Papers
680 papers found
One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear Self-Attention
Arvind V. Mahankali, Tatsunori Hashimoto, Tengyu Ma
Is attention required for ICL? Exploring the Relationship Between Model Architecture and In-Context Learning Ability
Ivan Lee, Nan Jiang, Taylor Berg-Kirkpatrick
Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering
Han Zhou, Xingchen Wan, Lev Proleev et al.
MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning
Haozhe Zhao, Zefan Cai, Shuzheng Si et al.
How Do Transformers Learn In-Context Beyond Simple Functions? A Case Study on Learning with Representations
Tianyu Guo, Wei Hu, Song Mei et al.
Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining
Licong Lin, Yu Bai, Song Mei
DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning
Jing Xiong, Zixuan Li, Chuanyang Zheng et al.
Privacy-Preserving In-Context Learning for Large Language Models
Tong Wu, Ashwinee Panda, Jiachen T. Wang et al.
The Cost of Scaling Down Large Language Models: Reducing Model Size Affects Memory before In-context Learning
Tian Jin, Nolan Clement, Xin Dong et al.
Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
Xinyu Tang, Richard Shin, Huseyin A Inan et al.
MEND: Meta Demonstration Distillation for Efficient and Effective In-Context Learning
Yichuan Li, Xiyao Ma, Sixing Lu et al.
In-Context Learning Learns Label Relationships but Is Not Conventional Learning
Jannik Kossen, Yarin Gal, Tom Rainforth
The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning
Bill Yuchen Lin, Abhilasha Ravichander, Ximing Lu et al.
Beyond task performance: evaluating and reducing the flaws of large multimodal models with in-context-learning
Mustafa Shukor, Alexandre Rame, Corentin Dancette et al.
In-context Exploration-Exploitation for Reinforcement Learning
Zhenwen Dai, Federico Tomasi, Sina Ghiassian
Understanding In-Context Learning from Repetitions
Jianhao Yan, Jin Xu, Chiyu Song et al.
Are Human-generated Demonstrations Necessary for In-context Learning?
Rui Li, Guoyin Wang, Jiwei Li
CausalLM is not optimal for in-context learning
Nan Ding, Tomer Levinboim, Jialin Wu et al.
Towards Offline Opponent Modeling with In-context Learning
Yuheng Jing, Kai Li, Bingyun Liu et al.
Rapid Selection and Ordering of In-Context Demonstrations via Prompt Embedding Clustering
Kha Pham, Hung Le, Man Ngo et al.
Why In-Context Learning Models are Good Few-Shot Learners?
Shiguang Wu, Yaqing Wang, Quanming Yao
ICLR: In-Context Learning of Representations
Core Francisco Park, Andrew Lee, Ekdeep Singh Lubana et al.
Competition Dynamics Shape Algorithmic Phases of In-Context Learning
Core Francisco Park, Ekdeep Singh Lubana, Hidenori Tanaka
LICO: Large Language Models for In-Context Molecular Optimization
Tung Nguyen, Aditya Grover
In-context Time Series Predictor
Jiecheng Lu, Yan Sun, Shihao Yang