Today’s large language models can be as unreliable as they are eloquent. Their tendency to fabricate facts and lose the thread makes them risky tools for scientific research, especially in highly regulated industries like pharmaceuticals and chemistry. They also struggle to provide sources and will fabricate a bogus academic journal without batting an eye. Speaking…
SciBite Chat: Elsevier’s answer to ChatGPT for life science researchers, minus the hallucinations
The life sciences industry is abuzz with the potential of generative AI, but its application in the highly regulated pharmaceutical sector faces challenges. As Jane Lomax, Ph.D., head of ontologies at Elsevier’s SciBite subsidiary notes, “Everyone across the whole industry is experimenting with it. But no one knows for sure yet how best to use…
The role of natural language processing in advancing disease research
In any area of disease research, a deep understanding of recent and future trends surrounding a particular condition is crucial to the drug discovery process. But with the volume of scientific literature increasing all the time, it is difficult to manually sift through all the existing information and correlate data in such a way to…