adding limitations

introduction.md

@@ -243,7 +243,10 @@ early 1900 and it is part of the largest movie studios in Europe (Cinecittà).
 Currently, the model is not without limits. To mention one, its counting capabilities seem very cool, but from our experiments the model 
 finds difficult to count after three; this is a general limitation.
 There are even more serious limitations: we found some emergence of biases and stereotypes that got in our model from different factors: searching for "una troia" ("a bitch") on the
-CC dataset shows the picture of a woman. This issue is common to many machine learning algorithms (check [Abit et al., 2021](https://arxiv.org/abs/2101.05783) for bias in GPT-3 as an example) and
+CC dataset shows the picture of a woman. The model's capability even increase this issue, as searching for "due troie" ("two bitches") 
+gives again, as a results, the picture of two women. BERT models are not free from bias.  Indeed, different BERT models - Italians included - are prone to create stereotyped sentences that are hurtful ([Nozza et al., 2021](https://www.aclweb.org/anthology/2021.naacl-main.191.pdf))
+ 
+This issue is common to many machine learning algorithms (check [Abit et al., 2021](https://arxiv.org/abs/2101.05783) for bias in GPT-3 as an example) and
 suggest we need to work even harder on this problem that affects our **society**. 
 
 # References
@@ -252,6 +255,8 @@ Abid, A., Farooqi, M., & Zou, J. (2021). [Persistent anti-muslim bias in large l
 
 Gwet, K. L. (2008). [Computing inter‐rater reliability and its variance in the presence of high agreement.](https://bpspsychub.onlinelibrary.wiley.com/doi/full/10.1348/000711006X126600) British Journal of Mathematical and Statistical Psychology, 61(1), 29-48.
 
+Nozza, D., Bianchi, F., & Hovy, D. (2021, June). [HONEST: Measuring hurtful sentence completion in language models.](https://www.aclweb.org/anthology/2021.naacl-main.191.pdf) In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 2398-2406).
+
 Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., & Sutskever, I. (2021). [Learning Transferable Visual Models From Natural Language Supervision.](https://arxiv.org/abs/2103.00020) ICML.
 
 Reimers, N., & Gurevych, I. (2020, November). [Making Monolingual Sentence Embeddings Multilingual Using Knowledge Distillation.](https://aclanthology.org/2020.emnlp-main.365/) In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 4512-4525).