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📌 Overview
Text2DistBench is a reading comprehension 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 infer statistics such as: stance/topic distribution, and most/second- frequent labels.
⚙️ Configurations
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 (estimation, most_freq, second_freq).
Format: posterior_sampled_<comment_num>_<task>
🟨 Prior Configurations
Include only metadata (no comments).
Format: prior_<task>
⚙️ Dataset Format
Each benchmark instance corresponds to a distributional reading comprehension question.
{
"qid": < question id >,
"qtype": < distribution type >,
"answer": < answer (mode label or distribution depending on task) >,
"ref_dist": < Ground-truth distribution over labels >,
"question": < Full prompt shown to model (instruction + evidence + query) >,
"source": "< entity name >",
"meta_data": "< text evidence >",
"comments": "< text evidence (if posterior)>",
"condition": "< conditioning variable for P(s|t), P(t|s) >",
}
⚙️ 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")