diff --git a/_gsocproposals/2026/proposal_FASEROH.md b/_gsocproposals/2026/proposal_FASEROH.md
index bb239d5..d87b4bc 100644
--- a/_gsocproposals/2026/proposal_FASEROH.md
+++ b/_gsocproposals/2026/proposal_FASEROH.md
@@ -10,7 +10,9 @@ organization:
 
 ## Description
 
-State-of-the-art sequence to sequence models (seq2seq) have yielded spectacular advances in neural machine translation (NMT) (see, for example, [Ref1](https://arxiv.org/pdf/1912.02047.pdf) ). Recently, these models have been successfully applied to  symbolic mathematics by conceptualizing the latter as  translation from one sequence of symbols to another ( [Ref2](https://arxiv.org/abs/1912.01412) ). It is easy to imagine numerous tasks that can be construed as translations. In the proposed Gsoc project the goal is to create a tool that automatically provides an accurate symbolic representation of a histogram by construing the problem as one of translation from a histogram to a symbolic function. We call the project Fast Accurate Symbolic Empirical Representation Of Histograms (FASEROH).
+State-of-the-art sequence-to-sequence (seq2seq) models, a class of neural sequence models, have led to significant advances in neural machine translation (NMT) (see, for example, [NMT-Felix Stahlberg](https://arxiv.org/pdf/1912.02047.pdf)). Recently, these models have been successfully applied to [symbolic mathematics](https://arxiv.org/abs/1912.01412) by conceptualizing the latter as translation from one sequence of symbols to another. It is easy to imagine numerous tasks that can be construed as translation problems. 
+
+In the proposed GSoC project, the goal is to create a tool that automatically provides an accurate symbolic representation of a histogram by framing the problem as a translation task from a histogram to a symbolic function. We call this project **Fast Accurate Symbolic Empirical Representation Of Histograms (FASEROH)**.
 
 ## Duration