Prateek Yadav, Qing Sun, Hantian Ding, Xiaopeng Li, Dejiao Zhang, Ming Tan, Parminder Bhatia, Xiaofei Ma, Ramesh Nallapati, Murali Krishna Ramanathan, Mohit Bansal, Bing Xiang
| Challenge: | Large-scale code generation models such as Copilot and CodeT5 are expensive to train and re-train. |
| Approach: | They propose a benchmark for Continual Learning (CL) that covers a wide range of tasks with different input and output programming languages. |
| Outcome: | The proposed method improves on Prompt Pooling with Teacher Forcing, which suffers catastrophic forgetting due to stark distribution shifts in coding tasks. |
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Weixiang Zhao, Shilong Wang, Yulin Hu, Yanyan Zhao, Bing Qin, Xuanyu Zhang, Qing Yang, Dongliang Xu, Wanxiang Che
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| Challenge: | Existing continuous learning paradigms fine-tune language model parameters or use adapters or variants to adapt the LM. |
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TL-CL: Task And Language Incremental Continual Learning (2024.emnlp-main)
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| Challenge: | a multilingual model is periodically updated to accommodate new tasks in previously learned languages or new languages for established tasks. |
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| Challenge: | Existing neural approaches for natural language generation are typically developed offline for specific domains. |
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Continual Lifelong Learning in Natural Language Processing: A Survey (2020.coling-main)
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| Challenge: | Existing approaches to continual learning (CL) are costly and time-consuming. |
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Fine-tuned Language Models are Continual Learners (2022.emnlp-main)
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| Challenge: | Recent work on large language models relies on intuition that most tasks can be described via natural language instructions. |
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Continual Learning of Large Language Models (2025.emnlp-tutorials)
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| Challenge: | This tutorial explores the challenges of continual learning in large language models . participants will learn strategies to mitigate forgetting and manage data and evaluation pipelines . |
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Continual Training of Language Models for Few-Shot Learning (2022.emnlp-main)
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| Challenge: | Recent work on applying large language models (LMs) achieves impressive performance in many NLP applications. |
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CITB: A Benchmark for Continual Instruction Tuning (2023.findings-emnlp)
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| Challenge: | Existing methods for instruction tuning do not leverage the rich natural language instructions. |
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