Update README.md

README.md

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   dataset_size: 1114583
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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.
+**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 understand and response statistics such as:
+stance/topic distribution, and most/second- 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`  
+These configurations differ by sample size (50 or 100 comments) and task type (estimation, most_freq, second_freq):
+`posterior_sampled_<comment_num>_<task>`
 
-**Prior configurations** contain only metadata without comments: 
-`prior_estimation`, 
-`prior_most_freq`, 
-`prior_second_freq`  
+**Prior configurations** contain only metadata without comments: `prior_<task>`
 
 
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