The Report on ChatGPT's Biological Knowledge Accuracy

 



This report summarizes a study evaluating the accuracy of biological knowledge generated by the generative AI tool ChatGPT. With the rise of generative AI, assessing their capabilities and content is crucial for establishing trust. This study computationally examines ChatGPT's claims using robust network models, focusing on the aggregate-level accuracy of biological knowledge embedded in ChatGPT-generated texts.

The research employs a biological networks approach to systematically investigate linked entities within ChatGPT. An ontology-driven fact-checking algorithm compares biological graphs derived from approximately 200,000 PubMed abstracts (representing "real" knowledge) with graphs from a ChatGPT-3.5 Turbo generated dataset ("simulated" knowledge). The algorithm specifically analyzes disease-gene links within these graphs to assess ChatGPT's accuracy in this domain.

Results indicate a high accuracy of disease-gene links in ChatGPT-generated text, ranging from 70% to 86% across 10 samples. This suggests ChatGPT-3.5 Turbo can produce reasonably accurate texts regarding disease-gene relationships at a macro level, fostering confidence in its biological knowledge.

The study highlights potential applications of ChatGPT in biological research, including information extraction, scientific article summarization, and research hypothesis generation. However, it acknowledges that the study focuses on macro-level accuracy, and further research is needed to assess accuracy at more granular levels.

Future steps include evaluating ChatGPT's knowledge in specialized biological fields, comparing its performance to other generative AI tools, and examining newer ChatGPT versions.

In conclusion, this study demonstrates the potential of generative AI tools like ChatGPT in bioscience, suggesting their utility in scientific research. However, continuous evaluation remains vital to ensure their reliability and accuracy. This research provides a valuable initial step, paving the way for further exploration in this area.


Source : Fact-Checking Generative AI: Ontology-Driven Biological Graphs for Disease-Gene Link Verification



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