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In-Context Learning

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Showing 1551–1600 of 2297 papers

TitleStatusHype
TCRA-LLM: Token Compression Retrieval Augmented Large Language Model for Inference Cost Reduction—0
Teaching Algorithmic Reasoning via In-context Learning—0
Technical Debt in In-Context Learning: Diminishing Efficiency in Long Context—0
Tests as Prompt: A Test-Driven-Development Benchmark for LLM Code Generation—0
Test-Time Adaptation for Generalizable Task Progress Estimation—0
Test-Time Training Provably Improves Transformers as In-context Learners—0
Textbooks Are All You Need II: phi-1.5 technical report—0
Text-centric Alignment for Multi-Modality Learning—0
Synthetic Data Generation in Low-Resource Settings via Fine-Tuning of Large Language Models—0
New News: System-2 Fine-tuning for Robust Integration of New Knowledge—0
TextMatch: Enhancing Image-Text Consistency Through Multimodal Optimization—0
Se^2: Sequential Example Selection for In-Context Learning—0
ThatiAR: Subjectivity Detection in Arabic News Sentences—0
The broader spectrum of in-context learning—0
The Closeness of In-Context Learning and Weight Shifting for Softmax Regression—0
The Cost of Down-Scaling Language Models: Fact Recall Deteriorates before In-Context Learning—0
The Developmental Landscape of In-Context Learning—0
The Dual-Route Model of Induction—0
The Earth is Flat? Unveiling Factual Errors in Large Language Models—0
The Evolution of Statistical Induction Heads: In-Context Learning Markov Chains—0
The Gaps between Pre-train and Downstream Settings in Bias Evaluation and Debiasing—0
The Geometry of Prompting: Unveiling Distinct Mechanisms of Task Adaptation in Language Models—0
The ICL Consistency Test—0
The in-context inductive biases of vision-language models differ across modalities—0
The Inductive Bias of In-Context Learning: Rethinking Pretraining Example Design—0
The Learnability of In-Context Learning—0
The Limits of ChatGPT in Extracting Aspect-Category-Opinion-Sentiment Quadruples: A Comparative Analysis—0
Beyond the Black Box: A Statistical Model for LLM Reasoning and Inference—0
The mechanistic basis of data dependence and abrupt learning in an in-context classification task—0
The Pitfalls of Growing Group Complexity: LLMs and Social Choice-Based Aggregation for Group Recommendations—0
The Point of View of a Sentiment: Towards Clinician Bias Detection in Psychiatric Notes—0
The Power of Adaptation: Boosting In-Context Learning through Adaptive Prompting—0
The Power of Large Language Models for Wireless Communication System Development: A Case Study on FPGA Platforms—0
The Prompt is Mightier than the Example—0
The Role of Diversity in In-Context Learning for Large Language Models—0
The Scope of In-Context Learning for the Extraction of Medical Temporal Constraints—0
The Strong Pull of Prior Knowledge in Large Language Models and Its Impact on Emotion Recognition—0
Think from Words(TFW): Initiating Human-Like Cognition in Large Language Models Through Think from Words for Japanese Text-level Classification—0
ThinkSum: Probabilistic reasoning over sets using large language models—0
Think Twice Before Recognizing: Large Multimodal Models for General Fine-grained Traffic Sign Recognition—0
TokenRec: Learning to Tokenize ID for LLM-based Generative Recommendation—0
ToolNet: Connecting Large Language Models with Massive Tools via Tool Graph—0
A Complete Survey on Contemporary Methods, Emerging Paradigms and Hybrid Approaches for Few-Shot Learning—0
Towards ASR Robust Spoken Language Understanding Through In-Context Learning With Word Confusion Networks—0
Towards Automated Functional Equation Proving: A Benchmark Dataset and A Domain-Specific In-Context Agent—0
Towards Autonomous Agents: Adaptive-planning, Reasoning, and Acting in Language Models—0
Towards Auto-Regressive Next-Token Prediction: In-Context Learning Emerges from Generalization—0
Towards Better Understanding of In-Context Learning Ability from In-Context Uncertainty Quantification—0
Towards Effective Disambiguation for Machine Translation with Large Language Models—0
Towards Few-Shot Identification of Morality Frames using In-Context Learning—0
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