Continual Learning Using Only Large Language Model Prompting
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866908168671461376 |
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| author | Qiu, Jiabao Ke, Zixuan Liu, Bing |
| author_facet | Qiu, Jiabao Ke, Zixuan Liu, Bing |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_15479 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Continual Learning Using Only Large Language Model Prompting Qiu, Jiabao Ke, Zixuan Liu, Bing Computation and Language Artificial Intelligence 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. |
| title | Continual Learning Using Only Large Language Model Prompting |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2412.15479 |