The recent advancements in large language models (LLMs) have primarily focused on mitigating catastrophic forgetting and enhancing knowledge retention. Researchers are exploring innovative methods to integrate new knowledge into LLMs without compromising the retention of previously acquired information. Techniques such as Low-Rank Adaptation (LoRA), joint post-training frameworks, and orthogonal subspace sequential learning are being employed to balance the acquisition of new knowledge with the preservation of old. Additionally, there is a growing emphasis on understanding and addressing the limitations of pre-trained models, particularly in handling rare or infrequent entities. The field is moving towards more sophisticated metrics and methods to measure and mitigate forgetting during both pre-training and fine-tuning stages. These developments aim to create more robust and versatile LLMs capable of handling a broader range of tasks and entities effectively.

Noteworthy papers include one that introduces a joint post-training framework, outperforming sequential methods with similar computational cost, and another that explores low-cost methods to mitigate forgetting during pre-training, offering new insights into the dynamics of forgetting.