Self-Tuning: Instructing LLMs to Effectively Acquire New Knowledge through Self-Teaching
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913840108666880 |
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| author | Zhang, Xiaoying Peng, Baolin Tian, Ye Zhou, Jingyan Zhang, Yipeng Mi, Haitao Meng, Helen |
| author_facet | Zhang, Xiaoying Peng, Baolin Tian, Ye Zhou, Jingyan Zhang, Yipeng Mi, Haitao Meng, Helen |
| contents | Large language models (LLMs) often struggle to provide up-to-date information due to their one-time training and the constantly evolving nature of the world. To keep LLMs current, existing approaches typically involve continued pre-training on new documents. However, they frequently face difficulties in extracting stored knowledge. Motivated by the remarkable success of the Feynman Technique in efficient human learning, we introduce Self-Tuning, a learning framework aimed at improving an LLM's ability to effectively acquire new knowledge from unseen raw documents through self-teaching. Specifically, we develop a Self-Teaching strategy that augments the documents with a set of knowledge-intensive tasks created in a self-supervised manner, focusing on three crucial aspects: memorization, comprehension, and self-reflection. Additionally, we introduce three Wiki-Newpages-2023-QA datasets to facilitate an in-depth analysis of an LLM's knowledge acquisition ability concerning memorization, extraction, and reasoning. Extensive experimental results on various models, e.g., Llama2-7B reveal that Self-Tuning consistently exhibits superior performance across all knowledge acquisition tasks and excels in preserving previous knowledge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_06326 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Self-Tuning: Instructing LLMs to Effectively Acquire New Knowledge through Self-Teaching Zhang, Xiaoying Peng, Baolin Tian, Ye Zhou, Jingyan Zhang, Yipeng Mi, Haitao Meng, Helen Computation and Language Large language models (LLMs) often struggle to provide up-to-date information due to their one-time training and the constantly evolving nature of the world. To keep LLMs current, existing approaches typically involve continued pre-training on new documents. However, they frequently face difficulties in extracting stored knowledge. Motivated by the remarkable success of the Feynman Technique in efficient human learning, we introduce Self-Tuning, a learning framework aimed at improving an LLM's ability to effectively acquire new knowledge from unseen raw documents through self-teaching. Specifically, we develop a Self-Teaching strategy that augments the documents with a set of knowledge-intensive tasks created in a self-supervised manner, focusing on three crucial aspects: memorization, comprehension, and self-reflection. Additionally, we introduce three Wiki-Newpages-2023-QA datasets to facilitate an in-depth analysis of an LLM's knowledge acquisition ability concerning memorization, extraction, and reasoning. Extensive experimental results on various models, e.g., Llama2-7B reveal that Self-Tuning consistently exhibits superior performance across all knowledge acquisition tasks and excels in preserving previous knowledge. |
| title | Self-Tuning: Instructing LLMs to Effectively Acquire New Knowledge through Self-Teaching |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2406.06326 |