Summary of The Rosetta Paradox: Domain-specific Performance Inversions in Large Language Models, by Basab Jha et al.
The Rosetta Paradox: Domain-Specific Performance Inversions in Large Language Models
by Basab Jha, Ujjwal Puri
First submitted to arxiv on: 9 Dec 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 This abstract presents an intriguing phenomenon, dubbed the Rosetta Paradox, where large language models excel in specialized domains but struggle with general knowledge. The paper formalizes this paradox and proposes a framework to quantify its effects. This includes a Domain Specificity Index (DSI) and Performance Inversion Metric (PIM). Researchers can leverage these metrics to analyze the performance of language models across various domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Rosetta Paradox is an unexpected discovery in the world of artificial intelligence. Normally, you’d expect AI language models like GPT and BERT to be great at many things. But this paradox shows that they are actually really good at specific topics, but not as good when it comes to everyday knowledge. This paper explains what’s going on with these models and gives us a way to measure how well they do in different areas. |
Keywords
» Artificial intelligence » Bert » Gpt
