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In-Context Learning

Papers

Showing 351375 of 2297 papers

TitleStatusHype
Dolphins: Multimodal Language Model for DrivingCode1
Data Whisperer: Efficient Data Selection for Task-Specific LLM Fine-Tuning via Few-Shot In-Context LearningCode1
Ensemble-Instruct: Generating Instruction-Tuning Data with a Heterogeneous Mixture of LMsCode1
Evolving Prompts In-Context: An Open-ended, Self-replicating PerspectiveCode1
All in an Aggregated Image for In-Image LearningCode1
Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context LearningCode1
eP-ALM: Efficient Perceptual Augmentation of Language ModelsCode1
Are Large Language Models Temporally Grounded?Code1
Ada-Instruct: Adapting Instruction Generators for Complex ReasoningCode1
Evaluating Knowledge Generation and Self-Refinement Strategies for LLM-based Column Type AnnotationCode1
Evaluating Named Entity Recognition Using Few-Shot Prompting with Large Language ModelsCode1
DIALIGHT: Lightweight Multilingual Development and Evaluation of Task-Oriented Dialogue Systems with Large Language ModelsCode1
In-Context Ensemble Learning from Pseudo Labels Improves Video-Language Models for Low-Level Workflow UnderstandingCode1
Code Prompting Elicits Conditional Reasoning Abilities in Text+Code LLMsCode1
LIVE: Learnable In-Context Vector for Visual Question AnsweringCode1
Code-Style In-Context Learning for Knowledge-Based Question AnsweringCode1
From system models to class models: An in-context learning paradigmCode1
Is C4 Dataset Optimal for Pruning? An Investigation of Calibration Data for LLM PruningCode1
IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language ModelsCode1
Cognitive Overload Attack:Prompt Injection for Long ContextCode1
ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot LearningCode1
AMAGO: Scalable In-Context Reinforcement Learning for Adaptive AgentsCode1
Exploring Effective Factors for Improving Visual In-Context LearningCode1
Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language ModelsCode1
IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language ModelsCode1
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