remove chatGSE name from main text

content/01.abstract.md

@@ -4,4 +4,4 @@ Current-generation Large Language Models (LLMs) have stirred enormous interest i
 To facilitate interfacing with LLMs in the biomedical space, while at the same time safeguarding their functionalities through sensible constraints, we propose a dedicated, open-source framework: BioChatter.
 Based on open-source software packages, we synergise the many functionalities that currently develop around LLMs, such as knowledge integration / retrieval-augmented generation, model chaining, and benchmarks, resulting in an easy-to-use and inclusive framework for application in many use cases of biomedical informatics.
 We focus on robust and user-friendly implementation, including ways to deploy privacy-preserving local open-source LLMs.
-We demonstrate use cases via a multi-purpose web app, ChatGSE (https://chat.biocypher.org), and provide documentation, support, and an open community to all interested researchers.
+We demonstrate use cases via a multi-purpose web app (https://chat.biocypher.org), and provide documentation, support, and an open community to all interested researchers.

content/20.results.md

@@ -19,7 +19,7 @@ Functionalities include:
 
 - **fact-checking** of LLM responses using a second LLM
 
-In the following, we briefly describe these components, which are demonstrated in the ChatGSE web app (https://chat.biocypher.org).
+In the following, we briefly describe these components, which are demonstrated in our web app (https://chat.biocypher.org).
 
 ### Question Answering and LLM Connectivity
 

content/40.methods.md

@@ -58,7 +58,7 @@ For instance, we detail the properties of a node and the source and target class
 Additionally, during the KG build process, we enrich this information and save it to a YAML file and, optionally, directly to the KG.
 This information is used by BioChatter to tune its understanding of the KG, which allows the LLM to query the KG more efficiently.
 By understanding the context of the KG, the exact contents, and the exact spelling of all identifiers and properties, we effectively support the LLM in generating correct queries.
-To illustrate the usage of this feature, we provide a demonstration repository at https://github.com/biocypher/pole including a KG build procedure and ChatGSE app, which can be run using a single Docker Compose command.
+To illustrate the usage of this feature, we provide a demonstration repository at https://github.com/biocypher/pole including a KG build procedure and web app, which can be run using a single Docker Compose command.
 
 ### Retrieval-Augmented Generation