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@@ -66,7 +66,7 @@ The dataset is in **Mandarin Chinese** (Simplified).
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  ### Data Instances
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- #### VLM Subset
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  An example instance for the VLM dataset includes a visual scene and a context description:
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  ```json
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  {
@@ -143,10 +143,6 @@ This dataset was created to bridge the gap in **multilingual** and **multimodal*
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  This dataset contributes to the development of more **culturally and linguistically inclusive AI**. It evaluates models on an understudied pragmatic phenomena. Furthermore, by probing "rational" communication strategies, it aids in creating agents that communicate more efficiently and naturally with humans.
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- ### Discussion of Biases
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- While the dataset addresses linguistic bias, the models evaluated using it may still exhibit **prompting bias**. As noted in the accompanying paper, instruction-tuned models often default to over-specification (choosing explicit but costly alternatives) rather than the economical forms preferred by rational agents. Users should be aware that high accuracy on the sanity checks (superlatives) does not guarantee pragmatic competence in ambiguous contexts.
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  ### Other Known Limitations
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  * **Scale of VLM Data:** The VLM subset is small (26 items) and should be treated as a proof-of-concept. Statistical claims based on this subset rely on bootstrapping over generations rather than items.
 
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  ### Data Instances
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+ #### VLM Subset [work-in-progress]
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  An example instance for the VLM dataset includes a visual scene and a context description:
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  ```json
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  {
 
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  This dataset contributes to the development of more **culturally and linguistically inclusive AI**. It evaluates models on an understudied pragmatic phenomena. Furthermore, by probing "rational" communication strategies, it aids in creating agents that communicate more efficiently and naturally with humans.
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  ### Other Known Limitations
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  * **Scale of VLM Data:** The VLM subset is small (26 items) and should be treated as a proof-of-concept. Statistical claims based on this subset rely on bootstrapping over generations rather than items.