  This paper studies the evolving domain of Continual Learning (CL) in large
language models (LLMs), with a focus on developing strategies for efficient and
sustainable training. Our primary emphasis is on continual domain-adaptive
pretraining, a process designed to equip LLMs with the ability to integrate new
information from various domains while retaining previously learned knowledge
and enhancing cross-domain knowledge transfer without relying on
domain-specific identification. Unlike previous studies, which mostly
concentrate on a limited selection of tasks or domains and primarily aim to
address the issue of forgetting, our research evaluates the adaptability and
capabilities of LLMs to changing data landscapes in practical scenarios. To
this end, we introduce a new benchmark designed to measure the adaptability of
LLMs to these evolving data environments, offering a comprehensive framework
for evaluation. We examine the impact of model size on learning efficacy and
forgetting, as well as how the progression and similarity of emerging domains
affect the knowledge transfer within these models. Our findings uncover several
key insights: (i) when the sequence of domains shows semantic similarity,
continual pretraining enables LLMs to better specialize in the current domain
compared to stand-alone fine-tuning, (ii) training across a diverse range of
domains enhances both backward and forward knowledge transfer, and (iii)
smaller models are particularly sensitive to continual pretraining, showing the
most significant rates of both forgetting and learning. We posit that our
research marks a shift towards establishing a more realistic benchmark for
investigating CL in LLMs, and has the potential to play a key role in guiding
the direction of future research in the field.
