Transferring Linear Features Across Language Models With Model Stitching

A recent study published on arXiv introduces an innovative technique called model stitching, which enables the transfer of linear features between different language models. By employing affine mappings, researchers demonstrated that both small and large language models can learn similar representation spaces. This finding suggests that despite differences in model size, underlying representations may be aligned through linear transformations. The approach holds promise for improving model training efficiency by facilitating feature reuse across models. Observations from the study support the effectiveness of model stitching in bridging representation gaps. This development contributes to ongoing efforts in the AI community to optimize language model training and interoperability. Further research may explore practical applications and scalability of this technique.