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Plant blindness and diversity in AI language models
Anja Geitmann1, Amir J Bidhendi1
1Faculty of Agricultural and Environmental Sciences, McGill University, Montreal, QC, Canada.
Trends in Plant Science
|August 4, 2023
Summary
Large language models (LLMs) can speed up scientific tasks. However, ChatGPT
Area of Science:
- Artificial Intelligence in Scientific Research
- Computational Biology
- Bioinformatics
Background:
- Large language models (LLMs) show potential for accelerating scientific discovery.
- The application of LLMs in biological sciences requires careful evaluation.
Purpose of the Study:
- To assess the diversity and reliability of answers generated by ChatGPT for biological questions.
- To highlight the necessity of expert validation in AI model development for science.
Main Methods:
- Analysis of ChatGPT responses to a range of biology-related queries.
- Evaluation of the consistency and accuracy of the generated content, focusing on specific biological concepts like 'plant awareness'.
Main Results:
- ChatGPT's responses regarding biological topics, specifically 'plant awareness,' exhibited significant variability.
- This variability underscores potential limitations in current LLM understanding of complex scientific domains.
Conclusions:
- Scientists must actively validate AI-generated data and training methodologies.
- Ensuring the accuracy and reliability of AI tools is crucial for their effective integration into scientific workflows.
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