Update README.md

README.md

@@ -1,6 +1,6 @@
 ---
 language: en
-license: apache-2.0
+license: mit
 size_categories:
 - 1K<n<10K
 task_categories:
@@ -32,35 +32,16 @@ The key feature of this dataset is that it captures communication between two ag
 - **Curated by**: University of California, Berkeley
 - **Time per Task**: Median 33.0s for speakers, 10.5s for listeners
 
-## Uses
-
-### Direct Use
-
-The dataset is designed for:
-- Training and evaluating referring expression generation models
-- Training and evaluating visual question answering systems
-- Studying human spatial language use in multi-perspective scenarios
-- Developing embodied AI systems that can communicate about shared environments
-- Research on perspective-taking in language generation and comprehension
-
-### Out-of-Scope Use
-
-The dataset should not be used for:
-- Training systems to navigate or manipulate physical environments
-- Training general-purpose vision-language models without consideration of perspective
-- Applications requiring real-time interaction or dialogue (dataset contains single-turn interactions only)
-
 ## Dataset Structure
 
 Each instance contains:
-- Speaker view image (1280x720 resolution)
-- Listener view image (1280x720 resolution)
-- Natural language referring expression from speaker
+- Speaker view image (1024x1024 resolution)
+- Listener view image (1024x1024 resolution)
+- Natural language referring expression from human speaker
 - Target object location
 - Listener object selection
 - Scene metadata including:
   - Agent positions and orientations
-  - Field of view overlap measurements
   - Referent placement method (random vs adversarial)
   - Base environment identifier
 
@@ -88,32 +69,11 @@ The dataset was created to study how humans and AI systems handle referential co
 
 #### Who are the source data producers?
 
-- Base 3D environments: ScanNet++ dataset
+- Base 3D environments: ScanNet dataset
 - Referring expressions: English-speaking crowdworkers from the United States
 - Quality filtering: Automated GPT-4V system
 - Scene generation: Automated system with physics simulation
 
-### Personal and Sensitive Information
-
-The dataset does not contain personally identifiable information. Crowdworker data was checked to exclude private information and offensive content.
-
-## Bias, Risks, and Limitations
-
-- Limited to indoor environments from ScanNet++
-- English language only
-- Single-turn interactions only (no dialogue)
-- Restricted to specific object types (spheres)
-- May reflect cultural biases in spatial language use
-- Limited demographic diversity of crowdworkers
-
-### Recommendations
-
-- Consider cultural and linguistic differences in spatial language when using the dataset
-- Account for perspective differences when developing models
-- Evaluate performance across different relative orientations and referent placements
-- Consider expanding to multi-turn dialogue in future work
-- Test for biases in spatial language use across different demographics
-
 ## Citation
 
 **BibTeX:**
@@ -128,8 +88,4 @@ The dataset does not contain personally identifiable information. Crowdworker da
 
 ## Dataset Card Contact
 
-Contact the authors at {terran, lingjun, suhr}@berkeley.edu
-
-## More Information
-
-Code, models, and dataset available at: https://github.com/zinengtang/MulAgentRef
+Contact the authors at terran@berkeley.edu