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Continual Learning of Natural Language Processing Tasks: A Survey

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arxiv 2211.12701 v2 pith:JWUIBFZP submitted 2022-11-23 cs.CL cs.AIcs.LGcs.NE

Zixuan Ke , Bing Liu This is my paper · ORCID
classification cs.CLcs.AIcs.LGcs.NE
keywords learningknowledgesurveytaskscontinualexistingforgettinglearned
verification ladder T0 review T1 audit T2 compute T3 formal
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Continual learning (CL) is a learning paradigm that emulates the human capability of learning and accumulating knowledge continually without forgetting the previously learned knowledge and also transferring the learned knowledge to help learn new tasks better. This survey presents a comprehensive review and analysis of the recent progress of CL in NLP, which has significant differences from CL in computer vision and machine learning. It covers (1) all CL settings with a taxonomy of existing techniques; (2) catastrophic forgetting (CF) prevention, (3) knowledge transfer (KT), which is particularly important for NLP tasks; and (4) some theory and the hidden challenge of inter-task class separation (ICS). (1), (3) and (4) have not been included in the existing survey. Finally, a list of future directions is discussed.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Rethinking Transfer in Continual Learning: A Replay-Based Realisation

    cs.LG 2026-07 conditional novelty 7.0 of 10

    In continual learning, forward transfer requires target headroom, a persistent carrier, and a compatible source; routing replay by gradient signatures improves accuracy and stability over uniform replay.

  2. ReCoLoRA: Spectrum-Aware Recursive Consolidation for Continual LLM Fine-Tuning

    cs.LG 2026-07 conditional novelty 6.0 of 10

    Spectrum-initialized LoRA with elbow ranks and recursive SVD consolidation of the effective weight beats rank-swept PEFT baselines on three of four 7–8B models in continual GLUE fine-tuning.

  3. LIFELONG SOTOPIA: Evaluating Social Intelligence of Language Agents Over Lifelong Social Interactions

    cs.AI 2025-06 conditional novelty 6.0 of 10

    Language agents' believability and goal achievement decline over multi-episode social interactions, and curated memory summaries only partially close the gap with humans.

  4. OrthoPhys: Physically Plausible Video Generation with Orthogonal-View Geometry Guidance

    cs.CV 2026-03 unverdicted novelty 5.0 of 10

    Generating synchronized four-view orthogonal foreground videos with geometry-enhanced attention, then using them as rigid guidance, improves physical realism in video generation over direct 2D methods.

  5. CCL-XCoT: An Efficient Cross-Lingual Knowledge Transfer Method for Mitigating Hallucination Generation

    cs.CL 2025-07 conditional novelty 5.0 of 10

    CCL-XCoT combines curriculum-based contrastive pretraining with cross-lingual chain-of-thought fine-tuning, lifting hallucination-free rates in low-resource QA from 1-18% to 55-74%.

  6. Continual Speech Learning with Fused Speech Features

    cs.CL 2025-06 conditional novelty 5.0 of 10

    Gated fusion of frozen Whisper layers improves continual learning on six speech tasks, with the double-stage variant best overall.

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