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In-Context Learning

419 directly classified papers

Papers per year

Papers

In-Context Learning State Vector with Inner and Momentum Optimization NIPS 2024
Learning to grok: Emergence of in-context learning and skill composition in modular arithmetic tasks NIPS 2024
From Unstructured Data to In-Context Learning: Exploring What Tasks Can Be Learned and When NIPS 2024
In-Context Learning of a Linear Transformer Block: Benefits of the MLP Component and One-Step GD Initialization NIPS 2024
Multimodal Task Vectors Enable Many-Shot Multimodal In-Context Learning NIPS 2024
A Simple Image Segmentation Framework via In-Context Examples NIPS 2024
Estimating the Hallucination Rate of Generative AI NIPS 2024
EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language Models NIPS 2024
Fast Best-of-N Decoding via Speculative Rejection NIPS 2024
Where does In-context Learning Happen in Large Language Models? NIPS 2024
Enhancing In-Context Learning Performance with just SVD-Based Weight Pruning: A Theoretical Perspective NIPS 2024
Leveraging Environment Interaction for Automated PDDL Translation and Planning with Large Language Models NIPS 2024
Linear Transformers are Versatile In-Context Learners NIPS 2024
On Mesa-Optimization in Autoregressively Trained Transformers: Emergence and Capability NIPS 2024
Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimization NIPS 2024
Opponent Modeling with In-context Search NIPS 2024
Make Your LLM Fully Utilize the Context NIPS 2024
Can LLMs Learn by Teaching for Better Reasoning? A Preliminary Study NIPS 2024
Universal In-Context Approximation By Prompting Fully Recurrent Models NIPS 2024
Towards Global Optimal Visual In-Context Learning Prompt Selection NIPS 2024
VLM Agents Generate Their Own Memories: Distilling Experience into Embodied Programs of Thought NIPS 2024
Many-Shot In-Context Learning NIPS 2024
Pretrained Transformer Efficiently Learns Low-Dimensional Target Functions In-Context NIPS 2024
Mixture of In-Context Experts Enhance LLMs' Long Context Awareness NIPS 2024
Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models NIPS 2024