From Communications of the ACM
In Search of Better Search
By Samuel Greengard
Long before hard drives and hyperlinks became symbols of modern life, humans etched stories on cave walls and on parchment paper. Later, books, libraries and the Internet filtered into daily life.
Only in the last quarter-century, with the rise of the Web, has it become possible to obtain near-instant and personalized search results. How to cook the perfect omelet? Check. Which astronauts rode Apollo 11 to the Moon? Check. The best towns to visit along the Amalfi Coast? Check...
Of course, the conversational and engaging nature of AI search adds to the charm. It’s easy to ask follow-up questions and pursue a topic without deep analysis. The system does the heavy lifting—or at least, it appears to do so. “The great strength of generative AI is its ability to summarize vast amounts of information and present these summaries in a fluent and well-organized manner,” said Marti A. Hearst, a professor in the School of Information and in the Computer Science Division at the University of California, Berkeley.
LLMs come with plenty of baggage, however. This includes biases based on how they were trained, the copyright and legality of content used to build the model, privacy concerns, and, perhaps most importantly, the accuracy of the results they deliver. “It is well-known and frequently stated that these systems are not always correct, and they can reflect the biases of and errors in the underlying data on which they are developed,” Hearst said...
Marti Hearst is a professor at the School of Information at UC Berkeley.
