Memorisation versus Generalisation in Pre-trained Language Models (2022.acl-long)
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| Challenge: | State-of-the-art pre-trained language models have been shown to memorise facts and perform well with limited amounts of training data. |
| Approach: | They propose to extend pre-trained language models to generalise and memorise facts in noisy and low-resource scenarios. |
| Outcome: | The proposed extension improves performance in low-resource named entity recognition tasks. |
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Boxi Cao, Qiaoyu Tang, Hongyu Lin, Shanshan Jiang, Bin Dong, Xianpei Han, Jiawei Chen, Tianshu Wang, Le Sun
| Challenge: | Pre-trained language models have shown remarkable memory formation, but vanilla networks without pre-training suffer catastrophic forgetting problem. |
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Methods for Estimating and Improving Robustness of Language Models (2022.naacl-srw)
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| Challenge: | Large language models suffer from weak generalisation ability due to shallow textual relations over full semantic complexity of the problem. |
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An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models (N19-1)
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| Challenge: | Existing transfer learning methods employ language models pretrained on large generic corpora, but results come at a high computational cost and require task-specific architectures. |
| Approach: | They propose a transfer learning approach that combine a task-specific optimization function with an auxiliary language model objective, which is adjusted during the training process. |
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Exploring Forgetting in Large Language Model Pre-Training (2025.acl-long)
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| Challenge: | Existing research on task-level forgetting in LLMs has focused on pretraining . but, there is limited attention to finer-grained forgetting during training . |
| Approach: | They investigated the existence and measurement of forgetting in pre-training . they examined low-cost, straightforward methods to mitigate forgetting during the pre- training phase . |
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On the Importance of Effectively Adapting Pretrained Language Models for Active Learning (2022.acl-short)
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| 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 . |
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Do Neural Language Models Overcome Reporting Bias? (2020.coling-main)
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| Challenge: | Recent studies show that pre-trained language models can overcome reporting bias by estimating the plausibility of rare but unspoken facts. |
| Approach: | They revisit the experiments conducted by Gordon and Van Durme (2013) . they find that pre-trained language models overestimate the very rare . |
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Text Classification with Few Examples using Controlled Generalization (N19-1)
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| Challenge: | Current training data for text classification is limited, resulting in limited generalization capacity. |
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A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios (2021.naacl-main)
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| Challenge: | a growing body of work is focused on improving performance in low-resource settings . a goal of this study is to explain how these methods differ in their requirements . |
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An Empirical Investigation Towards Efficient Multi-Domain Language Model Pre-training (2020.emnlp-main)
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| Challenge: | Pre-training large language models is a standard practice in the natural language processing community. |
| Approach: | They propose to use elastic weight consolidation to mitigate catastrophic forgetting when pre-trained large language models are evaluated on generic benchmarks. |
| Outcome: | The proposed model achieves state-of-the-art on out-of domain tasks with minimal pre-training . elastic weight consolidation provides best overall scores yielding only a 0.33% drop in performance across seven generic tasks while remaining competitive in bio-medical tasks. |
Adaptation Odyssey in LLMs: Why Does Additional Pretraining Sometimes Fail to Improve? (2024.emnlp-main)
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| Challenge: | In the last decade, the generalization and adaptation abilities of deep learning models were evaluated on fixed training and test distributions. |
| Approach: | They propose to train large language models on unlabeled text corpora and train them online. |
| Outcome: | The proposed model training on a text domain could degrade its perplexity on the test portion of the same domain. |