Summary of Robustft: Robust Supervised Fine-tuning For Large Language Models Under Noisy Response, by Junyu Luo et al.
RobustFT: Robust Supervised Fine-tuning for Large Language Models under Noisy Response
by Junyu Luo, Xiao Luo, Kaize Ding, Jingyang Yuan, Zhiping Xiao, Ming Zhang
First submitted to arxiv on: 19 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 This research paper introduces a robust framework for supervised fine-tuning (SFT) of large language models (LLMs), addressing the challenge of noise in practical applications. The proposed RobustFT framework detects and relabels noise in downstream task data, leveraging multi-expert collaborative systems and context-enhanced strategies. The approach also incorporates an effective data selection mechanism based on response entropy to retain high-quality samples for fine-tuning. Experimental results demonstrate RobustFT’s superior performance in noisy scenarios across multiple LLMs and five datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps improve the way we adapt large language models to specific tasks or domains by making them more robust to noise in the data. The researchers developed a new approach called RobustFT that can identify and correct mistakes in the data, making it better for training the model. They tested their method on different models and datasets and showed that it works well even when there’s a lot of noise. |
Keywords
» Artificial intelligence » Fine tuning » Supervised
