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In-Context Learning

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TitleStatusHype
Transformers Implement Functional Gradient Descent to Learn Non-Linear Functions In Context—0
Transformers Learn Nonlinear Features In Context: Nonconvex Mean-field Dynamics on the Attention Landscape—0
Transformers Learn Temporal Difference Methods for In-Context Reinforcement Learning—0
Transformers learn variable-order Markov chains in-context—0
TripletCLIP: Improving Compositional Reasoning of CLIP via Synthetic Vision-Language Negatives—0
Trojan Detection Through Pattern Recognition for Large Language Models—0
Tuning-Free Personalized Alignment via Trial-Error-Explain In-Context Learning—0
Turbo-ICL: In-Context Learning-Based Turbo Equalization—0
Türkçe Dil Modellerinin Performans Karşılaştırması Performance Comparison of Turkish Language Models—0
UI Layout Generation with LLMs Guided by UI Grammar—0
uMedSum: A Unified Framework for Advancing Medical Abstractive Summarization—0
Uncovering Hidden Intentions: Exploring Prompt Recovery for Deeper Insights into Generated Texts—0
Uncovering mesa-optimization algorithms in Transformers—0
Uncovering Overfitting in Large Language Model Editing—0
Unlocking the Power of LLM Uncertainty for Active In-Context Example Selection—0
Can Multimodal Large Language Models Truly Perform Multimodal In-Context Learning?—0
Understanding and Predicting Human Label Variation in Natural Language Inference through Explanation—0
On the Calibration of Multilingual Question Answering LLMs—0
Understanding In-Context Learning in Transformers and LLMs by Learning to Learn Discrete Functions—0
Understanding In-context Learning of Addition via Activation Subspaces—0
Understanding In-Context Learning via Supportive Pretraining Data—0
Understanding In-Context Learning with a Pelican Soup Framework—0
Understanding Knowledge Hijack Mechanism in In-context Learning through Associative Memory—0
Understanding Memorisation in LLMs: Dynamics, Influencing Factors, and Implications—0
Understanding Task Vectors in In-Context Learning: Emergence, Functionality, and Limitations—0
Understanding the Logical Capabilities of Large Language Models via Out-of-Context Representation Learning—0
Ungrammatical-syntax-based In-context Example Selection for Grammatical Error Correction—0
Unified Medical Lesion Segmentation via Self-referring Indicator—0
Unifying Attention Heads and Task Vectors via Hidden State Geometry in In-Context Learning—0
Unifying Demonstration Selection and Compression for In-Context Learning—0
Unifying Image Processing as Visual Prompting Question Answering—0
Dynamic Universal Approximation Theory: The Basic Theory for Transformer-based Large Language Models—0
Universal Link Predictor By In-Context Learning on Graphs—0
Universal Response and Emergence of Induction in LLMs—0
Universal Self-Adaptive Prompting—0
Universal Vulnerabilities in Large Language Models: Backdoor Attacks for In-context Learning—0
Unlearnable Algorithms for In-context Learning—0
Unleashing In-context Learning of Autoregressive Models for Few-shot Image Manipulation—0
Unleashing the Potential of Large Language Models for Predictive Tabular Tasks in Data Science—0
Unleashing the Power of Large Language Models in Zero-shot Relation Extraction via Self-Prompting—0
Unlocking Instructive In-Context Learning with Tabular Prompting for Relational Triple Extraction—0
Unraveling Indirect In-Context Learning Using Influence Functions—0
Unraveling the Mechanics of Learning-Based Demonstration Selection for In-Context Learning—0
Unsupervised Human Preference Learning—0
Unsupervised Meta-Learning via In-Context Learning—0
Unsupervised Prompting for Graph Neural Networks—0
Unsupervised Text Style Transfer via LLMs and Attention Masking with Multi-way Interactions—0
UnUnlearning: Unlearning is not sufficient for content regulation in advanced generative AI—0
Unveiling Induction Heads: Provable Training Dynamics and Feature Learning in Transformers—0
Unveiling the Misuse Potential of Base Large Language Models via In-Context Learning—0
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