Researchers have found that using a specific type of quantization called bitsandbytes can amplify proactive interference in large language models. Proactive interference occurs when the model's ability to recall previously overwritten information degrades over time. The study tested three precision levels of quantization (FP16, INT8, and INT4) on three different instruction-tuned models. The results showed that INT4 quantization significantly reduced accuracy under high interference in every model, while INT8 also carried a smaller penalty in two out of the three models. This effect is specific to semantically similar distractors and reverses sign when using numeric control conditions. --- Why it matters: This study matters because it highlights the potential consequences of using bitsandbytes quantization on large language models' performance, particularly in applications that rely on long, updatable contexts. Understanding these effects can inform decisions about model deployment and optimization. Source: https://arxiv.org/abs/2608.18578