1. Applications of ChatGPT & Co in
scientific research: systematic search
and comparison of tools
Mohamed Nejjar | Technical University of Munich
mohamed.nejjar@tum.de
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3. AI is still in its early days.
Fast paced development.
It can already talk??!
What are generative language models?
To which extent can they support you?
Is this really of interest to me?
Can we fully trust these tools?
Will these tools replace me and take my
job?
How could the findings of this research
benefit me?
They can already
communicate with
us?!
Why am i here?
How does this
work?
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4. Outline 1.Methodology
A. Tools used
B. Tasks selected
C. Criteria chosen
2.Findings and implications
3.Conclusion
4.Q&A
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5. Methodology
A. Tools used
GPT-3.5, GPT-4, Bing, Google Bard, You,
Github Copilot, DeepL, Quillbot and
Graphmaker.ai
Some of the most advanced and widely used
solutions in this field and area of
research
Some are generative language models (e.g.
ChatGPT, Bing), others are more specific in
what they do (e.g. DeepL, Copilot)
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6. Methodology
B. Tasks selected
Need to be tested on a wide array of
tasks
Text manipulation : Text enhancement,
text summarization and relevant reference
retrieval.
Inspiration source : Writing inspiration
source, presentation questions
generation.
Technical support : Coding, Data
Visualization and Data Analysis
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7. Methodology
C. Criteria chosen
How can we assess the capabilities of
these tools?
Why choose some criteria over others?
How unbiased is this rating system ?
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8. Findings and implications
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Impressive capabilities and range of knowledge
The diversity of tools is synonymous with a
multitude of ways to solve problems
But not ready to be fully relied on
Limitations include context misunderstanding,
lack of judgement and artificial hallucination
Good practices of AI use in research
