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In-Context Learning

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Showing 1826–1850 of 2297 papers

TitleStatusHype
MMRAG: Multi-Mode Retrieval-Augmented Generation with Large Language Models for Biomedical In-Context Learning—0
MoConVQ: Unified Physics-Based Motion Control via Scalable Discrete Representations—0
MOKA: Open-World Robotic Manipulation through Mark-Based Visual Prompting—0
Monocle: Hybrid Local-Global In-Context Evaluation for Long-Text Generation with Uncertainty-Based Active Learning—0
Monte Carlo Sampling for Analyzing In-Context Examples—0
More is not always better? Enhancing Many-Shot In-Context Learning with Differentiated and Reweighting Objectives—0
More Samples or More Prompts? Exploring Effective In-Context Sampling for LLM Few-Shot Prompt Engineering—0
mOSCAR: A Large-scale Multilingual and Multimodal Document-level Corpus—0
Multi-Layer Ranking with Large Language Models for News Source Recommendation—0
Multilingual Few-Shot Learning via Language Model Retrieval—0
Multilingual Fine-Grained News Headline Hallucination Detection—0
Multimodal Contrastive In-Context Learning—0
MULTI: Multimodal Understanding Leaderboard with Text and Images—0
Multi-Step Dialogue Workflow Action Prediction—0
MuVi: Video-to-Music Generation with Semantic Alignment and Rhythmic Synchronization—0
N2C2: Nearest Neighbor Enhanced Confidence Calibration for Cross-Lingual In-Context Learning—0
Narrowing the Gap between Zero- and Few-shot Machine Translation by Matching Styles—0
Navigating the Dual Facets: A Comprehensive Evaluation of Sequential Memory Editing in Large Language Models—0
Navigating the Labyrinth: Evaluating and Enhancing LLMs' Ability to Reason About Search Problems—0
Neither Stochastic Parroting nor AGI: LLMs Solve Tasks through Context-Directed Extrapolation from Training Data Priors—0
Neural Machine Translation Models Can Learn to be Few-shot Learners—0
Neural Network Reprogrammability: A Unified Theme on Model Reprogramming, Prompt Tuning, and Prompt Instruction—0
Neuromorphic In-Context Learning for Energy-Efficient MIMO Symbol Detection—0
Leveraging Large Language Models to Develop Heuristics for Emerging Optimization ProblemsCode0
Leveraging Large Language Models for Scalable Vector Graphics-Driven Image UnderstandingCode0
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