citation update

README.md

@@ -149,5 +149,27 @@ for decode_dream in output:
 Upon evaluation we identified no dual-use implication for the present model. The model parameters, including the weights are available under CC0 1.0 Public Domain Dedication.
 
 # Cite
-Please note that the paper referring to this model, titled PreDA: Prefix-Based Dream Reports Annotation
-with Generative Language Models, has been accepted for publication at LOD 2025 conference and will appear in the conference proceedings.
+If you use our models in your research, please cite us as:
+```bibtex
+@InProceedings{10.1007/978-3-032-21477-5_13,
+author="Bertolini, Lorenzo
+and Comte, Valentin
+and Ceresa, Mario
+and Consoli, Sergio",
+editor="Nicosia, Giuseppe
+and Ojha, Varun
+and Giesselbach, Sven
+and Pardalos, M. Panos
+and Umeton, Renato
+and Emanuele, La Malfa
+and Gabriele, La Malfa",
+title="PreDA: Prefix-Based Dream Reports Annotation with Generative Language Models",
+booktitle="Machine Learning, Optimization, and Data Science",
+year="2026",
+publisher="Springer Nature Switzerland",
+address="Cham",
+pages="191--206",
+abstract="Dream reports are recollections of our experiences while asleep, and have strong research and clinical value. Since their analysis can be extremely time-consuming, researchers have adopted multiple types of automatised approaches, including, in more recent years, pre-trained language models (PLMs). However, most work has focused on limited aspects of the report content, such as characters or emotions. In this work, we introduce PreDA (prefix-based dream reports annotation), a framework to build language models able to annotate a dream report for multiple relevant aspects, using generative PLMs. We provide experimental evidence showing how a single PLM of small dimension can efficiently annotate a report on multiple features of the Hall and Van De Castle (HVDC) framework, give a detailed analysis of the model's performance, and explain how the training data impact learning and generalisation ability of the model.",
+isbn="978-3-032-21477-5"
+}
+```