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In-Context Learning

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Showing 1501–1525 of 2297 papers

TitleStatusHype
Is In-Context Learning in Large Language Models Bayesian? A Martingale PerspectiveCode0
Controlling Large Language Model Agents with Entropic Activation Steering—0
The Point of View of a Sentiment: Towards Clinician Bias Detection in Psychiatric Notes—0
From Unstructured Data to In-Context Learning: Exploring What Tasks Can Be Learned and When—0
UniBias: Unveiling and Mitigating LLM Bias through Internal Attention and FFN ManipulationCode0
Mamba State-Space Models Are Lyapunov-Stable Learners—0
LLM-RankFusion: Mitigating Intrinsic Inconsistency in LLM-based RankingCode0
Would I Lie To You? Inference Time Alignment of Language Models using Direct Preference HeadsCode0
Why Larger Language Models Do In-context Learning Differently?—0
From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems—0
Does learning the right latent variables necessarily improve in-context learning?Code0
Mashee at SemEval-2024 Task 8: The Impact of Samples Quality on the Performance of In-Context Learning for Machine Text Classification—0
A Theoretical Understanding of Self-Correction through In-context Alignment—0
Benchmarks Underestimate the Readiness of Multi-lingual Dialogue Agents—0
IM-Context: In-Context Learning for Imbalanced Regression TasksCode0
Dual Process Learning: Controlling Use of In-Context vs. In-Weights Strategies with Weight Forgetting—0
Multi-modal Generation via Cross-Modal In-Context LearningCode0
On Understanding Attention-Based In-Context Learning for Categorical Data—0
SelfCP: Compressing Over-Limit Prompt via the Frozen Large Language Model Itself—0
Benchmarking General-Purpose In-Context Learning—0
Unifying Demonstration Selection and Compression for In-Context Learning—0
RAGSys: Item-Cold-Start Recommender as RAG System—0
On Mesa-Optimization in Autoregressively Trained Transformers: Emergence and CapabilityCode0
Aligning LLMs through Multi-perspective User Preference Ranking-based Feedback for Programming Question Answering—0
On the Noise Robustness of In-Context Learning for Text GenerationCode0
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