Send message to Authors

Short abstract:

We compare the communicative aspects and forms of agency of LLM-based systems for text generation with those of traditional search engines. We argue that with search engines, the communication partners are still human beings, but for generative A.I., the communicative partner is the machine.

Long abstract:

We compare the communicative aspects and forms of agency of LLM-based systems for text generation with those of traditional search engines. Systems such as Google search practically achieve the automation of an extensive and dynamic catalog. Such a system allows users to enter into communication with authors (who may be also unknown, anonymous or multiple) of texts that can be accessed by following links provided in response to their queries. The communication partners are still human beings. LLM-based systems such as GPT3.5 instead, use the texts produced by human beings in the training data and in the interaction to autonomously generate content that may have never been written before and read by a human. In this unprecedented form of artificial communication, the communicative partner is directly the machine and the agency of such systems can be understood as an interactional achievement .

The new, A.I.-powered search engine Perplexity combines the features of both search engine and generative A.I. (more generally described as “retrieval augmented generation”), operating as a partner that enables communication both through others and with others. Through empirical experiments, we compare the performance of Perplexity with that of GPT3.5, focusing on the interaction with the user in response to factual questions, the use and indication of sources, and the handling of hallucinations or lack of knowledge.

Our hypothesis is that the communicative agency of the digital system underlies Perplexity's performance, enabling it to achieve results that can exceed those of systems much larger in size and computational capacity.