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In-Context Learning

Papers

Showing 14761500 of 2297 papers

TitleStatusHype
Self-Augmented In-Context Learning for Unsupervised Word TranslationCode0
QuRating: Selecting High-Quality Data for Training Language ModelsCode3
Crafting a Good Prompt or Providing Exemplary Dialogues? A Study of In-Context Learning for Persona-based Dialogue Generation0
Chain-of-Planned-Behaviour Workflow Elicits Few-Shot Mobility Generation in LLMs0
Uncertainty Quantification for In-Context Learning of Large Language ModelsCode1
Large Language Model-Based Interpretable Machine Learning Control in Building Energy Systems0
GrounDial: Human-norm Grounded Safe Dialog Response Generation0
ICDPO: Effectively Borrowing Alignment Capability of Others via In-context Direct Preference OptimizationCode1
HGOT: Hierarchical Graph of Thoughts for Retrieval-Augmented In-Context Learning in Factuality EvaluationCode0
The dynamic interplay between in-context and in-weight learning in humans and neural networksCode0
Can LLMs Learn New Concepts Incrementally without Forgetting?Code2
Text-centric Alignment for Multi-Modality Learning0
Universal Link Predictor By In-Context Learning on Graphs0
Step-On-Feet Tuning: Scaling Self-Alignment of LLMs via Bootstrapping0
Assessing Generalization for Subpopulation Representative Modeling via In-Context Learning0
VisLingInstruct: Elevating Zero-Shot Learning in Multi-Modal Language Models with Autonomous Instruction OptimizationCode0
Chain-of-Layer: Iteratively Prompting Large Language Models for Taxonomy Induction from Limited ExamplesCode1
In-Context Data Distillation with TabPFN0
NICE: To Optimize In-Context Examples or Not?Code0
Entropy-Regularized Token-Level Policy Optimization for Language Agent ReinforcementCode1
InternLM-Math: Open Math Large Language Models Toward Verifiable ReasoningCode4
On the Out-Of-Distribution Generalization of Multimodal Large Language Models0
In-Context Principle Learning from MistakesCode0
NoisyICL: A Little Noise in Model Parameters Calibrates In-context LearningCode0
In-Context Learning Can Re-learn Forbidden Tasks0
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