Layer by Layer: Uncovering Where Multi-Task Learning Happens in Instruction-Tuned Large Language Models
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929561178996736 |
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| author | Zhao, Zheng Ziser, Yftah Cohen, Shay B. |
| author_facet | Zhao, Zheng Ziser, Yftah Cohen, Shay B. |
| contents | Fine-tuning pre-trained large language models (LLMs) on a diverse array of tasks has become a common approach for building models that can solve various natural language processing (NLP) tasks. However, where and to what extent these models retain task-specific knowledge remains largely unexplored. This study investigates the task-specific information encoded in pre-trained LLMs and the effects of instruction tuning on their representations across a diverse set of over 60 NLP tasks. We use a set of matrix analysis tools to examine the differences between the way pre-trained and instruction-tuned LLMs store task-specific information. Our findings reveal that while some tasks are already encoded within the pre-trained LLMs, others greatly benefit from instruction tuning. Additionally, we pinpointed the layers in which the model transitions from high-level general representations to more task-oriented representations. This finding extends our understanding of the governing mechanisms of LLMs and facilitates future research in the fields of parameter-efficient transfer learning and multi-task learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_20008 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Layer by Layer: Uncovering Where Multi-Task Learning Happens in Instruction-Tuned Large Language Models Zhao, Zheng Ziser, Yftah Cohen, Shay B. Computation and Language Machine Learning Fine-tuning pre-trained large language models (LLMs) on a diverse array of tasks has become a common approach for building models that can solve various natural language processing (NLP) tasks. However, where and to what extent these models retain task-specific knowledge remains largely unexplored. This study investigates the task-specific information encoded in pre-trained LLMs and the effects of instruction tuning on their representations across a diverse set of over 60 NLP tasks. We use a set of matrix analysis tools to examine the differences between the way pre-trained and instruction-tuned LLMs store task-specific information. Our findings reveal that while some tasks are already encoded within the pre-trained LLMs, others greatly benefit from instruction tuning. Additionally, we pinpointed the layers in which the model transitions from high-level general representations to more task-oriented representations. This finding extends our understanding of the governing mechanisms of LLMs and facilitates future research in the fields of parameter-efficient transfer learning and multi-task learning. |
| title | Layer by Layer: Uncovering Where Multi-Task Learning Happens in Instruction-Tuned Large Language Models |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2410.20008 |