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In-Context Learning

336 directly classified papers

Papers per year

Papers

Data Distributional Properties Drive Emergent In-Context Learning in Transformers NIPS 2022
End-to-end Algorithm Synthesis with Recurrent Networks: Extrapolation without Overthinking NIPS 2022
The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning NIPS 2022
Exploring Length Generalization in Large Language Models NIPS 2022
What Makes Instruction Learning Hard? An Investigation and a New Challenge in a Synthetic Environment EMNLP 2022
Robustness of Demonstration-based Learning Under Limited Data Scenario EMNLP 2022
Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations EMNLP 2022
Leveraging the Inductive Bias of Large Language Models for Abstract Textual Reasoning NIPS 2021
What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers EMNLP 2021
Analyzing BERT’s Knowledge of Hypernymy via Prompting EMNLP 2021
Incremental Identification of Qualitative Models of Biological Systems using Inductive Logic Programming JMLR 2008