"The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit"

Combining graph networks and early exits speeds up LLM recommendations significantly.

This paper introduces a dual-speed optimization for LLM recommender systems using GCN retrieval and early exit strategies to balance speed and accuracy.

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https://arxiv.org/abs/2501.02173

🤔 Original Problem:

RAG-enhanced LLM recommenders face two major bottlenecks: slow retrieval times and computational overhead from processing long input sequences. These issues limit real-time applications.

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🔧 Solution in this Paper:

→ The system employs GCN-Retriever to generate user embeddings by analyzing interaction graphs, replacing slower LLM-based retrieval.

→ Multi-head early exit architecture allows model inference to terminate at intermediate layers when confidence thresholds are met.

→ Layer-specific learning rates optimize training, with shallower layers getting higher rates for capturing generic features.

→ A probability-based exit criterion monitors prediction consistency across layers to determine optimal termination points.

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💡 Key Insights:

→ Averaging embeddings from multiple GCN layers provides better user representations than using just the final layer

→ Early exit strategies work best when combined with efficient retrieval mechanisms

→ Layer-specific training improves model stability and convergence

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📊 Results:

→ AUC improvements: BookCrossing (4.72%), Beauty (27.16%), Video Games (16.71%)

→ GCN-retriever achieves 72.51 AUC vs LLM-retriever's 69.05 AUC on BookCrossing

→ System maintains accuracy while improving RPS from 3.83 to 4.57 on Video Games dataset