← Search

Xingyu Dang

2 accepted papers

2025

RNNs are not Transformers (Yet): The Key Bottleneck on In-Context Retrieval

ICLR 2025poster

This paper investigates the gap in representation powers of Transformers and Recurrent Neural Networks (RNNs), which are more memory efficient than Transformers. We aim to understand whether RNNs can match the performance of Transformers, particularly when enhanced with Chain-of-Thought (CoT) prompt…