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In-Context Learning

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Showing 651–700 of 2297 papers

TitleStatusHype
Unsupervised Prompting for Graph Neural Networks—0
In-Context Watermarks for Large Language Models—0
Any Large Language Model Can Be a Reliable Judge: Debiasing with a Reasoning-based Bias Detector—0
Meta-Learning an In-Context Transformer Model of Human Higher Visual Cortex—0
Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories—0
Large Language Models as Computable Approximations to Solomonoff Induction—0
Bridging Sign and Spoken Languages: Pseudo Gloss Generation for Sign Language Translation—0
Adversarially Pretrained Transformers may be Universally Robust In-Context LearnersCode0
Out-of-Distribution Generalization of In-Context Learning: A Low-Dimensional Subspace Perspective—0
Mechanistic Fine-tuning for In-context Learning—0
Reinforcing Question Answering Agents with Minimalist Policy Gradient Optimization—0
In-Context Learning Boosts Speech Recognition via Human-like Adaptation to Speakers and Language Varieties—0
EmoGist: Efficient In-Context Learning for Visual Emotion Understanding—0
QA-prompting: Improving Summarization with Large Language Models using Question-AnsweringCode0
DSMentor: Enhancing Data Science Agents with Curriculum Learning and Online Knowledge Accumulation—0
AutoMathKG: The automated mathematical knowledge graph based on LLM and vector database—0
CIE: Controlling Language Model Text Generations Using Continuous SignalsCode0
Causal Head Gating: A Framework for Interpreting Roles of Attention Heads in Transformers—0
FinePhys: Fine-grained Human Action Generation by Explicitly Incorporating Physical Laws for Effective Skeletal Guidance—0
Improving LLM Outputs Against Jailbreak Attacks with Expert Model Integration—0
Bridging Generative and Discriminative Learning: Few-Shot Relation Extraction via Two-Stage Knowledge-Guided Pre-trainingCode0
Induction Head Toxicity Mechanistically Explains Repetition Curse in Large Language Models—0
Transformers as Unsupervised Learning Algorithms: A study on Gaussian MixturesCode0
Do different prompting methods yield a common task representation in language models?—0
Transformer learns the cross-task prior and regularization for in-context learning—0
When Thinking Fails: The Pitfalls of Reasoning for Instruction-Following in LLMs—0
Feasibility with Language Models for Open-World Compositional Zero-Shot Learning—0
Context parroting: A simple but tough-to-beat baseline for foundation models in scientific machine learning—0
Illusion or Algorithm? Investigating Memorization, Emergence, and Symbolic Processing in In-Context LearningCode0
Predictability Shapes Adaptation: An Evolutionary Perspective on Modes of Learning in Transformers—0
Towards Fair In-Context Learning with Tabular Foundation ModelsCode0
A Survey on Large Language Models in Multimodal Recommender Systems—0
Scaling Context, Not Parameters: Training a Compact 7B Language Model for Efficient Long-Context Processing—0
Automated Meta Prompt Engineering for Alignment with the Theory of Mind—0
NurValues: Real-World Nursing Values Evaluation for Large Language Models in Clinical Context—0
Tests as Prompt: A Test-Driven-Development Benchmark for LLM Code Generation—0
Incomplete In-context Learning—0
Spoken Language Understanding on Unseen Tasks With In-Context Learning—0
Probing In-Context Learning: Impact of Task Complexity and Model Architecture on Generalization and EfficiencyCode0
Turbo-ICL: In-Context Learning-Based Turbo Equalization—0
Harnessing LLMs Explanations to Boost Surrogate Models in Tabular Data Classification—0
Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring—0
Rethinking Invariance in In-context LearningCode0
Leveraging Large Language Models for enzymatic reaction prediction and characterizationCode0
RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection—0
Understanding In-context Learning of Addition via Activation Subspaces—0
The Pitfalls of Growing Group Complexity: LLMs and Social Choice-Based Aggregation for Group Recommendations—0
EAM: Enhancing Anything with Diffusion Transformers for Blind Super-Resolution—0
In-Context Learning for Label-Efficient Cancer Image Classification in Oncology—0
CDE-Mapper: Using Retrieval-Augmented Language Models for Linking Clinical Data Elements to Controlled Vocabularies—0
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