MoRAL: MoE Augmented LoRA for LLMs’ Lifelong Learning proposes a novel approach for lifelong learning in large language models (LLMs). MoRAL combines the multi-tasking capabilities of Mixture-of-Experts (MoE) with the parameter-efficient fine-tuning of Low-Rank Adaptation (LoRA) to enable efficient lifelong learning. Unlike conventional methods that rely on factual triplets, MoRAL uses simple question-answer pairs, which are more practical and effective for robust learning. The paper introduces a new benchmark, 5L-bench, which includes a newly curated dataset of question-answer pairs and evaluation metrics for open-book and closed-book settings. Experimental results show that MoRAL significantly improves performance in open-book settings, with up to 30.15% improvement in "RA" for Phi-2-2.7B compared to closed-book settings. MoRAL also demonstrates better performance for models with more parameters and is more robust to catastrophic forgetting. The paper evaluates MoRAL across various LLMs, showing its effectiveness in both open-book and closed-book scenarios, as well as cross-settings. The results highlight MoRAL's potential for enhancing LLMs' ability to adapt to new data and retain knowledge over time.MoRAL: MoE Augmented LoRA for LLMs’ Lifelong Learning proposes a novel approach for lifelong learning in large language models (LLMs). MoRAL combines the multi-tasking capabilities of Mixture-of-Experts (MoE) with the parameter-efficient fine-tuning of Low-Rank Adaptation (LoRA) to enable efficient lifelong learning. Unlike conventional methods that rely on factual triplets, MoRAL uses simple question-answer pairs, which are more practical and effective for robust learning. The paper introduces a new benchmark, 5L-bench, which includes a newly curated dataset of question-answer pairs and evaluation metrics for open-book and closed-book settings. Experimental results show that MoRAL significantly improves performance in open-book settings, with up to 30.15% improvement in "RA" for Phi-2-2.7B compared to closed-book settings. MoRAL also demonstrates better performance for models with more parameters and is more robust to catastrophic forgetting. The paper evaluates MoRAL across various LLMs, showing its effectiveness in both open-book and closed-book scenarios, as well as cross-settings. The results highlight MoRAL's potential for enhancing LLMs' ability to adapt to new data and retain knowledge over time.