Mitigate judge's order bias with swap mode#4
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geoalgo merged 1 commit intoOpenEuroLLM:mainfrom Nov 17, 2025
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Nov 17, 2025
| judge_model=args.judge_model, | ||
| n_instructions=args.n_instructions, | ||
| provide_explanation=args.provide_explanation, | ||
| swap_mode=args.swap_mode, |
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At this point args.swap_mode is a string but we map it to a boolean, we should probably rather test args.swap_mode == "fixed" rather?
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Yes, you are right. This got lost in the change from correct_order_bias (bool) to swap_mode (str).
| for annotation in annotations_reversed | ||
| ] | ||
| ) | ||
| prefs = (prefs + (1 - prefs_reversed)) / 2.0 |
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Does it work in case of nans? Seems like return 2n battles would be cleaner?
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Yes, it does:
>>> pd.Series([1, 2, 3, None]) + pd.Series([1, 2, None, 4])
0 2.0
1 4.0
2 NaN
3 NaN
dtype: float64Nonetheless, I think your suggestion is, generally, cleaner. How about the following?
prefs = pd.concat([prefs, (1 - prefs_reversed)]).reset_index(drop=True)
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I have created #6 |
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