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Sergey Pletenev et al. explore the integration of new knowledge into Large Language Models (LLMs) using Low-Rank Adaptation (LoRA). The study focuses on fine-tuning the Llama-3.1-8B-instruct model with varying amounts of new information while aiming to retain previously learned knowledge. The researchers found that mixing known and new facts in training data yields the best results but also noted potential drawbacks, such as a decline in performance on external benchmarks and a bias towards overrepresented answers when the data is skewed. Additionally, the model sometimes becomes overly confident and hesitant to answer. These findings emphasize the need for careful consideration of training data composition and tuning parameters to balance the incorporation of new knowledge with maintaining overall model capabilities.
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