Text2DistBench / README.md
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---
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
data_files:
- split: test
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-*
dataset_info:
- config_name: posterior_sampled_100_estimation
features:
- name: qid
dtype: string
- name: task
dtype: string
- name: domain
dtype: string
- name: distribution_type
dtype: string
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- name: answer
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- name: ref_dist
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- name: question
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- name: meta_data
dtype: string
- name: comments
dtype: string
splits:
- name: test
num_bytes: 7224323
num_examples: 297
download_size: 1753670
dataset_size: 7224323
- config_name: posterior_sampled_100_most_freq
features:
- name: qid
dtype: string
- name: task
dtype: string
- name: domain
dtype: string
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- config_name: prior_estimation
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- config_name: prior_most_freq
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dtype: string
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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
```bash
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")
```