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README.md

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+---
+license: cc
+task_categories:
+- text-generation
+language:
+- en
+size_categories:
+- 10K<n<100K
+---
+
+## MCiteBench Dataset
+
+MCiteBench is a benchmark to evaluate multimodal citation text generation in Multimodal Large Language Models (MLLMs). For more details, please refer to our [paper](test).
+
+## Data Download
+
+Please download the `MCiteBench_full_dataset.zip`. It contains the `data.jsonl` file and the `visual_resources` folder.
+
+## Data Format
+The data format for `data_example.jsonl` and `data.jsonl` is as follows:
+
+```yaml
+question_id: [str]             # The ID of the question
+pdf_id: [str]                  # The ID of the associated PDF document
+url: [str]                     # The URL path of the corresponding openreview submission
+question_type: [str]           # The type of question, with possible values: "explanation" or "locating"
+question: [str]                # The text of the question
+answer: [str]                  # The answer to the question, which can be a string, list, float, or integer, depending on the context
+
+evidence_keys: [list]          # A list of abstract references or identifiers for evidence, such as "section x", "line y", "figure z", or "table k".
+                               # These are not the actual content but pointers or descriptions indicating where the evidence can be found.
+                               # Example: ["section 2.1", "line 45", "Figure 3"]
+evidence_contents: [list]      # A list of resolved or actual evidence content corresponding to the `evidence_keys`.
+                               # These can include text excerpts, image file paths, or table file paths that provide the actual evidence for the answer.
+                               # Each item in this list corresponds directly to the same-index item in `evidence_keys`.
+                               # Example: ["This is the content of section 2.1.", "/path/to/figure_3_add_caption.jpg"]
+evidence_modal: [str]          # The modality type of the evidence, with possible values: ['figure', 'table', 'text', 'mixed'] indicating the source type of the evidence
+evidence_count: [int]          # The total count of all evidence related to the question
+distractor_count: [int]        # The total number of distractor items, meaning information blocks that are irrelevant or misleading for the answer
+info_count: [int]              # The total number of information blocks in the document, including text, tables, images, etc.
+text_2_idx: [dict[str, str]]   # A dictionary mapping text information to corresponding indices
+idx_2_text: [dict[str, str]]   # A reverse dictionary mapping indices back to the corresponding text content
+image_2_idx: [dict[str, str]]  # A dictionary mapping image paths to corresponding indices
+idx_2_image: [dict[str, str]]  # A reverse dictionary mapping indices back to image paths
+table_2_idx: [dict[str, str]]  # A dictionary mapping table paths to corresponding indices
+idx_2_table: [dict[str, str]]  # A reverse dictionary mapping indices back to table paths
+meta_data: [dict]              # Additional metadata used during the construction of the data
+distractor_contents: [list]    # Similar to `evidence_contents`, but contains distractors, which are irrelevant or misleading information
+```