Large language models (LLMs) are impressive, but they sometimes hallucinate, presenting inaccurate or outdated information. This is especially problematic when the information they've memorized clashes with the context they're given. Imagine an LLM confidently stating that Pluto is still a planet, despite being provided with up-to-date astronomical data. This 'knowledge conflict' can lead to incorrect answers and erode trust in AI. Researchers are exploring ways to resolve these conflicts, and a new technique called SPARE is showing promise. SPARE, which stands for Sparse Auto-Encoder-based Representation Engineering, acts like a knowledge traffic controller within the LLM. It leverages 'sparse auto-encoders' to dissect the LLM's inner workings and identify the specific features responsible for choosing between memorized and contextual knowledge. Think of it as pinpointing the exact neurons that make the LLM favor its internal Pluto fact over the new evidence. Once these features are isolated, SPARE can subtly adjust the LLM's internal activations, nudging it toward the correct knowledge source. In tests on open-domain question-answering tasks, SPARE significantly outperformed other methods for resolving knowledge conflicts. It proved more effective than directly editing the LLM's internal states and even surpassed techniques like contrastive decoding. Notably, SPARE achieved this without retraining the model, offering an efficient and practical way to enhance LLM accuracy. While exciting, SPARE does have limitations. It currently relies on pre-trained sparse auto-encoders, which aren't available for all LLMs. Further research will explore how to adapt SPARE to various models and task types, ultimately aiming to create LLMs that can dynamically evaluate and select the most reliable information, leading to more trustworthy and robust AI systems.