"Adaptive Contextual Caching for Mobile Edge Large Language Model Service"

Contextual caching with deep reinforcement learning boosts LLM performance at the edge.

Adaptive Contextual Caching (ACC) enhances mobile-edge Large Language Model (LLM) retrieval by proactively storing relevant data. This reduces latency and improves resource use in Retrieval-Augmented Generation (RAG).

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https://arxiv.org/abs/2501.09383

Original Problem 🤔:

→ Mobile-edge LLMs face latency and resource limitations due to limited computational power and bandwidth.

→ Traditional caching methods like Least Recently Used (LRU) and First In, First Out (FIFO) are inefficient in dynamic environments.

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Solution in this Paper 💡:

→ Adaptive Contextual Caching (ACC) anticipates user needs by proactively caching relevant data for LLMs.

→ ACC uses a deep reinforcement learning module to refine cache replacement policies.

→ This approach considers user context, document similarity, and overhead from cache misses.

→ It dynamically adapts to different knowledge domains and update frequencies.

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Key Insights from this Paper 💎:

→ Proactive caching with reinforcement learning significantly improves cache hit rates and reduces latency.

→ Contextual analysis is crucial for efficient caching in dynamic environments.

→ Adaptive caching policies are more effective than traditional methods in LLM applications.

→ The dynamic cache update mechanism reduces retrieval latency by up to 40% while maintaining accuracy and cost efficiency.

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Results 💯:

→ Cache hit rates increase to over 80% after 11 training episodes.

→ Retrieval latency reduces by up to 40%.

→ Local caching overhead reduces by up to 55%.