Update README.md

README.md

@@ -22,6 +22,8 @@ configs:
 
 # ORBIT-MT-Eval: Expert Annotations and Prompts for Machine Translation Meta-Evaluation
 
+**Paper:** [Beyond Prompts: A Systematic Study of LLM-Based Machine Translation Evaluators](https://arxiv.org/abs/2610.33741)
+
 ORBIT-MT-Eval is an English-to-Russian reference dataset for machine translation meta-evaluation: comparing evaluator outputs against professional expert annotations. Each of the 600 source–translation segments has three independent expert annotations following the [RATE protocol](https://aclanthology.org/2025.findings-emnlp.1203/). In *Beyond Prompts: A Systematic Study of LLM-Based Machine Translation Evaluators*, two annotations serve as gold references and the third is scored against each reference and averaged to produce the Human_E agreement baseline. The release also includes the prompts used by ORBIT and the locally maintained prompting baselines. GEMBA and `unified-mqm-boosted-v5` are linked to their official upstream implementations.
 
 ## Dataset at a glance
@@ -51,7 +53,7 @@ ORBIT-MT-Eval is an English-to-Russian reference dataset for machine translation
 ```python
 from datasets import load_dataset
 
-dataset = load_dataset("foksly/orbit-mt-eval", "rate_annotations", split="test")
+dataset = load_dataset("yandex/orbit-mt-eval", "rate_annotations", split="test")
 print(dataset[0])
 ```
 
@@ -84,7 +86,10 @@ Prompting baselines and ORBIT-SC use GPT-5.4. ORBIT-MC fuses three evaluator bac
   author    = {Popov, Dmitry and Bokhyan, Roman and Enikeeva, Ekaterina and
                Mekhraliev, Artem and Vysotsky, Stepan and Karpachev, Nikolay},
   booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2026},
-  year      = {2026}
+  year      = {2026},
+  pages     = {51648--51664},
+  publisher = {Association for Computational Linguistics},
+  url       = {https://aclanthology.org/2026.findings-emnlp.2404/}
 }
 ```