et al.
The Transient Nature of Emergent In-Context Learning in Transformers
Aaditya Singh, Stephanie Chan, Ted Moskovitz et al.
Linking In-context Learning in Transformers to Human Episodic Memory
Li Ji-An, Corey Y Zhou, Marcus K. Benna et al.
Probing the Decision Boundaries of In-context Learning in Large Language Models
Siyan Zhao, Tung Nguyen, Aditya Grover
Iterative Forward Tuning Boosts In-Context Learning in Language Models
Jiaxi Yang, Binyuan Hui, Min Yang et al.
An Empirical Study of In-context Learning in LLMs for Machine Translation
Pranjal Chitale, Jay Gala, Raj Dabre
Prompt, Translate, Fine-Tune, Re-Initialize, or Instruction-Tune? Adapting LLMs for In-Context Learning in Low-Resource Languages
Christopher Toukmaji, Jeffrey Flanigan
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.
Strategic Demonstration Selection for Improved Fairness in LLM In-Context Learning
Jingyu Hu, Weiru Liu, Mengnan Du
Interpretability Analysis of Arithmetic In-Context Learning in Large Language Models
Gregory Polyakov, Christian Hepting, Carsten Eickhoff et al.