Update README.md

README.md

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-license: other
-license_name: adobe-research-license
-license_link: LICENSE
-language:
-- en
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+---
+license: other
+license_name: adobe-research-license
+license_link: LICENSE
+language:
+- en
+---
 # [ICML 2025] Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage
 
 This dataset is associated with the evaluation in our ICML 2025 paper, [Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage](https://arxiv.org/abs/2412.15484).
@@ -39,10 +39,10 @@ Summarize the generated captions into a dictionary where the key is the correspo
     "test_00599.json": <caption_test_00599>,
 }
 ```
-You may refer to the [sample captions](https://github.com/david-yoon/CapMAS_private/blob/main/sample_captions/llava1.6-vicuna_llama3_th1.0/captions_final.json) for guidance.
+You may refer to the [sample captions](https://github.com/adobe-research/CapMAS/blob/master/sample_captions/llava1.6-vicuna_llama3_th1.0/captions_final.json) for guidance.
 
 ## Evaluation
-Please visit our [GitHub repository](https://github.com/david-yoon/CapMAS_private).
+Please visit our [GitHub repository](https://github.com/adobe-research/CapMAS).
 We provide the evaluation codes for the three metrics used in our paper: **Factuality**, **Coverage**, and **CLAIR** (Chan et al., EMNLP 2023). These evaluations rely on GPT-4o, so please fill in your OpenAI API key **OR** Azure OpenAI credentials in the `conf/gpt4o` file.
 ### Factuality (ours)
 ```factuality