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  1. README.md +124 -3
  2. mmred.png +3 -0
  3. seq_len_1/.ipynb_checkpoints/dataset_dict-checkpoint.json +1 -0
  4. seq_len_1/dataset_dict.json +1 -0
  5. seq_len_1/test/data-00000-of-00001.arrow +3 -0
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  7. seq_len_1/test/state.json +13 -0
  8. seq_len_1/train/data-00000-of-00001.arrow +3 -0
  9. seq_len_1/train/dataset_info.json +32 -0
  10. seq_len_1/train/state.json +13 -0
  11. seq_len_1/val/data-00000-of-00001.arrow +3 -0
  12. seq_len_1/val/dataset_info.json +32 -0
  13. seq_len_1/val/state.json +13 -0
  14. seq_len_128/dataset_dict.json +1 -0
  15. seq_len_128/test/data-00000-of-00001.arrow +3 -0
  16. seq_len_128/test/dataset_info.json +32 -0
  17. seq_len_128/test/state.json +13 -0
  18. seq_len_16/dataset_dict.json +1 -0
  19. seq_len_16/test/data-00000-of-00001.arrow +3 -0
  20. seq_len_16/test/dataset_info.json +32 -0
  21. seq_len_16/test/state.json +13 -0
  22. seq_len_16/train/data-00000-of-00001.arrow +3 -0
  23. seq_len_16/train/dataset_info.json +32 -0
  24. seq_len_16/train/state.json +13 -0
  25. seq_len_16/val/data-00000-of-00001.arrow +3 -0
  26. seq_len_16/val/dataset_info.json +32 -0
  27. seq_len_16/val/state.json +13 -0
  28. seq_len_2/dataset_dict.json +1 -0
  29. seq_len_2/test/data-00000-of-00001.arrow +3 -0
  30. seq_len_2/test/dataset_info.json +32 -0
  31. seq_len_2/test/state.json +13 -0
  32. seq_len_2/train/data-00000-of-00001.arrow +3 -0
  33. seq_len_2/train/dataset_info.json +32 -0
  34. seq_len_2/train/state.json +13 -0
  35. seq_len_2/val/data-00000-of-00001.arrow +3 -0
  36. seq_len_2/val/dataset_info.json +32 -0
  37. seq_len_2/val/state.json +13 -0
  38. seq_len_32/dataset_dict.json +1 -0
  39. seq_len_32/test/data-00000-of-00001.arrow +3 -0
  40. seq_len_32/test/dataset_info.json +32 -0
  41. seq_len_32/test/state.json +13 -0
  42. seq_len_4/dataset_dict.json +1 -0
  43. seq_len_4/test/data-00000-of-00001.arrow +3 -0
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  50. seq_len_4/val/dataset_info.json +32 -0
README.md CHANGED
@@ -1,3 +1,124 @@
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- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ configs:
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+ - config_name: default
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+ data_files:
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+ - split: train
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+ path: '*/train/*.arrow'
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+ - split: val
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+ path: '*/val/*.arrow'
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+ - split: test
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+ path: '*/test/*.arrow'
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+ - config_name: seq_len_1
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+ data_files:
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+ - split: train
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+ path: seq_len_1/train/*.arrow
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+ - split: val
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+ path: seq_len_1/val/*.arrow
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+ - split: test
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+ path: seq_len_1/test/*.arrow
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+ - config_name: seq_len_2
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+ data_files:
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+ - split: train
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+ path: seq_len_2/train/*.arrow
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+ - split: val
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+ path: seq_len_2/val/*.arrow
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+ - split: test
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+ path: seq_len_2/test/*.arrow
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+ - config_name: seq_len_4
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+ data_files:
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+ - split: train
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+ path: seq_len_4/train/*.arrow
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+ - split: val
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+ path: seq_len_4/val/*.arrow
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+ - split: test
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+ path: seq_len_4/test/*.arrow
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+ - config_name: seq_len_8
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+ data_files:
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+ - split: train
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+ path: seq_len_8/train/*.arrow
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+ - split: val
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+ path: seq_len_8/val/*.arrow
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+ - split: test
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+ path: seq_len_8/test/*.arrow
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+ - config_name: seq_len_16
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+ data_files:
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+ - split: train
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+ path: seq_len_16/train/*.arrow
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+ - split: val
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+ path: seq_len_16/val/*.arrow
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+ - split: test
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+ path: seq_len_16/test/*.arrow
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+ - config_name: seq_len_32
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+ data_files:
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+ - split: test
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+ path: seq_len_32/test/*.arrow
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+ - config_name: seq_len_64
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+ data_files:
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+ - split: test
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+ path: seq_len_64/test/*.arrow
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+ - config_name: seq_len_128
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+ data_files:
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+ - split: test
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+ path: seq_len_128/test/*.arrow
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+ ---
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+
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+ # MMReD: A Cross-Modal Benchmark for Dense Reasoning
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+
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+ <center>
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+ <img src="mmred.png" alt="Overview of MMReD Benchmark" width=1920>
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+ </center>
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+
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+ This is the dataset & benchmark accompanying MMReD paper. It was obtained by running generation script in [MMReD repository](https://anonymous.4open.science/r/mmred-125A):
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+
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+ ```bash
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+ python scripts/generate_dataset.py
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+ ```
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+
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+ It only contains textual split of the dataset to save space as images can be deterministically generated from jsons.
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+
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+ To run full evaluation, training, or generate images from jsons for LVLMs, please refer to the repository.
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+
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+ ## Multi-Modal Controllable Environment = Dense Factual Haystack
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+
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+ **MMReD** introduces a concept of dense context, aiming at controllable generative evaluation of reasoning over arbitrarily long factually dense scenarios.
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+
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+ It contains 8 splits corresponding to different sequence lengths in environment transitions: [1, 2, 4, ..., 128]. Each split contains 24 evaluation questions with 200/50/50 train/val/test samples per question type.
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+
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+ ```python
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+ from datasets import load_dataset
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+ train_16 = load_dataset("dondo-sss/mmred", "seq_len_128")["train"]
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+ test_128 = load_dataset("dondo-sss/mmred", "seq_len_128")["test"]
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+ ```
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+
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+ Question are generally divided into 2 groups - resembling standard NIAH evaluation & our introduced Dense Long Context evaluation:
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+
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+ Different splits grouped by sequence length are available both form evaluation & training, with training samples generated up to sequence length of **16**.
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+
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+ | ID | Question template | Dataset Name |
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+ |--------------|------------------------------------------------------------------------------------|----------------------------------|
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+ |**NIAH**|
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+ | FA-FA-R | In which room did [C] first appear? | room_on_char_first_app |
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+ | FA-CCFA-R | In which room was [C1] when [C2] first appeared in the [R]? | char_on_char_first_app |
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+ | FA-FR-C | Who was the first to appear in the [R]? | first_at_room |
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+ | FA-RCFA-C | Who was in the [R1] when [C] first appeared in the [R2]? | char_at_frame |
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+ | FA-NRFA-I | How many characters were in the [R1] when [C] first appeared in the [R2]? | n_room_on_char_first_app |
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+ | FI-FA-R | In which room was [C] at the final step? | final_app |
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+ | FI-CCFA-R | In which room was [C1] when [C2] made their final appearance in the [R]? | char_on_char_final_app |
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+ | FI-LR-C | Who was the last to appear in the [R]? | last_at_room |
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+ | FI-RCFA-C | Who was in the [R1] when [C] made their final appearance in the [R2]? | char_at_frame |
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+ | FI-NRFA-I | How many chars were in the [R1] when [C] made their final app in the [R2]? | n_room_on_char_final_app |
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+ | FX-CF-R | In which room was [C] at step [X]? | room_at_frame |
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+ | FX-RF-C | Who was in the [R] at step [X]? | char_at_frame |
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+ | FX-CCF-C | Who was in the same room as [C] at step [X]? | char_on_char_at_frame |
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+ | FX-NCF-I | How many other characters were in the same room as [C] at step [X]? | n_char_at_frame |
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+ | FX-NE-I | How many rooms were empty at step [X]? | n_empty |
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+ |**LC**|
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+ | LC-RE-R | Which room was empty for the [comp] steps? | room_empty |
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+ | LC-WS-R | In which room did [C] spend the [comp] time? | where_spend |
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+ | LC-CR-R | Which room was crowded (three or more people) for the most steps? | crowded_room |
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+ | LC-WHS-C | Who spent the [comp] time in the [R]? | who_spend |
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+ | LC-SA-C | Who spent the [comp] time alone in the rooms? | spend_alone |
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+ | LC-ST-C | With whom did [C] spend the [comp] time together in the same room? | spend_together |
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+ | LC-SR-I | How many steps did [C] spend in the [R]? | steps_in_room |
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+ | LC-RV-I | How many different rooms did [C] visit? | rooms_visited |
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+ | LC-CC-I | How many times did a crowd (three or more people in one room) appear? | crowd_count |
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