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Mitigating Catastrophic Semantic Divergence in Large Language Models

Large Language Models (LLMs) face a significant challenge of catastrophic semantic divergence, which hampers their performance and reliability. This issue arises when LLMs deployed across distributed systems experience semantic drift, leading to inconsistent and inaccurate outputs. The problem affects the efficiency and trustworthiness of LLM applications. It requires a solution to ensure consistent and accurate semantic processing.

Computing & Technology, Computer Science, Machine Learning