Papers by Gregory Yauney
Data Similarity is Not Enough to Explain Language Model Performance (2023.emnlp-main)
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| Challenge: | Large language models perform well on many but not all downstream tasks. |
| Approach: | They compare large language models with downstream benchmarks to determine whether similarity measures correlate with model performance. |
| Outcome: | The results show that similarity measures are not correlated with accuracy or each other in other benchmarks. |
Comparing Text Representations: A Theory-Driven Approach (2021.emnlp-main)
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| Challenge: | Recent advances in NLP have been made by learning representations that transform complex tasks into simple classification tasks. |
| Approach: | They propose a method to evaluate the compatibility between representations and tasks by fitting text features to specific characteristics of text datasets. |
| Outcome: | The proposed model provides a calibrated, quantitative measure of the difficulty of a classification-based NLP task. |
A Pretrainer’s Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity (2024.naacl-long)
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Shayne Longpre, Gregory Yauney, Emily Reif, Katherine Lee, Adam Roberts, Barret Zoph, Denny Zhou, Jason Wei, Kevin Robinson, David Mimno, Daphne Ippolito
| Challenge: | a large number of pretraining data design practices are under-documented, authors say . authors: strong performance of modern language models depends on selfsupervised pretraining . |
| Approach: | They propose to pretrain models on data curated at different collection times . they find temporal shift between evaluation data and pretraining data leads to performance degradation . |
| Outcome: | The results validate, quantify, and expose many undocumented intuitions about text pretraining . authors say this practice has outperformed other models in the field . |
Domain-Specific Lexical Grounding in Noisy Visual-Textual Documents (2020.emnlp-main)
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| Challenge: | Existing image-text grounding approaches require detailed annotations, authors say . existing methods are difficult to adapt to unlabeled multi-image, multi-sentence documents, they say . |
| Approach: | They propose a method that can learn contextual meanings from unlabeled documents . they demonstrate that a simple unsupervised clustering-based method can be useful . |
| Outcome: | The proposed method is particularly effective for local contextual meanings of a word . existing image-text grounding methods are difficult to adapt to unlabeled multi-image, multi-sentence documents . |