Summary of Towards Robust Instruction Tuning on Multimodal Large Language Models, by Wei Han et al.
Towards Robust Instruction Tuning on Multimodal Large Language Models
by Wei Han, Hui Chen, Soujanya Poria
First submitted to arxiv on: 22 Feb 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 Medium Difficulty summary: Fine-tuning large language models (LLMs) on multi-task instruction-following data has been shown to be an effective way to improve their zero-shot capabilities on new tasks. Recent works have focused on generating and selecting high-quality instruction-following data, requiring significant human labor to create model-understandable instructions for specific tasks and filter LLM-generated data. This paper introduces INSTRAUG, an automatic instruction augmentation method that can expand a multimodal instruction-following dataset by 30 times starting from just a few basic meta instructions. The authors demonstrate the effectiveness of INSTRAUG on two popular multimodal benchmarks, MULTIINSTRUCT and InstructBLIP, showing that it can significantly improve the alignment of multimodal large language models (MLLMs) across 12 tasks, equivalent to scaling up training data multiple times. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper helps computers learn new things by giving them better instructions. Right now, people have to spend a lot of time creating these instructions for computers to understand. The authors invented a way to make this process faster and more efficient using something called INSTRAUG. They tested it on two types of tasks and showed that it can help computers get better at understanding and following instructions. |
Keywords
» Artificial intelligence » Alignment » Fine tuning » Multi task » Zero shot
