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05/24/2023
How Predictable Are Large Language Model Capabilities? A Case Study on BIG-bench
We investigate the predictability of large language model (LLM) capabili...
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05/24/2023
Estimating Large Language Model Capabilities without Labeled Test Data
Large Language Models (LLMs) have exhibited an impressive ability to per...
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05/25/2022
Eliciting Transferability in Multi-task Learning with Task-level Mixture-of-Experts
Recent work suggests that transformer models are capable of multi-task l...
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10/16/2021
Sparse Distillation: Speeding Up Text Classification by Using Bigger Models
Distilling state-of-the-art transformer models into lightweight student ...
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04/18/2021
On the Influence of Masking Policies in Intermediate Pre-training
Current NLP models are predominantly trained through a pretrain-then-fin...
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03/18/2021
Refining Neural Networks with Compositional Explanations
Neural networks are prone to learning spurious correlations from biased ...
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01/02/2021
Zero-shot Learning by Generating Task-specific Adapters
Pre-trained text-to-text transformers achieve impressive performance acr...
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12/31/2020
Studying Strategically: Learning to Mask for Closed-book QA
Closed-book question-answering (QA) is a challenging task that requires ...
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10/09/2020
Semi-Automated Protocol Disambiguation and Code Generation
For decades, Internet protocols have been specified using natural langua...
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05/02/2020
Teaching Machine Comprehension with Compositional Explanations
Advances in extractive machine reading comprehension (MRC) rely heavily ...
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11/04/2019
Learning to Annotate: Modularizing Data Augmentation for Text Classifiers with Natural Language Explanations
Deep neural networks usually require massive labeled data, which restric...
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11/04/2019
Learning to Annotate: Modularizing Data Augmentation for TextClassifiers with Natural Language Explanations
Deep neural networks usually require massive labeled data, which restric...
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04/19/2019