Computer Science > Machine Learning
[Submitted on 23 Aug 2023 (v1), last revised 11 Oct 2023 (this version, v2)]
Title:InstructionGPT-4: A 200-Instruction Paradigm for Fine-Tuning MiniGPT-4
View PDFAbstract:Multimodal large language models are typically trained in two stages: first pre-training on image-text pairs, and then fine-tuning using supervised vision-language instruction data. Recent studies have shown that large language models can achieve satisfactory results even with a limited amount of high-quality instruction-following data. In this paper, we introduce InstructionGPT-4, which is fine-tuned on a small dataset comprising only 200 examples, amounting to approximately 6\% of the instruction-following data used in the alignment dataset for MiniGPT-4. To achieve this, we first propose several metrics to access the quality of multimodal instruction data. Based on these metrics, we present an effective and trainable data selector to automatically identify and filter low-quality vision-language data. By employing this method, InstructionGPT-4 outperforms the original MiniGPT-4 on various evaluations. Overall, our findings demonstrate that less but high-quality instruction tuning data is efficient in enabling multimodal large language models to generate better output. Our code is available at this https URL.
Submission history
From: Weiran Huang [view email][v1] Wed, 23 Aug 2023 11:27:30 UTC (1,777 KB)
[v2] Wed, 11 Oct 2023 14:49:26 UTC (5,174 KB)
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