Recent Advances in Pre-trained Language Models: Why Do They Work and How Do They Work (2022.aacl-tutorials)
Copied to clipboard
| Challenge: | Pre-trained language models are language models that are pre-taught on large-scaled corpora in a self-supervised fashion. |
| Approach: | This tutorial provides a broad and comprehensive introduction to pre-trained language models . it focuses on emerging methods that enable PLMs to perform diverse downstream tasks . |
| Outcome: | This tutorial focuses on the benefits of pre-trained language models and how to use them in NLP tasks. |
Similar Papers
LaoPLM: Pre-trained Language Models for Lao (2022.lrec-1)
Copied to clipboard
| Challenge: | Pre-trained language models (PLMs) can capture different levels of concepts in context . previous work on Lao has been hampered by the lack of annotated datasets . |
| Approach: | They construct a text classification dataset to alleviate the resource-scarce situation of Lao . they evaluate them on two downstream tasks: part-of-speech tagging and text classification . |
| Outcome: | The proposed model can capture different levels of concepts in context and generate universal language representations. |
A Close Look into the Calibration of Pre-trained Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Pre-trained language models (PLMs) may fail in giving reliable estimates of their predictive uncertainty. |
| Approach: | They conduct fine-grained control experiments to study the dynamic change in PLMs’ calibration performance in training. |
| Outcome: | The proposed methods significantly reduce PLMs’ confidence in wrong predictions. |
On the Importance of Effectively Adapting Pretrained Language Models for Active Learning (2022.acl-short)
Copied to clipboard
| Challenge: | Recent active learning approaches in NLP use off-the-shelf pretrained language models (LMs) . a poor training strategy can be catastrophic for AL, authors argue . |
| Approach: | They propose to first adapt the pretrained LM to the target task and then use it for AL. |
| Outcome: | The proposed approach provides substantial data efficiency improvements compared to the standard fine-tuning approach. |
Revisiting Pre-trained Language Models and their Evaluation for Arabic Natural Language Processing (2022.emnlp-main)
Copied to clipboard
Abbas Ghaddar, Yimeng Wu, Sunyam Bagga, Ahmad Rashid, Khalil Bibi, Mehdi Rezagholizadeh, Chao Xing, Yasheng Wang, Xinyu Duan, Zhefeng Wang, Baoxing Huai, Xin Jiang, Qun Liu, Phillippe Langlais
| Challenge: | Existing pre-trained language models are not well-explored and are not reproducible in the literature. |
| Approach: | They propose to improve existing Arabic language pre-trained language models using a more methodical approach. |
| Outcome: | The proposed models outperform existing models on ALUE, a leaderboard-powered benchmark for Arabic NLU and NLG tasks. |
Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis (2022.findings-emnlp)
Copied to clipboard
Yuxin Xiao, Paul Pu Liang, Umang Bhatt, Willie Neiswanger, Ruslan Salakhutdinov, Louis-Philippe Morency
| Challenge: | Pre-trained language models (PLMs) have gained increasing popularity due to compelling prediction performance in diverse natural language processing tasks. |
| Approach: | They compare three popular options for encoding and Temp Scaling for PLMs . they recommend using Temp Loss as uncertainty quantifier and Focal Loss for fine-tuning . |
| Outcome: | Using pre-trained language models, we compare three options on NLP classification tasks and domain shift. |
Making Pre-trained Language Models both Task-solvers and Self-calibrators (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing work shows that pre-trained language models can be effective for high-stake applications, but they become overconfident in their wrong predictions. |
| Approach: | They propose to use extra data to train pre-trained language models to effectively utilize training samples to make them both task-solvers and self-calibrators. |
| Outcome: | The proposed method can be used in three downstream applications, including selective classification, adversarial defense, and model cascading. |
Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks (2026.acl-short)
Copied to clipboard
| Challenge: | Language models (LMs) are pre-trained on raw text datasets to generate text sequences token-by-token. |
| Approach: | They propose a framework that integrates Language Learning Tasks alongside standard next-token prediction to stimulate the acquisition of morphological, syntactic, and semantic knowledge. |
| Outcome: | The proposed framework improves performance on linguistic competence benchmarks while maintaining competitive performance on reasoning tasks. |
Meta-Learning the Difference: Preparing Large Language Models for Efficient Adaptation (2022.tacl-1)
Copied to clipboard
| Challenge: | Large pretrained language models are often domain- or task-adapted via finetuning or prompting. |
| Approach: | They propose to use domain-adaptive pretraining to prepare large pretrained language models for domain- or task-adaptation by learning to learn the difference between general and adapted PLMs. |
| Outcome: | Experiments on few-shot dialogue completion, low-resource abstractive summarization, and multi-domain language modeling show improvements in adaptation time and performance over finetuning or preparation via domain-adaptive pretraining. |
Retrieval-based Language Models and Applications (2023.acl-tutorials)
Copied to clipboard
| Challenge: | In this tutorial, we will provide a comprehensive overview of retrieval-based language models. |
| Approach: | This tutorial will provide a comprehensive overview of recent advances in retrieval-based language models. |
| Outcome: | This tutorial will provide a comprehensive overview of recent advances in retrieval-based language models. |
Development of Cognitive Intelligence in Pre-trained Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies show evidence for emergent cognitive abilities in Large Pre-trained Language Models (PLMs). Prior research into emergental cognitive abilities of PLMs has been path-independent to model training. |
| Approach: | They use four task categories to examine the alignment of ten popular families of PLMs and evaluate their performance to the developmental trajectories of children's thinking. |
| Outcome: | The results show that the models are more aligned to children's thinking than previous studies. |