Summary of Semiparametric Token-sequence Co-supervision, by Hyunji Lee et al.
Semiparametric Token-Sequence Co-Supervision
by Hyunji Lee, Doyoung Kim, Jihoon Jun, Sejune Joo, Joel Jang, Kyoung-Woon On, Minjoon Seo
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed semiparametric token-sequence co-supervision training method leverages both traditional next token prediction and next sequence prediction losses to train language models. By simultaneously optimizing these two objectives, the approach consistently outperforms single-supervision methods. This co-supervision encourages broader generalization capabilities across the model, particularly by enhancing the stability of nonparametric sequence embeddings through robust parametric token spaces established during pretraining. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to train language models using two types of supervision. It helps them learn more accurately and makes them better at understanding text in different ways. By combining these supervisions, it gets even better results than just one or the other. This is important because it shows how language models can be trained to work well with complex texts. |
Keywords
» Artificial intelligence » Generalization » Pretraining » Token
