Summary of Towards Understanding Multi-task Learning (generalization) Of Llms Via Detecting and Exploring Task-specific Neurons, by Yongqi Leng and Deyi Xiong
Towards Understanding Multi-Task Learning (Generalization) of LLMs via Detecting and Exploring Task-Specific Neurons
by Yongqi Leng, Deyi Xiong
First submitted to arxiv on: 9 Jul 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 The paper investigates the learning mechanisms behind large language models (LLMs) by analyzing neurons responsible for specific tasks. The authors propose a novel approach to detect task-sensitive neurons using gradient attribution and demonstrate their correlation with given tasks. They find that task-specific neurons are strongly associated with generalization and specialization across tasks, leading to insights into the interpretability of LLMs in multi-task learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how large language models learn multiple tasks. Scientists detected special “task-sensitive” neurons within these models, which are connected to specific tasks. The study shows that these task-specific neurons help the model generalize and specialize across different tasks. This research provides valuable insights into how large language models work when learning many things at once. |
Keywords
* Artificial intelligence * Generalization * Multi task
