in-context learning

Thoughts to Target: Enhance Planning for Target-driven Conversation EMNLP 2024 Timeline-based Sentence Decomposition with In Context Learning for Temporal Fact Extraction ACL 2024 TISE: A Tripartite In-context Selection Method for Event Argument Extraction NAACL 2024 Toolken+: Improving LLM Tool Usage with Reranking and a Reject Option EMNLP 2024 Topic-guided Example Selection for Domain Adaptation in LLM-based Machine Translation EACL 2024 Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts CVPR 2024 Toward In-Context Teaching: Adapting Examples to Students’ Misconceptions ACL 2024 Towards Comprehensive Language Analysis for Clinically Enriched Spontaneous Dialogue COLING 2024 Towards Fine-Grained Pedagogical Control over English Grammar Complexity in Educational Text Generation NAACL 2024 Towards Global Optimal Visual In-Context Learning Prompt Selection NeurIPS 2024 Towards More Unified In-context Visual Understanding CVPR 2024 Towards Offline Opponent Modeling with In-context Learning ICLR 2024 Towards Robust In-Context Learning for Machine Translation with Large Language Models COLING 2024 Towards Understanding How Transformers Learn In-context Through a Representation Learning Lens NeurIPS 2024 Towards Understanding the Relationship between In-context Learning and Compositional Generalization COLING 2024 TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Industry Systems EMNLP 2024 Trainable Transformer in Transformer ICML 2024 Trained Transformers Learn Linear Models In-Context JMLR 2024 Training Dynamics of Multi-Head Softmax Attention for In-Context Learning: Emergence, Convergence, and Optimality (extended abstract) COLT 2024 Transformers are Minimax Optimal Nonparametric In-Context Learners NeurIPS 2024 Transformers as Game Players: Provable In-context Game-playing Capabilities of Pre-trained Models NeurIPS 2024 Transformers Implement Functional Gradient Descent to Learn Non-Linear Functions In Context ICML 2024 Transformers Learn Nonlinear Features In Context: Nonconvex Mean-field Dynamics on the Attention Landscape ICML 2024 Transformers Learn to Achieve Second-Order Convergence Rates for In-Context Linear Regression NeurIPS 2024 Transformers on Markov data: Constant depth suffices NeurIPS 2024