Large Language Models as Search Engines: Societal Challenges

TL;DR

This article surveys the societal challenges posed by Large Language Models acting as information portals, potentially displacing traditional search engines. It catalogues 15 challenges across Content Creators, End Users, LLM Providers, and Society, detailing current technical and legal mitigations and signaling future research needs. A key finding is the novel risk of cannibalizing the Web, which could undermine content production and the data ecosystems LLMs rely on. The work argues that a balanced approach—combining technical safeguards, regulatory frameworks, and public LLM literacy—is essential to harness benefits while mitigating harms.

Abstract

Large Language Models (LLMs) may one day replace search engines as the primary portal to information on the Web. In this article, we investigate the societal challenges that such a change could bring. We focus on the roles of LLM Providers, Content Creators, and End Users, and identify 15 types of challenges. With each, we show current mitigation strategies -- both from the technical perspective and the legal perspective. We also discuss the impact of each challenge and point out future research opportunities.

Figures (1)

• Figure 1: Parties in an LLM ecosystem