Summary of Towards Better Multi-task Learning: a Framework For Optimizing Dataset Combinations in Large Language Models, by Zaifu Zhan et al.
Towards Better Multi-task Learning: A Framework for Optimizing Dataset Combinations in Large Language Models
by Zaifu Zhan, Rui Zhang
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed framework efficiently selects optimal dataset combinations to enhance multi-task learning (MTL) performance in large language models. A neural network predicts the best combinations, refining the selection iteratively and improving efficiency while being model-, dataset-, and domain-independent. Experiments on 12 biomedical datasets across four tasks demonstrate that the approach effectively identifies better combinations, even for seemingly unpromising tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to choose the right combination of data sets helps large language models learn many things at once. A special computer program predicts which data sets will work best and then tries out different options to find the very best one. This makes it faster and more efficient to use these powerful language models for lots of tasks, like finding important words or understanding events in text. |
Keywords
» Artificial intelligence » Multi task » Neural network
