Preserving Knowledge in Large Language Model with Model-Agnostic Self-Decompression

• URL: http://arxiv.org/abs/2406.11354v2
• Date: Wed, 19 Jun 2024 11:36:30 GMT
• Title: Preserving Knowledge in Large Language Model with Model-Agnostic Self-Decompression
• Authors: Zilun Zhang, Yutao Sun, Tiancheng Zhao, Leigang Sha, Ruochen Xu, Kyusong Lee, Jianwei Yin,
• Abstract summary: Large Language Models (LLMs) often suffer from catastrophic forgetting when post-pretrained or supervised fine-tuned (SFT) on domain-specific data. This paper focuses on TG-SFT, which can synthetically generate SFT data for the instruction tuning steps.
• Score: 40.4998607679863
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Humans can retain old knowledge while learning new information, but Large Language Models (LLMs) often suffer from catastrophic forgetting when post-pretrained or supervised fine-tuned (SFT) on domain-specific data. Moreover, for Multimodal Large Language Models (MLLMs) which are composed of the LLM base and visual projector (e.g. LLaVA), a significant decline in performance on language benchmarks was observed compared to their single-modality counterparts. To address these challenges, we introduce a novel model-agnostic self-decompression method, Tree Generation (TG), that decompresses knowledge within LLMs into the training corpus. This paper focuses on TG-SFT, which can synthetically generate SFT data for the instruction tuning steps. By incorporating the dumped corpus during SFT for MLLMs, we significantly reduce the forgetting problem.

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