metadata
license: cc-by-4.0
configs:
- config_name: posterior_sampled_100_estimation
data_files:
- split: test
path: posterior_sampled_100_estimation/test-*
- config_name: posterior_sampled_100_most_freq
data_files:
- split: test
path: posterior_sampled_100_most_freq/test-*
- config_name: posterior_sampled_100_second_freq
data_files:
- split: test
path: posterior_sampled_100_second_freq/test-*
- config_name: posterior_sampled_50_estimation
data_files:
- split: test
path: posterior_sampled_50_estimation/test-*
- config_name: posterior_sampled_50_most_freq
data_files:
- split: test
path: posterior_sampled_50_most_freq/test-*
- config_name: posterior_sampled_50_second_freq
data_files:
- split: test
path: posterior_sampled_50_second_freq/test-*
- config_name: prior_estimation
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path: prior_estimation/test-*
- config_name: prior_most_freq
data_files:
- split: test
path: prior_most_freq/test-*
- config_name: prior_second_freq
data_files:
- split: test
path: prior_second_freq/test-*
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- config_name: prior_second_freq
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Text2DistBench is a benchmark for evaluating whether large language models can infer distributional knowledge from natural language evidence. Given metadata and a set of user comments about an entity (e.g., a movie or song), models must estimate statistics such as: stance distribution, topic distribution, and most/second-most frequent labels.
The dataset is constructed from movie and music entities released between 2025-12-01 and 2026-03-01.
It includes two types of configurations:
Posterior configurations include both metadata and user comments as evidence.
These configurations differ by sample size (50 or 100 comments) and task type:
posterior_sampled_50_estimation,
posterior_sampled_50_most_freq,posterior_sampled_50_second_freq,posterior_sampled_100_estimation,
posterior_sampled_100_most_freq,
posterior_sampled_100_second_freq
Prior configurations contain only metadata without comments: prior_estimation, prior_most_freq, prior_second_freq
Load the Dataset
from datasets import load_dataset
ds = load_dataset("frett/Text2DistBench", "posterior_sampled_50_estimation", split="test")
ds = load_dataset("frett/Text2DistBench", "prior_most_freq", split="test")