Datasets Descriptions:

Experiment 1: 


We refer Experiment 1 as Category_1 in the dataset. 

There are 8 columns in our dataset. The description of each column is given below:


Version: this is the named and nameless variant in our experiment. Version 1 refers the named variant and Version 2 refers the nameless variant. 


brand_name: This column indicates 4 different types of brand names.  


context: This column indicates different sentences. These are the context sentences.

item_category: This column is either 'positive' or 'negative'. When the attribute or stimulus in the context sentence is positive, we named it as 'positive' and when the attribute or stimulus is negative, then we named it as 'negative'.   

type_category: This columns tells us, which direction the data is. There are two different types of direction, namely type_1 and type_2. We refer these two directions as Stimulus to Attribute Inference (SAI) and Attribute to Stimulus Association (ASA) in our paper. 

type_1 =  Stimulus to Attribute Inference (SAI)
type_2 = Attribute to Stimulus Association (ASA)


anti_stereotype: When the 'item_category' column is 'negative', then this column contains attribute/stimulus that is positive among the options according to our definition. On the other hand, when the 'item_category' column is 'positive', then this column contains attribute/stimulus that is negative among the options according to our definition in paper. 

stereotype: This column is opposite of 'anti_stereotype' column. When the 'item_category' column is 'negative', then this column contains attribute/stimulus that is negative among the options according to our definition. On the other hand, when the 'item_category' column is 'positive', then this column contains attribute/stimulus that is positive among the options according to our definition in paper.

unrelated: This column contains the neutral attributes or stimuli according to our definition in paper. 


Experiment 2: 

We refer Experiment 2 as Category_2 in the dataset. 


There are 4 columns in our dataset. The description of each column is given below:


brand_name: This column indicates 4 different types of brand names. 


luxury: this column represent the countries with the highest GDP per capita


non-luxury: this column represent the countries with the lowest GDP per capita.



context: this the column with the detailed description and prompt. 



Experiment 3: 

We refer Experiment 3 as Category_3 in the dataset. 


There are 3 columns in our dataset. The description of each column is given below:


brand_name: This column indicates 4 different types of brand names. 


country: this column represent the countries


context: this the column with the detailed description and prompt. 