MARBLES Dataset

MARBLES is a physical guesstimation benchmark designed to probe the world models of large language models (LLMs). It is one of three guesstimation datasets introduced in the associated paper, alongside FUTURE and ELECPRED.

Dataset Description

This dataset contains 15 guesstimation questions that ask how many small objects (M&Ms, marbles, or quarters) fit inside various common containers (Starbucks cup, shot glass, measuring cup, Altoids tin, or card box). Each question has a ground-truth answer obtained through physical measurement.

Associated Paper

Chuang, Y. S., Narendran, S., Harlalka, N., Cheung, A., Gao, S., Suresh, S., ... & Rogers, T. T. (2025). Probing LLM World Models: Enhancing Guesstimation with Wisdom of Crowds Decoding. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (pp. 4699-4713).

Paper: https://aclanthology.org/2025.emnlp-main.234/

Dataset Structure

Data Files

• questions.csv: Contains all 15 guesstimation questions with ground-truth answers
• images/: Contains reference images for each question

Data Fields

Field	Description
index_question	Unique question identifier (1-15)
dataset	Dataset name (MARBLES)
content	The guesstimation question in natural language
true_answer	Ground-truth answer obtained through physical measurement
image	Filename of the corresponding reference image

Example

Question: How many standard-sized M&Ms does it take to fill a Starbucks iced tall cup?
Answer: 382
Image: MM_starbucks.png

Objects and Containers

Objects:

• Standard-sized M&Ms
• Standard-sized U.S. marbles
• U.S. quarters

Containers:

• Starbucks iced tall cup
• Single-shot shot glass
• One cup dry ingredient measuring cup
• Altoids tin container
• Standard-sized Bicycle playing cards box

Images

While the original paper focuses on text-based guesstimation (without visual input), we include reference images for each question-answer pair. These images show the objects inside the containers and may be useful for multimodal research or verification purposes.

Usage

from datasets import load_dataset

dataset = load_dataset("YOUR_USERNAME/MARBLES")

Citation

If you use this dataset, please cite:

@inproceedings{chuang-etal-2025-probing,
    title = "Probing {LLM} World Models: Enhancing Guesstimation with Wisdom of Crowds Decoding",
    author = "Chuang, Yun-Shiuan  and
      Narendran, Sameer  and
      Harlalka, Nikunj  and
      Cheung, Alexander  and
      Gao, Sizhe  and
      Suresh, Siddharth  and
      Hu, Junjie  and
      Rogers, Timothy T.",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.emnlp-main.234/",
    doi = "10.18653/v1/2025.emnlp-main.234",
    pages = "4699--4713",
    ISBN = "979-8-89176-332-6"}

License

This dataset is released under the MIT License.