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05/15/2023
RL4F: Generating Natural Language Feedback with Reinforcement Learning for Repairing Model Outputs
Despite their unprecedented success, even the largest language models ma...
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11/28/2022
What learning algorithm is in-context learning? Investigations with linear models
Neural sequence models, especially transformers, exhibit a remarkable ca...
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09/29/2022
Compositional Semantic Parsing with Large Language Models
Humans can reason compositionally when presented with new tasks. Previou...
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05/23/2022
Tracing Knowledge in Language Models Back to the Training Data
Neural language models (LMs) have been shown to memorize a great deal of...
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02/03/2022
Pre-Trained Language Models for Interactive Decision-Making
Language model (LM) pre-training has proven useful for a wide variety of...
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01/30/2022
Compositionality as Lexical Symmetry
Standard deep network models lack the inductive biases needed to general...
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10/13/2021
Subspace Regularizers for Few-Shot Class Incremental Learning
Few-shot class incremental learning – the problem of updating a trained ...
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06/07/2021
Lexicon Learning for Few-Shot Neural Sequence Modeling
Sequence-to-sequence transduction is the core problem in language proces...
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10/08/2020
Learning to Recombine and Resample Data for Compositional Generalization
Flexible neural models outperform grammar- and automaton-based counterpa...
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05/21/2018