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license: cc-by-4.0
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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 2026-01-01 and 2026-04-01.
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
```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")
``` |