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In-Context Learning

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Showing 1451–1500 of 2297 papers

TitleStatusHype
Self-AMPLIFY: Improving Small Language Models with Self Post Hoc Explanations—0
In-Context Learning Demonstration Selection via Influence AnalysisCode1
Task-Oriented Dialogue with In-Context LearningCode1
Modularized Networks for Few-shot Hateful Meme DetectionCode0
Enabling Weak LLMs to Judge Response Reliability via Meta Ranking—0
Do Large Language Models Understand Logic or Just Mimick Context?—0
In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness—0
In-Context Example Ordering Guided by Label Distributions—0
Visual In-Context Learning for Large Vision-Language Models—0
GNNavi: Navigating the Information Flow in Large Language Models by Graph Neural NetworkCode0
TuneTables: Context Optimization for Scalable Prior-Data Fitted NetworksCode1
C-ICL: Contrastive In-context Learning for Information Extraction—0
PhaseEvo: Towards Unified In-Context Prompt Optimization for Large Language Models—0
Navigating the Dual Facets: A Comprehensive Evaluation of Sequential Memory Editing in Large Language Models—0
The Evolution of Statistical Induction Heads: In-Context Learning Markov Chains—0
RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language ModelCode2
LinkNER: Linking Local Named Entity Recognition Models to Large Language Models using UncertaintyCode0
Linear Transformers with Learnable Kernel Functions are Better In-Context ModelsCode2
Let's Learn Step by Step: Enhancing In-Context Learning Ability with Curriculum LearningCode1
Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQLCode0
Measuring and Reducing LLM Hallucination without Gold-Standard Answers—0
Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow ParadigmCode1
Understanding In-Context Learning with a Pelican Soup Framework—0
Prompt-Based Bias Calibration for Better Zero/Few-Shot Learning of Language Models—0
Bridging Associative Memory and Probabilistic Modeling—0
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 Generation—0
Chain-of-Planned-Behaviour Workflow Elicits Few-Shot Mobility Generation in LLMs—0
Uncertainty Quantification for In-Context Learning of Large Language ModelsCode1
Large Language Model-Based Interpretable Machine Learning Control in Building Energy Systems—0
GrounDial: Human-norm Grounded Safe Dialog Response Generation—0
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 Learning—0
Universal Link Predictor By In-Context Learning on Graphs—0
Step-On-Feet Tuning: Scaling Self-Alignment of LLMs via Bootstrapping—0
Assessing Generalization for Subpopulation Representative Modeling via In-Context Learning—0
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 TabPFN—0
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 Models—0
In-Context Principle Learning from MistakesCode0
NoisyICL: A Little Noise in Model Parameters Calibrates In-context LearningCode0
In-Context Learning Can Re-learn Forbidden Tasks—0
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