Comparative Analysis of RWKV Intermediate Layer Aggregation Overhead and Final-Layer Inference Latency in Semantic Textual

Abstract

Abstract: This paper investigates the efficacy of RWKV, a novel language model architecture known for its linear attention mechanism, for generating sentence embeddings in a zero-shot setting. I conduct a layer-wise analysis to evaluate the semantic similarity captured by embeddings from different hidden layers of a pre-trained RWKV model. The performance is assessed on the Microsoft Research Paraphrase Corpus (MRPC) dataset using Spearman correlation and compared against a GloVe-based baseline. My results indicate that while RWKV embeddings capture some semantic relatedness, they underperform compared

Research Question

How does the computational overhead of aggregating intermediate RWKV layers compare to the latency of standard final-layer inference during semantic textual similarity tasks?

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