  Adapting large language models (LLMs) to new domains/tasks and enabling them
to be efficient lifelong learners is a pivotal challenge. In this paper, we
propose MoRAL, i.e., Mixture-of-Experts augmented Low-Rank Adaptation for
Lifelong Learning. MoRAL combines the multi-tasking abilities of MoE with the
fine-tuning abilities of LoRA for effective life-long learning of LLMs. In
contrast to the conventional approaches that use factual triplets as inputs
MoRAL relies on simple question-answer pairs, which is a more practical and
effective strategy for robust and efficient learning. Owing to new data
settings, we introduce a new evaluation benchmark namely: Life Long Learning of
LLM (5L-bench) encompassing a newly curated dataset of question-answer pairs,
and a set of evaluation metrics for rigorous evaluation of MoRAL in open-book
and closed-book settings. Experimental evaluation shows (i) LLMs learn fast in
open-book settings with up to 30.15% improvement in "RA" for Phi-2-2.7B
compared to closed-book (for models fine-tuned with MoRAL); (ii) MoRAL shows
higher performance improvement for models with a greater number of parameters;
(iii) MoRAL is robust to catastrophic forgetting offering better knowledge
retention compared to baselines.
