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In-Context Learning

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TitleStatusHype
Evaluating Named Entity Recognition Using Few-Shot Prompting with Large Language ModelsCode1
Epidemic Information Extraction for Event-Based Surveillance using Large Language Models—0
Foundation Models for Music: A SurveyCode3
Focused Large Language Models are Stable Many-Shot Learners—0
MEDSAGE: Enhancing Robustness of Medical Dialogue Summarization to ASR Errors with LLM-generated Synthetic Dialogues—0
Probing Causality Manipulation of Large Language Models—0
Preliminary Investigations of a Multi-Faceted Robust and Synergistic Approach in Semiconductor Electron Micrograph Analysis: Integrating Vision Transformers with Large Language and Multimodal Models—0
In-Context Learning with Reinforcement Learning for Incomplete Utterance Rewriting—0
Multimodal Contrastive In-Context Learning—0
Causal-Guided Active Learning for Debiasing Large Language ModelsCode1
Investigating LLM Applications in E-Commerce—0
Transformers are Minimax Optimal Nonparametric In-Context Learners—0
uMedSum: A Unified Framework for Advancing Medical Abstractive Summarization—0
Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMsCode0
Interactive DualChecker for Mitigating Hallucinations in Distilling Large Language Models—0
Clinical Insights: A Comprehensive Review of Language Models in Medicine—0
Memorization in In-Context Learning—0
Benchmarking Large Language Models for Math Reasoning TasksCode0
QUITO-X: A New Perspective on Context Compression from the Information Bottleneck Theory—0
V-RoAst: Visual Road Assessment. Can VLM be a Road Safety Assessor Using the iRAP Standard?Code1
Revisiting VerilogEval: A Year of Improvements in Large-Language Models for Hardware Code GenerationCode3
Self-Refined Generative Foundation Models for Wireless Traffic Prediction—0
Recording for Eyes, Not Echoing to Ears: Contextualized Spoken-to-Written Conversion of ASR Transcripts—0
Strategic Demonstration Selection for Improved Fairness in LLM In-Context Learning—0
In-Context Learning with Representations: Contextual Generalization of Trained Transformers—0
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