Self-Supervised Learning's Vision Echoes in Large Language Models

Zurich, Switzerland – A recent observation by prominent AI researcher Lucas Beyer highlights a fascinating convergence between historical computer vision techniques and emerging practices in large language model (LLM) development. Beyer, known for his foundational work in self-supervised learning, noted that a proposed auxiliary task for LLMs—predicting the order of token chunks—bears a striking resemblance to self-supervised methods prevalent in computer vision around 2018.

In a recent social media post, Lucas Beyer, a distinguished researcher, stated, "An aux task for LLM to predict the order of next chunk of tokens. Haha this is so 2018!" He drew parallels to computer vision tasks such as "jigsaw," "relative patch location," and "rotation prediction." These methods, which involve artificially manipulating images and training models to reverse the changes, were cornerstones of self-supervised learning in vision, enabling models to learn robust representations without extensive manual labeling.

Beyer reflected on his own contributions to this field, referencing his first paper with collaborators Alexey Kolesnikov and Xiaohua Zhai, titled "Revisiting Self-Supervised Visual Representation Learning." Published in 2019, this work critically examined the "messiness" of various auxiliary tasks while simultaneously achieving state-of-the-art results by leveraging them. The paper underscored the effectiveness of these pretext tasks in boosting visual representation learning.

The resurgence of such "pretext tasks" in the realm of large language models signals a broader trend of cross-pollination between different AI domains. Researchers are increasingly finding that principles and architectures proven effective in one modality, like computer vision, can offer valuable insights and solutions when adapted to others, such as natural language processing. This interdisciplinary exchange is seen as a positive development, fostering innovation and accelerating progress in AI research. Beyer concluded his observation by expressing enthusiasm for this trend, stating, "It's interesting because i see more and more previous vision things becoming relevant to language modeling lately, i love it!"