base_model: mlabonne/NeuralMarcoro14-7B
license: apache-2.0
tags:
- mlabonne/NeuralMarcoro14-7B
- dpo
- 7B
- winograd
- mmlu_abstract_algebra
- mistral
datasets:
- hromi/winograd_dpo_basic
udkai_Turdus
A less contaminated version of udkai/Garrulus and the second model to be discussed in the paper Subtle DPO-Contamination with modified Winogrande increases TruthfulQA, Hellaswag & ARC.
Contrary to Garrulus which was obtained after 2 epochs, this model was obtained after one single epoch of "direct preference optimization" of NeuralMarcoro14-7B with [https://huggingface.co/datasets/hromi/winograd_dpo] .
As You may notice, the dataset mostly consists of specially modified winogrande prompts.
But before flagging this (or recommending this to be flagged), consider this:
Subtle DPO-Contamination with modified Winogrande in such an extent that the average accuracy of all 5-non Winogrande metrics (e.g. including also MMLU and GSM8K) is 0.2% higher than the base model.
| Model | ARC | HellaSwag | MMLU | Truthful QA | GSM8K | Average |
|---|---|---|---|---|---|---|
| mlabonne/NeuralMarcoro14-7B | 71.42 | 87.59 | 64.84 | 65.64 | 70.74 | 72.046 |
| udkai/Turdus | 73.38 | 88.56 | 64.52 | 67.11 | 67.7 | 72,254 |
Yes, as strange as it may sound, one can indeed increase ARC from 71.42% to 73.38 % with one single epoch of cca 1200 repetitive winograd schematas...