Datasets:

ReadingTimeMachine
/

visual_qa_histograms

@@ -1,68 +1,70 @@
----
-license: apache-2.0
----
-# What Lies Beneath: A Call for Distribution-based Visual Question & Answer Datasets
-### Publication: TBD (linked on publication)
-### GitHub Repo: TBD (linked on publication)
-This is a histogram-based dataset for visual question and answer (VQA) with humans and large language/multimodal models (LMMs).
-Data contains synthetically generated single-panel histograms images, data used to create histograms, bounding box data for titles, axis and tick labels, and data marks, and VQA question-answer pairs.  The subset of data presented in the paper (`example_hist/` folder) includes both human (two annotators) and LMM (ChatGPT-5-nano) annotations.
-See GitHub link for code used to create and parse the following files.
-## Directory Structure
-Overview of the [directory structure](https://huggingface.co/datasets/ReadingTimeMachine/visual_qa_histograms/tree/main) is as follows:
- - `example_hists/` -- contains img and json for a small (80 images), visually uniform set of histogram data with several questions annotated by both LMMs
- - `example_hists_larger/` -- larger (500 images) dataset of uniform histogram images
- - `example_hists_complex/` -- largest (1000 images) dataset of histograms with a variety of distributions, shapes, colors, etc.
-Paper-dataset (`example_hists/`) [directory structure](https://huggingface.co/datasets/ReadingTimeMachine/visual_qa_histograms/tree/main/example_hists):
- - `LLM_outputs/` -- contains outputs from various trials using ChatGPT-5
- - `imgs/` -- stores all images (also in `imgs.zip` file)
- - `jsons/` -- stores JSON for bounding boxes, data used to create images, VQA data
- - `human_and_llm_annotated_data.csv` -- contains two human annotations and two LMM annotations (gpt-5-nano, gpt-5-mini) for a subset of questions
-## Human and LMM Annotations
-Questions which have annotations in `human_and_llm_annotated_data.csv` are:
-  1. "What is the median value of the data in this figure panel?" and,
-  2. "How many gaussians were used to generate the data for the plot in the figure panel?".  The addition of a constraint in the `format` part of the prompt of "Please choose an integer number from 1 to 5." was used in the LMM prompts to mimic the background knowledge of the human annotators.
-Code for annotations from LMMs can be found in our GitHub repo linked at the top of this page.
-Human annotations were performed with the [Zooniverse](https://www.zooniverse.org/) citizen science platform.  For the number of gaussians, the humans were prompted to enter a number:
-<img src="https://huggingface.co/datasets/ReadingTimeMachine/visual_qa_histograms/resolve/main/docs/ngaussians.png" alt="Zooniverse interface showing histogram image with the prompt 'How many gaussians were used to make the underlying distribution?'">
-For the median, humans are first prompted to input the median as a number:
-<img src="https://huggingface.co/datasets/ReadingTimeMachine/visual_qa_histograms/resolve/main/docs/median_number.png" alt="Zooniverse interface showing histogram image with the prompt 'What is the median of the underlying distribution (as a number)?'">
-The humans were then prompted to draw the median with a line tool:
-<img src="https://huggingface.co/datasets/ReadingTimeMachine/visual_qa_histograms/resolve/main/docs/median_draw.png" alt="Zooniverse interface showing histogram image with the prompt 'What is the median of the underlying distribution (draw a line)?'">
-The human-drawn annotations were found to be more accurate.
-## Question formatting
-Questions are stored in whole and broken into parts based on the structure from the [Google Prompting Guide](https://www.kaggle.com/whitepaper-prompt-engineering):
- * persona: role the LMM is taking (e.g., "You are a helpful assistant that analyzes images.")
- * context: context in which the question is being answered (often blank, but example could be "Please choose from the following list of options...")
- * question: simplest form of question (e.g., "How many lines are on this plot?")
- * format: output format of answer (e.g., "Please format your answer for the median as a json snippet as in '{'nbars':''}' where the number of bars is an integer stored in the empty string in the snippet.")
-Definitions are not rigorous, user should feel welcome to mix and match at will.
-## Citation information
-If you use this work please cite:
-```
-TBD
 ```

+---
+license: apache-2.0
+---
+# What Lies Beneath: A Call for Distribution-based Visual Question & Answer Datasets
+### Publication: TBD (linked on publication)
+### GitHub Repo: [ReadingTimeMachine/LLM_VQA_JCDL2025](https://github.com/ReadingTimeMachine/LLM_VQA_JCDL2025)
+This is a histogram-based dataset for visual question and answer (VQA) with humans and large language/multimodal models (LMMs).
+Data contains synthetically generated single-panel histograms images, data used to create histograms, bounding box data for titles, axis and tick labels, and data marks, and VQA question-answer pairs.  The subset of data presented in the paper (`example_hist/` folder) includes both human (two annotators) and LMM (ChatGPT-5-nano) annotations.
+See GitHub link for code used to create and parse the following files.
+## Directory Structure
+Overview of the [directory structure](https://huggingface.co/datasets/ReadingTimeMachine/visual_qa_histograms/tree/main) is as follows:
+ - `example_hists/` -- contains img and json for a small (80 images), visually uniform set of histogram data with several questions annotated by both LMMs
+ - `example_hists_larger/` -- larger (500 images) dataset of uniform histogram images
+ - `example_hists_complex/` -- largest (1000 images) dataset of histograms with a variety of distributions, shapes, colors, etc.
+Paper-dataset (`example_hists/`) [directory structure](https://huggingface.co/datasets/ReadingTimeMachine/visual_qa_histograms/tree/main/example_hists):
+ - `LLM_outputs/` -- contains outputs from various trials using ChatGPT-5
+ - `imgs/` -- stores all images (also in `imgs.zip` file)
+ - `jsons/` -- stores JSON for bounding boxes, data used to create images, VQA data
+ - `human_and_llm_annotated_data.csv` -- contains two human annotations and two LMM annotations (gpt-5-nano, gpt-5-mini) for a subset of questions
+## Human and LMM Annotations
+Questions which have annotations in `human_and_llm_annotated_data.csv` are:
+  1. "What is the median value of the data in this figure panel?" and,
+  2. "How many gaussians were used to generate the data for the plot in the figure panel?".  The addition of a constraint in the `format` part of the prompt of "Please choose an integer number from 1 to 5." was used in the LMM prompts to mimic the background knowledge of the human annotators.
+Code for annotations from LMMs can be found in our GitHub repo linked at the top of this page.
+Human annotations were performed with the [Zooniverse](https://www.zooniverse.org/) citizen science platform.  For the number of gaussians, the humans were prompted to enter a number:
+<img src="https://huggingface.co/datasets/ReadingTimeMachine/visual_qa_histograms/resolve/main/docs/ngaussians.png" alt="Zooniverse interface showing histogram image with the prompt 'How many gaussians were used to make the underlying distribution?'">
+For the median, humans are first prompted to input the median as a number:
+<img src="https://huggingface.co/datasets/ReadingTimeMachine/visual_qa_histograms/resolve/main/docs/median_number.png" alt="Zooniverse interface showing histogram image with the prompt 'What is the median of the underlying distribution (as a number)?'">
+The humans were then prompted to draw the median with a line tool:
+<img src="https://huggingface.co/datasets/ReadingTimeMachine/visual_qa_histograms/resolve/main/docs/median_draw.png" alt="Zooniverse interface showing histogram image with the prompt 'What is the median of the underlying distribution (draw a line)?'">
+The human-drawn annotations were found to be more accurate.
+## Question formatting
+Questions are stored in whole and broken into parts based on the structure from the [Google Prompting Guide](https://www.kaggle.com/whitepaper-prompt-engineering):
+ * persona: role the LMM is taking (e.g., "You are a helpful assistant that analyzes images.")
+ * context: context in which the question is being answered (often blank, but example could be "Please choose from the following list of options...")
+ * question: simplest form of question (e.g., "How many lines are on this plot?")
+ * format: output format of answer (e.g., "Please format your answer for the median as a json snippet as in '{'nbars':''}' where the number of bars is an integer stored in the empty string in the snippet.")
+Definitions are not rigorous, user should feel welcome to mix and match at will.
+## Citation information
+If you use this work please cite:
+```
+Accepted to JCDL 2025!
+More info TBD
 ```