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Upload dataset (incl. keyword dicts)

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README.md CHANGED
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  - Compositionality
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  - Localism-aware compositionality
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  - Multimodal knowledge editing
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  # LACE-Bench: Localism-Aware Compositionality Evaluation Benchmark for Vision-Language Models
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  | `region_ids` | list[string] | IDs of the atomic regions involved in this relational group (e.g. `["2358647_0", "2358647_1"]`) |
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  ## Citation
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  ```bibtex
 
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  - Compositionality
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  - Localism-aware compositionality
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  - Multimodal knowledge editing
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+ configs:
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+ - config_name: default
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+ data_files:
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+ - split: train
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+ path: data/lace_train.parquet
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+ - split: test
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+ path: data/lace_test.parquet
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+ - config_name: keyword_dict
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+ data_files:
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+ - split: train
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+ path: data/train_keyword_dict.parquet
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+ - split: test
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+ path: data/test_keyword_dict.parquet
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  ---
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  # LACE-Bench: Localism-Aware Compositionality Evaluation Benchmark for Vision-Language Models
 
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  | `region_ids` | list[string] | IDs of the atomic regions involved in this relational group (e.g. `["2358647_0", "2358647_1"]`) |
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+ ## `keyword_dict` Config
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+
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+ In addition to the per-image records (`default` config), the dataset ships a `keyword_dict` config that maps each WordNet synset ID to the list of surface phrases observed for that concept across the corresponding split's region captions. These dictionaries are useful for keyword-based lookup, counterfactual phrase matching, and lexical normalization of mentions.
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+ **Files**
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+
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+ - `data/train_keyword_dict.parquet`
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+ - `data/test_keyword_dict.parquet`
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+
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+ **Schema**
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+ | Field | Type | Description |
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+ |---|---|---|
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+ | `synset_id` | string | WordNet synset identifier (e.g. `tree.n.01`), matching `keywords[].synset_id` in the `default` config |
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+ | `phrases` | list[string] | All distinct surface phrases (synonyms, plural forms, modifier-noun variants, casing variants) observed for the synset in that split |
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+
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+ **Example rows**
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+ | `synset_id` | `phrases` |
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+ |---|---|
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+ | `leaf.n.01` | `["leaves", "foliage", "leaf", "banana leaf", "dried leaves", "green leaves", ...]` |
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+ | `tree.n.01` | `["tree", "trunk", "evergreen tree", "pine trees", "fir tree", ...]` |
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+ | `bus.n.01` | `["bus", "city bus", "double decker bus", "motorbus", "coach", ...]` |
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+
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+ **Loading**
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+
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+ ```python
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+ from datasets import load_dataset
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+ ds = load_dataset("lacebench/LACE-Bench", "keyword_dict")
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+ ```
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+
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+
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  ## Citation
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  ```bibtex
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