| Challenge: | Existing studies on how to select appropriate data to pretrain word vectors or LMs are lacking. |
| Approach: | They propose to quantify aspects of similarity between pretraining and target data. |
| Outcome: | The proposed measures are good predictors of the usefulness of pretrained models for Named Entity Recognition over 30 data pairs. |
Similar Papers
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. |
When Do You Need Billions of Words of Pretraining Data? (2021.acl-long)
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| Challenge: | Pretrained language models (LMs) are dominated by models that can encode billions of words. |
| Approach: | They use classifier probing, information-theoretic probing and unsupervised relative acceptability judgments to evaluate model ability. |
| Outcome: | The proposed models require only about 10M to 100M words to learn to encode most syntactic and semantic features. |
Deciphering the Impact of Pretraining Data on Large Language Models through Machine Unlearning (2024.findings-acl)
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| Challenge: | Existing studies have suggested that the composition of the pretraining corpus exerts a significant impact upon the performance of LLMs. |
| Approach: | They analyze the impact of 48 datasets from 5 major categories of pretraining data of Large Language Models and measure their impacts on LLMs using benchmarks about nine major categories. |
| Outcome: | The proposed analysis provides insights into the organization of data to support more efficient pretraining of Large Language Models. |
To Pretrain or Not to Pretrain: Examining the Benefits of Pretrainng on Resource Rich Tasks (2020.acl-main)
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| Challenge: | Existing studies on pretraining NLP models with variants of Masked Language Model (MLM) objectives have shown that the number of training samples used in the downstream task is limited. |
| Approach: | They propose to use MLM objectives to pretrain NLP models with variants of Masked Language Model (MLM) objectives to improve accuracy on downstream tasks. |
| Outcome: | The proposed model can reach a diminishing return point as the supervised data size increases significantly. |
A Comparison of Language Modeling and Translation as Multilingual Pretraining Objectives (2024.emnlp-main)
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| Challenge: | Pretrained language models (PLMs) display impressive performances and have captured the attention of the NLP community. |
| Approach: | They propose to compare multilingual pretraining objectives in a controlled methodological environment with multilingual models. |
| Outcome: | The proposed model outperforms existing models in 6 languages and demonstrates that multilingual translation is an effective pretraining objective under the right conditions. |
How Much Pretraining Does Structured Data Need? (2026.eacl-long)
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| Challenge: | Large language models are increasingly adopted for handling structured data, despite pretraining on unstructured text. |
| Approach: | They propose to re-initialize subsets of layers with random weights before fine-tuning on structured datasets. |
| Outcome: | The proposed models are compared to unstructured datasets and show that they perform well over structured data. |
Similarity Analysis of Contextual Word Representation Models (2020.acl-main)
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| Challenge: | Existing and novel similarity measures are used to analyze contextual word representations . different architectures have rather similar representations, but different individual neurons. |
| Approach: | They propose a method to analyze contextual word representation models using similarity analysis. |
| Outcome: | The proposed approach can be used to analyze model similarity without external annotations. |
Target-Aware Language Modeling via Granular Data Sampling (2024.emnlp-main)
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Ernie Chang, Pin-Jie Lin, Yang Li, Changsheng Zhao, Daeil Kim, Rastislav Rabatin, Zechun Liu, Yangyang Shi, Vikas Chandra
| Challenge: | Language model pretraining is the cornerstone of universal language models (LMs), creating generalpurpose representations to excel across a variety of downstream tasks. |
| Approach: | They propose to use multi-granular tokens to sample large-scale language models for domain-specific use cases. |
| Outcome: | The proposed model outperforms random sampled samples on eight benchmarks with 1% of the data and performs on par with the full RefinedWeb data. |
From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models (2023.acl-long)
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| Challenge: | Hundreds of studies have highlighted ethical issues in NLP models . |
| Approach: | They propose to measure media biases in LMs trained on diverse data sources . they focus on hate speech and misinformation detection . |
| Outcome: | The proposed methods quantify the fairness of downstream NLP models trained on politically biased LMs. |
Cost-effective Selection of Pretraining Data: A Case Study of Pretraining BERT on Social Media (2020.findings-emnlp)
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| Challenge: | Recent studies show that domain-specific BERT models can be improved when in-domain data is used for pretraining. |
| Approach: | They propose to use Twitter and forum text as pretraining sources for two BERT models and use similarity measures to nominate in-domain data for pretraining. |
| Outcome: | The proposed method can be used to improve performance on downstream tasks by using in-domain data. |