Summary of Olmes: a Standard For Language Model Evaluations, by Yuling Gu et al.
OLMES: A Standard for Language Model Evaluations
by Yuling Gu, Oyvind Tafjord, Bailey Kuehl, Dany Haddad, Jesse Dodge, Hannaneh Hajishirzi
First submitted to arxiv on: 12 Jun 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 proposed Open Standard for Reproducible Language Model Evaluations (OLMES) aims to address the lack of a common standard setup in evaluating language models. The current evaluation practices vary widely, leading to non-reproducible claims about model performance. OLMES identifies and reviews these factors, including prompt formatting, in-context examples, probability normalizations, and task formulation, providing recommendations guided by existing literature and new experiments. This open standard enables meaningful comparisons between smaller and larger models, resolving open questions and promoting reproducibility in the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Evaluating language models can be tricky because different models are tested on the same tasks but in different ways. This makes it hard to compare which model is best. The proposed OLMES aims to solve this problem by creating a standard way of testing language models. It looks at what factors make evaluations different, like how prompts are written and what examples are used. By following these guidelines, researchers can compare their models more easily and get a better idea of which one performs the best. |
Keywords
» Artificial intelligence » Language model » Probability » Prompt
