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In-Context Learning

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TitleStatusHype
Zero-shot Generative Linguistic SteganographyCode0
Interpretable Machine Learning for TabPFNCode1
Few-Shot Image Classification and Segmentation as Visual Question Answering Using Vision-Language Models—0
Team Trifecta at Factify5WQA: Setting the Standard in Fact Verification with Fine-TuningCode0
The Whole is Better than the Sum: Using Aggregated Demonstrations in In-Context Learning for Sequential RecommendationCode0
WavCraft: Audio Editing and Generation with Large Language ModelsCode4
Borrowing Treasures from Neighbors: In-Context Learning for Multimodal Learning with Missing Modalities and Data ScarcityCode2
Rectifying Demonstration Shortcut in In-Context LearningCode0
MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training—0
Unveiling the Generalization Power of Fine-Tuned Large Language ModelsCode1
Revealing the Parallel Multilingual Learning within Large Language ModelsCode1
Distilling Named Entity Recognition Models for Endangered Species from Large Language Models—0
Fundamental Components of Deep Learning: A category-theoretic approachCode5
PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistencyCode2
AutoGuide: Automated Generation and Selection of Context-Aware Guidelines for Large Language Model Agents—0
Rich Semantic Knowledge Enhanced Large Language Models for Few-shot Chinese Spell Checking—0
BAGEL: Bootstrapping Agents by Guiding Exploration with Language—0
Knowledge Graph Large Language Model (KG-LLM) for Link Prediction—0
In-context learning enables multimodal large language models to classify cancer pathology images—0
One size doesn't fit all: Predicting the Number of Examples for In-Context Learning—0
SELMA: Learning and Merging Skill-Specific Text-to-Image Experts with Auto-Generated Data—0
MEND: Meta dEmonstratioN Distillation for Efficient and Effective In-Context LearningCode0
In-context Exploration-Exploitation for Reinforcement Learning—0
In-context Prompt Learning for Test-time Vision Recognition with Frozen Vision-language Model—0
FewFedPIT: Towards Privacy-preserving and Few-shot Federated Instruction Tuning—0
Few-Shot Cross-Lingual Transfer for Prompting Large Language Models in Low-Resource Languages—0
Concept-aware Data Construction Improves In-context Learning of Language Models—0
DP-TabICL: In-Context Learning with Differentially Private Tabular Data—0
A Dataset and Benchmark for Hospital Course Summarization with Adapted Large Language ModelsCode1
InstructGIE: Towards Generalizable Image Editing—0
LLMs in the Imaginarium: Tool Learning through Simulated Trial and ErrorCode2
On the Essence and Prospect: An Investigation of Alignment Approaches for Big Models—0
TEGEE: Task dEfinition Guided Expert Ensembling for Generalizable and Few-shot Learning—0
Where does In-context Translation Happen in Large Language Models—0
Large Language Models are In-Context Molecule LearnersCode2
How Far Are We from Intelligent Visual Deductive Reasoning?Code1
Benchmarking Hallucination in Large Language Models based on Unanswerable Math Word ProblemCode0
German also Hallucinates! Inconsistency Detection in News Summaries with the Absinth DatasetCode0
Japanese-English Sentence Translation Exercises Dataset for Automatic Grading—0
JMI at SemEval 2024 Task 3: Two-step approach for multimodal ECAC using in-context learning with GPT and instruction-tuned Llama modelsCode0
Causal Prompting: Debiasing Large Language Model Prompting based on Front-Door Adjustment—0
Benchmarking the Text-to-SQL Capability of Large Language Models: A Comprehensive Evaluation—0
How Well Can Transformers Emulate In-context Newton's Method?—0
MOKA: Open-World Robotic Manipulation through Mark-Based Visual Prompting—0
ProTrix: Building Models for Planning and Reasoning over Tables with Sentence ContextCode1
Analyzing and Adapting Large Language Models for Few-Shot Multilingual NLU: Are We There Yet?—0
Not All Layers of LLMs Are Necessary During Inference—0
Transformers for Supervised Online Continual Learning—0
Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models—0
Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized RehearsalCode1
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