natural-adv-examples
promptbench
| natural-adv-examples | promptbench | |
|---|---|---|
| 1 | 4 | |
| 572 | 2,079 | |
| - | 7.9% | |
| 0.0 | 9.2 | |
| about 2 months ago | 6 days ago | |
| Python | Python | |
| MIT License | MIT License |
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natural-adv-examples
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Tracking mentions began in Dec 2020.
promptbench
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Show HN: Times faster LLM evaluation with Bayesian optimization
Fair question.
Evaluate refers to the phase after training to check if the training is good.
Usually the flow goes training -> evaluation -> deployment (what you called inference). This project is aimed for evaluation. Evaluation can be slow (might even be slower than training if you're finetuning on a small domain specific subset)!
So there are [quite](https://github.com/microsoft/promptbench) [a](https://github.com/confident-ai/deepeval) [few](https://github.com/openai/evals) [frameworks](https://github.com/EleutherAI/lm-evaluation-harness) working on evaluation, however, all of them are quite slow, because LLM are slow if you don't have infinite money. [This](https://github.com/open-compass/opencompass) one tries to speed up by parallelizing on multiple computers, but none of them takes advantage of the fact that many evaluation queries might be similar and all try to evaluate on all given queries. And that's where this project might come in handy.
- FLaNK Weekly 31 December 2023
- FLaNK 25 December 2023
- Promptbench: A Unified Library for Evaluating and Understanding LLMs
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