2024/04/02 by David Herel, Tomas Mikolov, Tomáš Mikolov +2 · 1 voice
Computer Science · Psychology · #Computer science #Linguistics #Natural Language Processing Techniques #Natural language processing #Philosophy #Psychology #cs.AI #cs.CL
paper · pdf · doi:10.48550/arxiv.2404.02305
openalex publication_date 2024/04/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In various fields of knowledge creation, including science, new ideas often build on pre-existing information. In this work, we explore this concept within the context of language models. Specifically, we explore the potential of self-training models on their own outputs, akin to how humans learn and build on their previous thoughts and actions. While this approach is intuitively appealing, our research reveals its practical limitations. We find that extended self-training of the GPT-2 model leads to a significant degradation in performance, resulting in repetitive and collapsed token output.