  Recent advances in the development of large language models are rapidly
changing how online applications function. LLM-based search tools, for
instance, offer a natural language interface that can accommodate complex
queries and provide detailed, direct responses. At the same time, there have
been concerns about the veracity of the information provided by LLM-based tools
due to potential mistakes or fabrications that can arise in algorithmically
generated text. In a set of online experiments we investigate how LLM-based
search changes people's behavior relative to traditional search, and what can
be done to mitigate overreliance on LLM-based output. Participants in our
experiments were asked to solve a series of decision tasks that involved
researching and comparing different products, and were randomly assigned to do
so with either an LLM-based search tool or a traditional search engine. In our
first experiment, we find that participants using the LLM-based tool were able
to complete their tasks more quickly, using fewer but more complex queries than
those who used traditional search. Moreover, these participants reported a more
satisfying experience with the LLM-based search tool. When the information
presented by the LLM was reliable, participants using the tool made decisions
with a comparable level of accuracy to those using traditional search, however
we observed overreliance on incorrect information when the LLM erred. Our
second experiment further investigated this issue by randomly assigning some
users to see a simple color-coded highlighting scheme to alert them to
potentially incorrect or misleading information in the LLM responses. Overall
we find that this confidence-based highlighting substantially increases the
rate at which users spot incorrect information, improving the accuracy of their
overall decisions while leaving most other measures unaffected.
