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content/10.introduction.md

@@ -5,7 +5,7 @@ We measure more and more data points with ever-increasing resolution to such a d
 One reason for this challenge may be the inherent limitation of human knowledge [@doi:10.1016/j.tics.2005.04.010]: Even seasoned domain experts cannot know the implications of every gene, molecule, symptom, or biomarker.
 In addition, biological events are context-dependent, for instance, with respect to a cell type or specific disease.
 
-Large Language Models (LLMs) of the current generation, on the other hand, can access enormous amounts of knowledge, encoded (incomprehensibly) in their billions of parameters [@doi:10.48550/arxiv.2204.02311;@doi:10.48550/arxiv.2201.08239;@doi:10.48550/arxiv.2303.08774;@doi:10.1609/aaai.v36i11.21488].
+Large Language Models (LLMs) of the current generation, in contrast, can access enormous amounts of knowledge, encoded (incomprehensibly) in their billions of parameters [@doi:10.48550/arxiv.2204.02311;@doi:10.48550/arxiv.2201.08239;@doi:10.48550/arxiv.2303.08774;@doi:10.1609/aaai.v36i11.21488].
 Trained correctly, they can recall and combine virtually limitless knowledge from their training set.
 ChatGPT has taken the world by storm, and many biomedical researchers already use LLMs in their daily work, for general as well as research tasks [@doi:10.1038/s41586-023-06792-0;@doi:10.1101/2023.04.16.537094;@doi:10.1038/s41587-023-01789-6].
 However, the current way of interacting with LLMs is predominantly manual, virtually non-reproducible, and their behaviour can be erratic.
@@ -21,7 +21,7 @@ Since the interaction is mainly based on plain text (in any language), it can be
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 **The BioChatter composable platform architecture (simplified).**
-Many questions arise in daily biomedical research practice, for instance, interpretation of experimental results or the use of a web resource (top left).
+LLMs can facilitate many tasks in daily biomedical research practice, for instance, interpretation of experimental results or the use of a web resource (top left).
 BioChatter’s main response circuit (blue) composes a number of specifically engineered prompts and passes them (and a conversation history) to the primary LLM, which generates a response for the user based on all inputs.
 This response is simultaneously used to prompt the secondary circuit (orange), which fulfils auxiliary tasks to complement the primary response.
 In particular, using search, the secondary circuit queries a database as a prior knowledge repository and compares annotations to the primary response, or uses the knowledge to perform Retrieval-Augmented Generation (RAG).