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In-Context Learning

Papers

Showing 13761400 of 2297 papers

TitleStatusHype
Few-Shot Cross-Lingual Transfer for Prompting Large Language Models in Low-Resource Languages0
Concept-aware Data Construction Improves In-context Learning of Language Models0
DP-TabICL: In-Context Learning with Differentially Private Tabular Data0
A Dataset and Benchmark for Hospital Course Summarization with Adapted Large Language ModelsCode1
InstructGIE: Towards Generalizable Image Editing0
LLMs in the Imaginarium: Tool Learning through Simulated Trial and ErrorCode2
On the Essence and Prospect: An Investigation of Alignment Approaches for Big Models0
TEGEE: Task dEfinition Guided Expert Ensembling for Generalizable and Few-shot Learning0
Where does In-context Translation Happen in Large Language Models0
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 Grading0
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 Adjustment0
Benchmarking the Text-to-SQL Capability of Large Language Models: A Comprehensive Evaluation0
How Well Can Transformers Emulate In-context Newton's Method?0
MOKA: Open-World Robotic Manipulation through Mark-Based Visual Prompting0
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 Inference0
Transformers for Supervised Online Continual Learning0
Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models0
Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized RehearsalCode1
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