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Improving Span Representation for Domain-adapted Coreference Resolution ...
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Are Larger Pretrained Language Models Uniformly Better? Comparing Performance at the Instance Level ...
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Abstract:
Read paper: https://www.aclanthology.org/2021.findings-acl.334 Abstract: Larger language models have higher accuracy on average, but are they better on every single instance (datapoint)? Some work suggests larger models have higher out-of-distribution robustness, while other work suggests they have lower accuracy on rare subgroups. To understand these differences, we investigate these models at the level of individual instances. However, one major challenge is that individual predictions are highly sensitive to noise in the randomness in training. We develop statistically rigorous methods to address this, and after accounting for pretraining and finetuning noise, we find that our BERT-Large is worse than BERT-Mini on at least 1-4% of instances across MNLI, SST-2, and QQP, compared to the overall accuracy improvement of 2-10%. We also find that finetuning noise increases with model size, and that instance-level accuracy has momentum: improvement from BERT-Mini to BERT-Medium correlates with improvement from ...
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Keyword:
Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
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URL: https://dx.doi.org/10.48448/yyy2-1q93 https://underline.io/lecture/26425-are-larger-pretrained-language-models-uniformly-betterquestion-comparing-performance-at-the-instance-level
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Temporal Adaptation of BERT and Performance on Downstream Document Classification: Insights from Social Media ...
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An Empirical Study on Multiple Information Sources for Zero-Shot Fine-Grained Entity Typing ...
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Looking for Confirmations: An Effective and Human-Like Visual Dialogue Strategy ...
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MRF-Chat: Improving Dialogue with Markov Random Fields ...
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CrossVQA: Scalably Generating Benchmarks for Systematically Testing VQA Generalization ...
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Latent Hatred: A Benchmark for Understanding Implicit Hate Speech ...
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STaCK: Sentence Ordering with Temporal Commonsense Knowledge ...
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ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning ...
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GTM: A Generative Triple-wise Model for Conversational Question Generation ...
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Keep It Simple: Unsupervised Simplification of Multi-Paragraph Text ...
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GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling ...
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Discovering Dialog Structure Graph for Coherent Dialog Generation ...
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