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Existing continual learning (CL) methods mainly rely on fine-tuning or adapting large language mod- els (LLMs). They still suffer from catastrophic for- getting (CF). Little work has been done to exploit in-context learning (ICL) to leverage the extensive knowledge within LLMs for CL without updating any parameters. However, incrementally learning each new task in ICL necessitates adding training examples from each class of the task to the prompt, which hampers scalability as the prompt length in- creases. This issue not only leads to excessively long prompts that exceed the input token limit of the underlying LLM but also degrades the model’s performance due to the overextended context. To address this, we introduce InCA, a novel approach that integrates an external continual learner (ECL) with ICL to enable scalable CL without CF. The ECL is built incrementally to pre-select a small subset of likely classes for each test instance. By restricting the ICL prompt to only these selected classes, InCA prevents prompt lengths from becom- ing excessively long, while maintaining high per- formance. Experimental results demonstrate that InCA significantly outperforms existing CL base- lines, achieving substantial performance gains.
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This content will become publicly available on January 19, 2026
Continual Learning Using Only Large Language Model Prompting
We introduce CLOB, a novel continual learning (CL) paradigm wherein a large language model (LLM) is regarded as a black box. Learning is done incrementally via only verbal prompting. CLOB does not fine-tune any part of the LLM or add any trainable parameters to it. It is particularly suitable for LLMs that are accessible via APIs. We also propose a new CL technique, called CIS, based on incremental summarization that also overcomes the LLM’s input length limit. Experiments show CIS outperforms baselines by a very large margin.
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- Award ID(s):
- 2229876
- PAR ID:
- 10577384
- Publisher / Repository:
- The 31st International Conference on Computational Linguistics (COLING-2025)
- Date Published:
- Format(s):
- Medium: X
- Location:
- Abu Dhabi, UAE
- Sponsoring Org:
- National Science Foundation
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