Files changed (29) hide show
  1. README.md +707 -1066
  2. dataset_da_huse_2x3_5rh/val-00000-of-00001.parquet +0 -3
  3. dataset_da_huse_4x5_5rh/val-00000-of-00001.parquet +0 -3
  4. dataset_da_smoerrebroed_2x3_5rh/val-00000-of-00001.parquet +0 -3
  5. dataset_da_smoerrebroed_4x5_5rh/val-00000-of-00001.parquet +0 -3
  6. dataset_de_hauser_2x3_5rh/val-00000-of-00001.parquet +0 -3
  7. dataset_de_hauser_4x5_5rh/val-00000-of-00001.parquet +0 -3
  8. dataset_en_houses_2x3_5rh/test-00000-of-00001.parquet +2 -2
  9. dataset_en_houses_2x3_5rh/train-00000-of-00001.parquet +2 -2
  10. dataset_en_houses_2x3_5rh/val-00000-of-00001.parquet +0 -3
  11. dataset_en_houses_4x5_5rh/test-00000-of-00001.parquet +2 -2
  12. dataset_en_houses_4x5_5rh/train-00000-of-00001.parquet +2 -2
  13. dataset_en_houses_4x5_5rh/val-00000-of-00001.parquet +0 -3
  14. dataset_fo_hus_2x3_5rh/val-00000-of-00001.parquet +0 -3
  15. dataset_fo_hus_4x5_5rh/val-00000-of-00001.parquet +0 -3
  16. dataset_is_husum_2x3_5rh/val-00000-of-00001.parquet +0 -3
  17. dataset_is_husum_4x5_5rh/val-00000-of-00001.parquet +0 -3
  18. dataset_nb_hus_2x3_5rh/val-00000-of-00001.parquet +0 -3
  19. dataset_nb_hus_4x5_5rh/val-00000-of-00001.parquet +0 -3
  20. dataset_nl_huizen_2x3_5rh/test-00000-of-00001.parquet +0 -3
  21. dataset_nl_huizen_2x3_5rh/train-00000-of-00001.parquet +0 -3
  22. dataset_nl_huizen_2x3_5rh/val-00000-of-00001.parquet +0 -3
  23. dataset_nl_huizen_4x5_5rh/test-00000-of-00001.parquet +0 -3
  24. dataset_nl_huizen_4x5_5rh/train-00000-of-00001.parquet +0 -3
  25. dataset_nl_huizen_4x5_5rh/val-00000-of-00001.parquet +0 -3
  26. dataset_nn_hus_2x3_5rh/val-00000-of-00001.parquet +0 -3
  27. dataset_nn_hus_4x5_5rh/val-00000-of-00001.parquet +0 -3
  28. dataset_sv_hus_2x3_5rh/val-00000-of-00001.parquet +0 -3
  29. dataset_sv_hus_4x5_5rh/val-00000-of-00001.parquet +0 -3
README.md CHANGED
@@ -1,1066 +1,707 @@
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- ---
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- license: apache-2.0
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- configs:
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- - config_name: dataset_da_huse_2x3_5rh
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- data_files:
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- - split: train
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- path: dataset_da_huse_2x3_5rh/train-*
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- - split: val
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- path: dataset_da_huse_2x3_5rh/val-*
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- - split: test
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- path: dataset_da_huse_2x3_5rh/test-*
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- - config_name: dataset_da_huse_4x5_5rh
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- data_files:
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- - split: train
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- path: dataset_da_huse_4x5_5rh/train-*
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- - split: val
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- path: dataset_da_huse_4x5_5rh/val-*
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- - split: test
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- path: dataset_da_huse_4x5_5rh/test-*
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- - config_name: dataset_da_smoerrebroed_2x3_5rh
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- data_files:
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- - split: train
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- path: dataset_da_smoerrebroed_2x3_5rh/train-*
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- - split: val
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- path: dataset_da_smoerrebroed_2x3_5rh/val-*
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- - split: test
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- path: dataset_da_smoerrebroed_2x3_5rh/test-*
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- - config_name: dataset_da_smoerrebroed_4x5_5rh
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- data_files:
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- - split: train
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- path: dataset_da_smoerrebroed_4x5_5rh/train-*
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- - split: val
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- path: dataset_da_smoerrebroed_4x5_5rh/val-*
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- - split: test
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- path: dataset_da_smoerrebroed_4x5_5rh/test-*
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- - config_name: dataset_de_hauser_2x3_5rh
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- data_files:
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- - split: train
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- path: dataset_de_hauser_2x3_5rh/train-*
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- - split: val
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- path: dataset_de_hauser_2x3_5rh/val-*
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- - split: test
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- - config_name: dataset_de_hauser_4x5_5rh
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- data_files:
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- - split: train
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- path: dataset_de_hauser_4x5_5rh/train-*
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- - split: val
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- path: dataset_de_hauser_4x5_5rh/val-*
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- - split: test
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- path: dataset_de_hauser_4x5_5rh/test-*
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- - config_name: dataset_en_houses_2x3_5rh
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- data_files:
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- path: dataset_en_houses_2x3_5rh/val-*
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- - split: test
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- path: dataset_en_houses_2x3_5rh/test-*
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- - config_name: dataset_en_houses_4x5_5rh
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- data_files:
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- - split: train
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- path: dataset_en_houses_4x5_5rh/train-*
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- - split: val
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- path: dataset_en_houses_4x5_5rh/val-*
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- - split: test
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- path: dataset_en_houses_4x5_5rh/test-*
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- - config_name: dataset_fo_hus_2x3_5rh
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- data_files:
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- - split: train
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- path: dataset_fo_hus_2x3_5rh/train-*
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- - split: val
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- path: dataset_fo_hus_2x3_5rh/val-*
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- - split: test
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- - config_name: dataset_fo_hus_4x5_5rh
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- data_files:
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- - split: train
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- path: dataset_fo_hus_4x5_5rh/train-*
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- - split: val
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- path: dataset_fo_hus_4x5_5rh/val-*
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- - split: test
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- path: dataset_fo_hus_4x5_5rh/test-*
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- - config_name: dataset_is_husum_2x3_5rh
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- data_files:
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- path: dataset_is_husum_2x3_5rh/train-*
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- path: dataset_is_husum_2x3_5rh/val-*
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- - split: test
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- path: dataset_is_husum_2x3_5rh/test-*
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- - config_name: dataset_is_husum_4x5_5rh
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- data_files:
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- - split: train
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- path: dataset_is_husum_4x5_5rh/train-*
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- - split: val
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- path: dataset_is_husum_4x5_5rh/val-*
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- - split: test
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- path: dataset_is_husum_4x5_5rh/test-*
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- data_files:
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- - split: val
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- - config_name: dataset_nb_hus_4x5_5rh
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- data_files:
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- - split: train
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- path: dataset_nb_hus_4x5_5rh/train-*
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- - split: val
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- path: dataset_nb_hus_4x5_5rh/val-*
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- - split: test
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- path: dataset_nb_hus_4x5_5rh/test-*
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- - config_name: dataset_nl_huizen_2x3_5rh
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- data_files:
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- - split: train
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- - config_name: dataset_nl_huizen_4x5_5rh
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- - split: test
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- - config_name: dataset_nn_hus_2x3_5rh
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- data_files:
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- - split: val
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- - split: test
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- data_files:
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- - split: test
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- path: dataset_nn_hus_4x5_5rh/test-*
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- - config_name: dataset_sv_hus_2x3_5rh
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- data_files:
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- - config_name: dataset_sv_hus_4x5_5rh
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- data_files:
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- task_categories:
886
- - text-generation
887
- language:
888
- - da
889
- - en
890
- - nl
891
- - de
892
- - fo
893
- - is
894
- - nn
895
- - sv
896
- - nb
897
- pretty_name: MultiZebraLogic
898
- size_categories:
899
- - 10K<n<100K
900
- ---
901
-
902
- # Dataset Card for the MultiZebraLogic dataset
903
-
904
- This dataset includes zebra puzzles in multiple European languages and in two sizes: 2x3 and 4x5. It can be used for evaluating logical reasoning ability.
905
-
906
- The data has been generated using the code in [this repo](https://github.com/alexandrainst/zebra_puzzles).
907
-
908
- ## Dataset Details
909
-
910
- ### Dataset Description
911
-
912
- Zebra puzzles are a type of constraint satisfaction problem. They describe a number of objects, N_objects, that each have attributes, N_attributes.
913
- The goal is to couple the objects with the correct attributes, given some clues. Each solution can be described as a N_objects x N_attributes matrix.
914
-
915
- To increase difficulty, we include "red herrings" which follow the same structure as true clues, but contain no relevant information. We use 5 red herrings per puzzle.
916
-
917
- Most dataset folders contain puzzles with the "houses" theme, where the objects are houses and each attribute describes an inhabitant.
918
- Attributes are randomly selected categories such as nationalities and jobs.
919
- This is included in Danish, Dutch (draft version), English, Faroese, German, Icelandic, Norwegian Bokmål, Norwegian Nynorsk and Swedish.
920
-
921
- We are currently testing changes to template phrasing in the Danish version, so these may differ slightly from the rest.
922
-
923
- We also include data with the "smøerrebrød" theme, where the objects are smørrebrød (open sandwiches) and each attribute is an ingredient.
924
- Categories are ingredient types such as bread or garnish. This theme is only included in Danish.
925
-
926
- The dataset includes puzzles, solutions, lists of included clue types and indices of red herring clues.
927
-
928
- The training sets contain 128 puzzles each which are meant as examples for practise. The test sets contain 1024 puzzles each.
929
-
930
-
931
- - **Created by:** Sofie Helene Bruun (sofie.bruun@alexandra.dk) and Dan Saattrup Smart (dan.smart@alexandra.dk) from the Alexandra Institute.
932
- - **Funded by:** The EU Horizon project TrustLLM (grant agreement number 101135671) and [Danish Foundation Models](https://www.foundationmodels.dk/)
933
- - **Language(s) (NLP):** Danish (da), Dutch (nl), English (en), Faroese (fo), German (de), Icelandic (is), Norwegian Bokmål (nb), Norwegian Nynorsk (nn) and Swedish (sv).
934
- - **License:** apache-2.0
935
-
936
- ### Dataset Sources
937
-
938
- - **Repository:** https://github.com/alexandrainst/zebra_puzzles
939
- - **Paper:** https://arxiv.org/abs/2511.03553
940
-
941
- ## Uses
942
-
943
- <!-- Address questions around how the dataset is intended to be used. -->
944
-
945
- Logical reasoning ability can be evaluated by comparing reponses to puzzles to the true solutions. For examples of how this can be done, see the associated repository.
946
-
947
- The dataset contains examples of the suggested JSON format of reponses for evaluation of LLM's.
948
-
949
- Part of the dataset is intended for use in [EuroEval](https://github.com/EuroEval/EuroEval).
950
-
951
- ### Direct Use
952
-
953
- <!-- This section describes suitable use cases for the dataset. -->
954
-
955
- Each puzzle can be combined from the columns: introduction, clues and question.
956
-
957
- Evaluation can be performed by comparing a response to the solution column.
958
-
959
- When evaluating LLM's, consider including the format_instructions and format_example columns in each puzzle, so it is clear how the intended response should be formatted.
960
-
961
- To create puzzles without red herrings, remove the clues with indices defined in the red_herrings column.
962
-
963
- To analyse the effect of different clue types and red herrings, the clue_types and red_herrings columns can be compared to model performance.
964
-
965
- ### Out-of-Scope Use
966
-
967
- <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
968
-
969
- The clue_types and red_herrings columns should not be included during the solving process, as they will reduce the need for understanding the natural language prompt.
970
- Of course, the solution column should also not be included.
971
-
972
- ## Dataset Structure
973
-
974
- <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
975
-
976
- Each puzzle is generated randomly and independently of other puzzles.
977
-
978
- The columns are:
979
-
980
- - *introduction* (str): Defines the overall rules and introduces the attributes.
981
- - *clues* (list[str]): Clues relating the attributes and objects. 5 red herrings are included per puzzle.
982
- - *question* (str): The question to answer by a solution.
983
- - *format_instructions* (str): Instructions on how to respond in JSON format. This is relevant for LLM's.
984
- - *format_example* (str): An example of the reponse format with the included attribute categories from the puzzle (but not the exact attributes).
985
- - *solution* (dict[str,list[str]]): The solution matrix in JSON format.
986
- - *clue_types* (list[str]): The list of clue types matching the clues column.
987
- - *red_herrings* (list[int]): A list of indices to the red herring clues.
988
-
989
- ## Dataset Creation
990
-
991
- ### Creation Rationale
992
-
993
- <!-- Motivation for the creation of this dataset. -->
994
-
995
- The motivation is creating a multilingual benchmark for logical reasoning. The data allows us to compare logical reasoning ability of LLM's and compare scores across languages.
996
-
997
- Most of the dataset follows the traditional house theme, which is easy to translate.
998
- The smørrebrød theme is included to make it possible to compare the house theme to puzzles matching a European culture tied to a specific language.
999
-
1000
-
1001
- ### Source Data
1002
-
1003
- <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
1004
-
1005
- The data is created from words and phrases defined in the zebra puzzle repository.
1006
-
1007
- #### Data Collection and Processing
1008
-
1009
- <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
1010
-
1011
- The included words and phrases have been drafted by the author with the help of Google Translate, GPT-4.1 in Github Copilot, dictionaries and Wikipedia.
1012
- Relevant code and a few puzzles have been reviewed by native/fluent speakers of each included language (except in Dutch).
1013
- More details are included in the [paper](https://arxiv.org/abs/2511.03553).
1014
-
1015
- #### Who are the source data producers?
1016
-
1017
- <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
1018
-
1019
- Sofie Helene Bruun from the Alexandra Institute with help from other people involved in LLM evaluation across Europe.
1020
-
1021
-
1022
- #### Personal and Sensitive Information
1023
-
1024
- <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
1025
-
1026
- No personal or sensitive information is included.
1027
-
1028
- ## Bias, Risks, and Limitations
1029
-
1030
- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
1031
-
1032
- Not every combination of words in the dataset has been read by a native speaker of each language, so there is a risk that an included combination sounds unnatural or creates an unintended meaning.
1033
-
1034
- Attributes are combined randomly and might accidentally match stereotypes or traits of real people.
1035
-
1036
- The randomly generated smørrebrød are typically not representative of traditional Danish cuisine, although many of the ingredients are.
1037
-
1038
- ### Recommendations
1039
-
1040
- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
1041
-
1042
- Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
1043
-
1044
- ## Citation
1045
-
1046
- <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
1047
-
1048
- If you use this dataset in your research, please cite our paper:
1049
-
1050
- **BibTeX:**
1051
-
1052
- @misc{bruun2025multizebralogicmultilinguallogicalreasoning,
1053
- title={MultiZebraLogic: A Multilingual Logical Reasoning Benchmark},
1054
- author={Sofie Helene Bruun and Dan Saattrup Smart},
1055
- year={2025},
1056
- eprint={2511.03553},
1057
- archivePrefix={arXiv},
1058
- primaryClass={cs.CL},
1059
- url={https://arxiv.org/abs/2511.03553},
1060
- }
1061
-
1062
-
1063
- ## Dataset Card Contact
1064
-
1065
- sofie.bruun@alexandra.dk
1066
- dan.smart@alexandra.dk
 
1
+ ---
2
+ license: apache-2.0
3
+ configs:
4
+ - config_name: dataset_da_huse_2x3_5rh
5
+ data_files:
6
+ - split: train
7
+ path: dataset_da_huse_2x3_5rh/train-*
8
+ - split: test
9
+ path: dataset_da_huse_2x3_5rh/test-*
10
+ - config_name: dataset_da_huse_4x5_5rh
11
+ data_files:
12
+ - split: train
13
+ path: dataset_da_huse_4x5_5rh/train-*
14
+ - split: test
15
+ path: dataset_da_huse_4x5_5rh/test-*
16
+ - config_name: dataset_da_smoerrebroed_2x3_5rh
17
+ data_files:
18
+ - split: train
19
+ path: dataset_da_smoerrebroed_2x3_5rh/train-*
20
+ - split: test
21
+ path: dataset_da_smoerrebroed_2x3_5rh/test-*
22
+ - config_name: dataset_da_smoerrebroed_4x5_5rh
23
+ data_files:
24
+ - split: train
25
+ path: dataset_da_smoerrebroed_4x5_5rh/train-*
26
+ - split: test
27
+ path: dataset_da_smoerrebroed_4x5_5rh/test-*
28
+ - config_name: dataset_de_hauser_2x3_5rh
29
+ data_files:
30
+ - split: train
31
+ path: dataset_de_hauser_2x3_5rh/train-*
32
+ - split: test
33
+ path: dataset_de_hauser_2x3_5rh/test-*
34
+ - config_name: dataset_de_hauser_4x5_5rh
35
+ data_files:
36
+ - split: train
37
+ path: dataset_de_hauser_4x5_5rh/train-*
38
+ - split: test
39
+ path: dataset_de_hauser_4x5_5rh/test-*
40
+ - config_name: dataset_en_houses_2x3_5rh
41
+ data_files:
42
+ - split: train
43
+ path: dataset_en_houses_2x3_5rh/train-*
44
+ - split: test
45
+ path: dataset_en_houses_2x3_5rh/test-*
46
+ - config_name: dataset_en_houses_4x5_5rh
47
+ data_files:
48
+ - split: train
49
+ path: dataset_en_houses_4x5_5rh/train-*
50
+ - split: test
51
+ path: dataset_en_houses_4x5_5rh/test-*
52
+ - config_name: dataset_fo_hus_2x3_5rh
53
+ data_files:
54
+ - split: train
55
+ path: dataset_fo_hus_2x3_5rh/train-*
56
+ - split: test
57
+ path: dataset_fo_hus_2x3_5rh/test-*
58
+ - config_name: dataset_fo_hus_4x5_5rh
59
+ data_files:
60
+ - split: train
61
+ path: dataset_fo_hus_4x5_5rh/train-*
62
+ - split: test
63
+ path: dataset_fo_hus_4x5_5rh/test-*
64
+ - config_name: dataset_is_husum_2x3_5rh
65
+ data_files:
66
+ - split: train
67
+ path: dataset_is_husum_2x3_5rh/train-*
68
+ - split: test
69
+ path: dataset_is_husum_2x3_5rh/test-*
70
+ - config_name: dataset_is_husum_4x5_5rh
71
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72
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73
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75
+ path: dataset_is_husum_4x5_5rh/test-*
76
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77
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78
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79
+ path: dataset_nb_hus_2x3_5rh/train-*
80
+ - split: test
81
+ path: dataset_nb_hus_2x3_5rh/test-*
82
+ - config_name: dataset_nb_hus_4x5_5rh
83
+ data_files:
84
+ - split: train
85
+ path: dataset_nb_hus_4x5_5rh/train-*
86
+ - split: test
87
+ path: dataset_nb_hus_4x5_5rh/test-*
88
+ - config_name: dataset_nn_hus_2x3_5rh
89
+ data_files:
90
+ - split: train
91
+ path: dataset_nn_hus_2x3_5rh/train-*
92
+ - split: test
93
+ path: dataset_nn_hus_2x3_5rh/test-*
94
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95
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96
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97
+ path: dataset_nn_hus_4x5_5rh/train-*
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+ - split: test
99
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