使用Gauss-Tin增强LLM的记忆召回：一种混合指令与高斯回放的方法
Enhancing Memory Recall in LLMs with Gauss-Tin: A Hybrid Instructional and Gaussian Replay Approach
AI总结：
为解决LLMs的灾难性遗忘问题，提出结合回放策略与高斯混合模型并辅以指令引导的Gauss-Tin方法，显著提升了模型保留过往知识的能力，保留指标较传统方法提升6%。
摘要：
尽管大型语言模型（LLMs）取得了显著进展，但灾难性遗忘仍是一个重大挑战，即模型在学习新信息时会丢失之前掌握的知识。持续学习（CL）策略作为解决该问题的潜在方案应运而生，其中基于回放的技术在保留已学知识方面表现出卓越的性能。在此背景下，我们引入了Gauss-Tin，这是一种新颖的方法，它将回放策略与高斯混合模型相结合，以提升训练过程中样本选择的质量，并辅以指令引导来促进对过往学习的生成。该方法旨在通过策略性地强化重要的过往学习同时容纳新信息，来改善LLMs的保留能力。我们的实验结果表明，与传统方法相比，保留指标有6%的显著提升，这表明Gauss-Tin是缓解LLMs中灾难性遗忘的有效策略。本研究强调了混合模型在增强LLMs于动态学习环境中的鲁棒性和适应性方面的潜力。
英文摘要：
Despite the significant advancements in Large Language Models (LLMs), catastrophic forgetting remains a substantial challenge, where models lose previously acquired knowledge upon learning new information. Continual learning (CL) strategies have emerged as a potential solution to this problem, with replay-based techniques demonstrating superior performance in preserving learned knowledge. In this context, we introduce Gauss-Tin, a novel approach that integrates the replay strategy with a Gaussian mixture model to enhance the quality of sample selection during training, supplemented by instructional guidance to facilitate the generation of past learning. This method aims to improve LLMs' retention capabilities by strategically reinforcing important past learnings while accommodating new information. Our experimental results indicate a promising 6\% improvement in retention metrics over traditional methods, suggesting that Gauss-Tin is an effective strategy for mitigating catastrophic forgetting in LLMs. This study underscores the potential of hybrid models in enhancing the robustness and adaptability of LLMs in dynamic learning environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
