Summary of Large-scale Cloze Evaluation Reveals That Token Prediction Tasks Are Neither Lexically Nor Semantically Aligned, by Cassandra L. Jacobs et al.
Large-scale cloze evaluation reveals that token prediction tasks are neither lexically nor semantically aligned
by Cassandra L. Jacobs, Loïc Grobol, Alvin Tsang
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| 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 paper compares language models’ generative behavior at the next token prediction level to human productions in the cloze task. The results show that larger models trained for longer typically perform better, but they also consistently underestimate human responses, overestimate rare ones, and produce distinct semantic spaces. This work highlights the limitations of using language model generations as replacements or models of the cloze task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper compares how well computer programs can generate text to what humans write. It finds that bigger computers trained for longer do a better job, but they also tend to be too sure about rare responses and not confident enough about good ones. This makes them not very good at predicting what humans would say. The study shows that we shouldn’t use these computer-generated texts as if they were real human writing. |
Keywords
» Artificial intelligence » Language model » Token
