Abstract
Much of the recent discourse within the ML community has been centered around Large Language Models (LLMs), their functionality and potential – yet not only do we not have a working definition of LLMs, but much of this discourse relies on claims and assumptions that are worth re-examining. We contribute a definition of LLMs, critically examine five common claims regarding their properties (including ’emergent properties’), and conclude with suggestions for future research directions and their framing.
| Original language | English |
|---|---|
| Conference proceedings | Proceedings of the 41st International Conference on Machine Learning |
| Volume | 235 |
| Pages (from-to) | 42647-42665 |
| Publication status | Published - 2024 |
| Event | International Conference on Machine Learning - Wien Exhibition Congress Center, Vienna, Austria Duration: 21 Jul 2024 → 27 Jul 2024 https://icml.cc/Conferences/2024 |
Conference
| Conference | International Conference on Machine Learning |
|---|---|
| Location | Wien Exhibition Congress Center |
| Country/Territory | Austria |
| City | Vienna |
| Period | 21/07/2024 → 27/07/2024 |
| Internet address |
Keywords
- NLP
- language models
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