Mitigation of Zero-Shot Semantic Similarity Degradation in Out-of-Distribution Text via RWKV State-Space Formulations

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

To what extent does RWKV's state-space formulation mitigate performance degradation in zero-shot semantic similarity tasks when evaluated on out-of-distribution textual pairs compared to standard attention models?

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