Summary of Layer by Layer: Uncovering Where Multi-task Learning Happens in Instruction-tuned Large Language Models, By Zheng Zhao et al.
Layer by Layer: Uncovering Where Multi-Task Learning Happens in Instruction-Tuned Large Language Models
by Zheng Zhao, Yftah Ziser, Shay B. Cohen
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A fine-tuning approach for large language models (LLMs) has become popular for tackling various natural language processing (NLP) tasks. However, the extent to which these models retain task-specific knowledge remains unclear. This study investigates how pre-trained LLMs store information across over 60 NLP tasks and whether instruction tuning improves their representations. Our analysis reveals that some tasks are already encoded in the pre-trained models, while others benefit from fine-tuning. We also identified the layers where the model transitions from general to task-oriented representations. This finding enhances our understanding of LLMs’ governing mechanisms, facilitating future research in parameter-efficient transfer learning and multi-task learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can do many things, like understand and generate text. But have you ever wondered how they remember information for different tasks? Researchers studied this to see if fine-tuning helps or not. They looked at over 60 natural language processing tasks and found that some tasks are already stored in the model’s memory, while others need a little help from fine-tuning. This research is important because it helps us understand how these models work, which can lead to new ways of using them. |
Keywords
» Artificial intelligence » Fine tuning » Instruction tuning » Multi task » Natural language processing » Nlp » Parameter efficient » Transfer learning
