Papers with Continual learning
Refining Sample Embeddings with Relation Prototypes to Enhance Continual Relation Extraction (2021.acl-long)
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| Challenge: | Existing methods to extract relationships from texts depend on memory size and replay these memorized samples in subsequent tasks. |
| Approach: | They propose to use a model to extract relations between entities from texts where the samples of different relations are delivered into the model continuously. |
| Outcome: | The proposed model outperforms the state-of-the-art models and avoids catastrophic forgetting. |
Dynamic Dialogue Policy for Continual Reinforcement Learning (2022.coling-1)
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Christian Geishauser, Carel van Niekerk, Hsien-chin Lin, Nurul Lubis, Michael Heck, Shutong Feng, Milica Gašić
| Challenge: | Continual reinforcement learning of the dialogue policy has remained unaddressed . lack of a framework with training protocols, baseline models and suitable metrics has hindered research in this direction. |
| Approach: | They propose a continual learning algorithm, baseline architectures and metrics for assessing continual reinforcement learning models. |
| Outcome: | The proposed architecture can integrate new knowledge seamlessly and achieve significant zero-shot performance when exposed to unseen domains. |
Parameter-efficient Continual Learning Framework in Industrial Real-time Text Classification System (2022.naacl-industry)
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Tao Zhu, Zhe Zhao, Weijie Liu, Jiachi Liu, Yiren Chen, Weiquan Mao, Haoyan Liu, Kunbo Ding, Yudong Li, Xuefeng Yang
| Challenge: | Existing continual learning methods use data replay, parameter isolation and regularization to mitigate catastrophic forgetting. |
| Approach: | They propose a parameter-efficient continual learning framework that updates parameters offline and then trains using an online regularization method. |
| Outcome: | The proposed framework reduces catastrophic forgetting and saves the model with the changed parameters instead of all parameters. |
Sparse Adapter Fusion for Continual Learning in NLP (2026.eacl-long)
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| Challenge: | Existing methods for learning continuously face challenges such as inefficient parameter reuse across tasks and catastrophic forgetting when tasks are dissimilar. |
| Approach: | They propose a Sparse Adapter Fusion Method which dynamically fuses old and new adapters to address these challenges. |
| Outcome: | The proposed method outperforms state-of-the-art methods while utilizing less than 60% of the parameters. |
Rehearsal-Free Modular and Compositional Continual Learning for Language Models (2024.naacl-short)
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| Challenge: | Existing methods to overcome catastrophic forgetting are rehearsal-based and parameter isolation-based. |
| Approach: | They propose a rehearsal-free framework which continuously adds new modules to language models and composes them with existing modules. |
| Outcome: | Experiments on benchmarks show that MoCL outperforms state-of-the-art and effectively facilitates knowledge transfer. |
Generative Replay Inspired by Hippocampal Memory Indexing for Continual Language Learning (2023.eacl-main)
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| Challenge: | Continual learning (CL) is a fundamental requirement for human-like general intelligence (Parisi et al., 2019). |
| Approach: | They propose to control sample generation using compressed features of previous training samples by using hippocampal memory indexing to enhance the generative replay. |
| Outcome: | The proposed method outperforms current generative replay methods and generates training samples from previous tasks. |
Task-wrapped Continual Learning in Task-Oriented Dialogue Systems (2025.findings-naacl)
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| Challenge: | Continual learning is vital for task-oriented dialogue systems (ToDs), but its performance is limited by training separate adapters for each task, preventing global knowledge sharing. |
| Approach: | They propose a framework that employs task-wrapped Adapters to learn global and task-specific information through parameter sharing. |
| Outcome: | The proposed framework outperforms AdapterCL in 37 domains while using only 46% of the parameters. |
SEEKR: Selective Attention-Guided Knowledge Retention for Continual Learning of Large Language Models (2024.emnlp-main)
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| Challenge: | Existing methods fail to fully exploit the knowledge embedded in models from previous tasks . Existing techniques fail to exploit the information embedded in previous tasks, resulting in a large number of replay samples to achieve good results. |
| Approach: | They propose a method that uses attention weights to extract knowledge from previous tasks . they use a data replay strategy to extract the knowledge from the previous tasks. |
| Outcome: | The proposed method achieves comparable or even better performance with only 1/10 of replayed data used by other methods. |
Continual Learning for Text Classification with Information Disentanglement Based Regularization (2021.naacl-main)
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| Challenge: | Existing continual learning methods focus on preserving knowledge from previous tasks . Continual learning is a useful tool for learning over time, but it is not always possible to generalize to new tasks. |
| Approach: | They propose a disentanglement-based regularization method for continual learning on text classification that disentangles text hidden spaces into generic representations and regularizes them differently to constrain knowledge required to generalize. |
| Outcome: | The proposed method disentangles text hidden spaces into representations that are generic to all tasks and representations specific to each individual task. |
Continual Sequence Generation with Adaptive Compositional Modules (2022.acl-long)
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| Challenge: | Existing work on continual sequence generation relies on reuse of existing parameters to learn new tasks or blindly adds new parameters for each new task. |
| Approach: | They propose to use continual sequence generation to reuse existing parameters for new tasks . current continuous learning models often forget knowledge of dissimilar tasks if data distributions shift . |
| Outcome: | The proposed framework outperforms state-of-the-art models on various sequences of generation tasks. |
SKD-NER: Continual Named Entity Recognition via Span-based Knowledge Distillation with Reinforcement Learning (2023.emnlp-main)
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| Challenge: | Continual learning for named entity recognition (CL-NER) aims to enable models to continuously learn new entity types while retaining the ability to recognize previously learned ones. |
| Approach: | They propose a model that leverages knowledge distillation to retain memory and employs reinforcement learning strategies to optimize the soft labeling and distillation losses generated by the teacher model to effectively prevent catastrophic forgetting. |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets showing that it significantly improves the performance of the CL-NER task. |
Depth Aware Hierarchical Replay Continual Learning for Knowledge Based Question Answering (2024.lrec-main)
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| Challenge: | Continual learning models adapt well to the latest data but lose ability to remember past data due to changes in the data source. |
| Approach: | They propose a hierarchical replay framework that allows models to keep a small memory of previous learned data that uses replay. |
| Outcome: | The proposed model outperforms previous continual learning methods in mitigating catastrophic forgetting. |
Task-Attentive Transformer Architecture for Continual Learning of Vision-and-Language Tasks Using Knowledge Distillation (2023.findings-emnlp)
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| Challenge: | Existing algorithms for learning unimodal vision-only or language-only tasks are limited by the size and computational load of fine-tuning large-scale pre-trained neural networks. |
| Approach: | They propose a transformer-based CL architecture for learning bimodal vision-and-language tasks by increasing the number of the learnable parameters dynamically and using knowledge distillation. |
| Outcome: | The proposed model reaches state-of-the-art on vision-and-language tasks. |
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. |
| Approach: | They propose to examine the problem of continual learning in NLP through the lens of various NLP tasks and provide a critical review of existing methods. |
| Outcome: | The proposed methods are critical to the development of CL models and provide a critical review of existing methods and datasets. |
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. |
| Approach: | They propose to use a benchmark to study how instruction tuning works in CL tasks. |
| Outcome: | The proposed method can achieve similar or better results than existing CL methods. |
AIMMerging: Adaptive Iterative Model Merging Using Training Trajectories for Language Model Continual Learning (2025.emnlp-main)
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Yujie Feng, Jian Li, Xiaoyu Dong, Pengfei Xu, Xiaohui Zhou, Yujia Zhang, Zexin Lu, Yasha Wang, Alan Zhao, Xu Chu, Xiao-Ming Wu
| Challenge: | Recent model merging-based methods struggle to effectively manage the trade-off between learning new knowledge and preventing catastrophic forgetting. |
| Approach: | They propose a model merging framework that utilizes learning and forgetting signals from the training trajectory to dynamically monitor the model’s training status. |
| Outcome: | The proposed framework achieves significant performance improvements over existing state-of-the-art methods on three CL benchmarks with various model sizes (from 770M to 13B). |
InfoCL: Alleviating Catastrophic Forgetting in Continual Text Classification from An Information Theoretic Perspective (2023.findings-emnlp)
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| Challenge: | Recent studies have identified the severe performance decrease on analogous classes as a key factor for catastrophic forgetting. |
| Approach: | They propose a replay-based continual text classification method that uses fast-slow and current-past contrastive learning to perform mutual information maximization and better recover previously learned representations. |
| Outcome: | The proposed method achieves state-of-the-art on three text classification tasks. |
Alignment Precedes Fusion: Open-Vocabulary Named Entity Recognition as Context-Type Semantic Matching (2023.findings-emnlp)
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| Challenge: | Continual learning and zero-shot learning approaches have not been adopted to scale to novel-emerging types. |
| Approach: | They propose a method to recognize entities in novel types by their textual names or descriptions. |
| Outcome: | The proposed method outperforms the state-of-the-art methods on three challenging OVNER benchmarks by 9.7%, 9.5%, and 1.8% F1-score of novel types. |
Sub-network Discovery and Soft-masking for Continual Learning of Mixed Tasks (2023.findings-emnlp)
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| Challenge: | Existing methods for Continual Learning (CL) have limited KT and catastrophic forgetting . a new method overcomes CF by isolating the knowledge of each task . |
| Approach: | They propose a method to overcome catastrophic forgetting and encourage knowledge transfer . they propose to discover a sub-network for each task and a soft-masking mechanism to preserve the previous knowledge. |
| Outcome: | The proposed method outperforms baselines in classification, generation, information extraction and their mixture. |
FOREVER: Forgetting Curve-Inspired Memory Replay for Language Model Continual Learning (2026.acl-long)
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Yujie Feng, Hao Wang, Jian Li, Xu Chu, Zhaolu Kang, Yiran Liu, Yasha Wang, Philip S. Yu, Xiao-Ming Wu
| Challenge: | Continual learning (CL) for large language models (LLMs) aims to enable sequential knowledge acquisition without catastrophic forgetting. |
| Approach: | They propose a framework that aligns replay schedules with a model-centric notion of time. |
| Outcome: | Experiments on three benchmarks show that FOREVER consistently mitigates catastrophic forgetting. |
Recurrent Knowledge Identification and Fusion for Language Model Continual Learning (2025.acl-long)
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Yujie Feng, Xujia Wang, Zexin Lu, Shenghong Fu, Guangyuan Shi, Yongxin Xu, Yasha Wang, Philip S. Yu, Xu Chu, Xiao-Ming Wu
| Challenge: | Continual learning (CL) is crucial for large language models without costly retraining. |
| Approach: | They propose a framework for recurrent knowledge identification and fusion that enables dynamic estimation of parameter importance distributions to enhance knowledge transfer. |
| Outcome: | The proposed framework mitigates catastrophic forgetting and enhances knowledge transfer. |