| Challenge: | Existing explanation methods are inefficient when explaining a static black-box model. |
| Approach: | They propose a Lifelong Explanation approach that continuously trains a student explainer under the supervision of a teacher on different tasks undertaken in LL. |
| Outcome: | The proposed approach can be extended to include a teacher and maintain the same level of faithfulness to the black-box model as the student explainer while being up to 102 times faster at test time. |
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| Challenge: | Existing methods for explaining outcome of machine learning models produce explanations, or rationales, which identify the attributions of features in an input example. |
| Approach: | They propose a Learning to Explain approach that learns the behaviour of an underlying explanation algorithm simultaneously from all training examples. |
| Outcome: | The proposed approach is 5 to 7.5104 times faster than existing models and has comparable faithfulness to the black-box model. |
Efficient Meta Lifelong-Learning with Limited Memory (2020.emnlp-main)
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| Challenge: | Existing natural language learning models fail to continuously learn new tasks as they are re-trained throughout their lifetime. |
| Approach: | They propose a meta-lifelong framework that combines three common lifelong learning principles . they propose to store past examples in episodic memory and replay them at training and inference time . |
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Self-Evolving GPT: A Lifelong Autonomous Experiential Learner (2024.acl-long)
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| Challenge: | Existing approaches to provide LLMs with textual task-solving experience rely on manual efforts to acquire and apply such experience for each task. |
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If an LLM Were a Character, Would It Know Its Own Story? Evaluating Lifelong Learning in LLMs (2026.acl-long)
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Siqi Fan, Xiusheng Huang, Yiqun Yao, Xuezhi Fang, Kang Liu, Peng Han, Shuo Shang, Aixin Sun, Yequan Wang
| Challenge: | Existing benchmarks for large language models (LLMs) fail to capture these dynamics, focusing on static, open-ended evaluations. |
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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. |
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| Outcome: | Experiments on three benchmarks show that FOREVER consistently mitigates catastrophic forgetting. |
SirLLM: Streaming Infinite Retentive LLM (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) are becoming increasingly prevalent in various domains, requiring a one-off input of overly long texts to maintain a degree of memory. |
| Approach: | They propose a Streaming Infinite Retentive LLM which allows LLMs to maintain longer memory during infinite-length dialogues without fine-tuning. |
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Rational LAMOL: A Rationale-based Lifelong Learning Framework (2021.acl-long)
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Kasidis Kanwatchara, Thanapapas Horsuwan, Piyawat Lertvittayakumjorn, Boonserm Kijsirikul, Peerapon Vateekul
| Challenge: | Existing paradigms for machine learning suffer from catastrophic forgetting when a model completely forgets what it just learned in previous tasks. |
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Lifelong Knowledge Editing for LLMs with Retrieval-Augmented Continuous Prompt Learning (2024.emnlp-main)
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| Challenge: | Existing methods to correct outdated or erroneous knowledge in large language models (LLMs) are slow and cumbersome, resulting in catastrophic knowledge forgetting and degradation of model performance. |
| Approach: | They propose a RetriEval-augmented ContInuous Prompt lEarning method that converts knowledge statements into short and informative continuous prompts, prefixed to the LLM’s input query embedding. |
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ClozEx: A Task toward Generation of English Cloze Explanation (2023.findings-emnlp)
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| Challenge: | Existing tasks and datasets specifically designed for generating language learner explanations for cloze questions are lacking . clozing questions are used to assess language proficiency and enhance language learning . |
| Approach: | They propose a task ClozEx to generate explanations for cloze questions in LA . they use a curated dataset of clozing questions paired with explanations . |
| Outcome: | The proposed task generates fluent explanations for cloze questions in English as a second language learners. |
Rationale-Enhanced Language Models are Better Continual Relation Learners (2023.emnlp-main)
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| Challenge: | Recent studies have found that catastrophic forgetting arises from the model’s lack of robustness against future analogous relations. |
| Approach: | They propose a multi-task rationale tuning strategy to help the model learn current relations robustly and conduct contrastive rationale replay to further distinguish analogous relations. |
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