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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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# MMReD: A Cross-Modal Benchmark for Dense Reasoning |
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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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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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```bash |
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python scripts/generate_dataset.py |
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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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To run full evaluation, training, or generate images from jsons for LVLMs, please refer to the repository. |
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## Multi-Modal Controllable Environment = Dense Factual Haystack |
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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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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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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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```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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Questions are generally divided into 2 groups - resembling standard NIAH evaluation & our introduced Dense Long Context evaluation: |
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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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