# Generative Echo Chamber? Effects of LLM-Powered Search Systems on Diverse Information Seeking

arXiv: https://arxiv.org/abs/2402.05880 · HTML: https://mlsanity.com/paper/2402.05880/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 74 references that are in the map.
- First posted on arXiv 8 Feb 2024.

## Builds on (most cited first, 16 of 16)

- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Scaling Language Models: Methods, Analysis & Insights from Training Gopher](https://mlsanity.com/paper/2112.11446/) (2021)
- [Persistent Anti-Muslim Bias in Large Language Models](https://mlsanity.com/paper/2101.05783/) (2021)
- [Evaluating Verifiability in Generative Search Engines](https://mlsanity.com/paper/2304.09848/) (2023)
- [CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities](https://mlsanity.com/paper/2201.06796/) (2022)
- [A Survey on Conversational Recommender Systems](https://mlsanity.com/paper/2004.00646/) (2020)
- [Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction](https://mlsanity.com/paper/2210.05791/) (2022)
- [Rethinking Model Evaluation as Narrowing the Socio-Technical Gap](https://mlsanity.com/paper/2306.03100/) (2023)
- [Characteristics of Harmful Text: Towards Rigorous Benchmarking of Language Models](https://mlsanity.com/paper/2206.08325/) (2022)
- [Investigating Explainability of Generative AI for Code through Scenario-based Design](https://mlsanity.com/paper/2202.04903/) (2022)
- [Beyond Text Generation: Supporting Writers with Continuous Automatic Text Summaries](https://mlsanity.com/paper/2208.09323/) (2022)
- [Perfection Not Required? Human-AI Partnerships in Code Translation](https://mlsanity.com/paper/2104.03820/) (2021)
- [The AI Ghostwriter Effect: When Users Do Not Perceive Ownership of AI-Generated Text But Self-Declare as Authors](https://mlsanity.com/paper/2303.03283/) (2023)
- [The Effects of System Initiative during Conversational Collaborative Search](https://mlsanity.com/paper/2202.09728/) (2022)
- [Powering an AI Chatbot with Expert Sourcing to Support Credible Health Information Access](https://mlsanity.com/paper/2301.10710/) (2023)
- [Inform the uninformed: Improving Online Informed Consent Reading with an AI-Powered Chatbot](https://mlsanity.com/paper/2302.00832/) (2023)

## Built on by (most cited first, 13 of 13)

- [Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization](https://mlsanity.com/paper/2406.01171/) (2024)
- [Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Era](https://mlsanity.com/paper/2404.11457/) (2024)
- [AI and the Problem of Knowledge Collapse](https://mlsanity.com/paper/2404.03502/) (2024)
- [DiscipLink: Unfolding Interdisciplinary Information Seeking Process via Human-AI Co-Exploration](https://mlsanity.com/paper/2408.00447/) (2024)
- [Generative AI in Knowledge Work: Design Implications for Data Navigation and Decision-Making](https://mlsanity.com/paper/2503.18419/) (2025)
- [Antagonistic AI](https://mlsanity.com/paper/2402.07350/) (2024)
- [Search Engines Post-ChatGPT: How Generative Artificial Intelligence Could Make Search Less Reliable](https://mlsanity.com/paper/2402.11707/) (2024)
- [Aligning Large Language Models with Diverse Political Viewpoints](https://mlsanity.com/paper/2406.14155/) (2024)
- [Towards Ethical Personal AI Applications: Practical Considerations for AI Assistants with Long-Term Memory](https://mlsanity.com/paper/2409.11192/) (2024)
- [Large Language Models in Politics and Democracy: A Comprehensive Survey](https://mlsanity.com/paper/2412.04498/) (2024)
- [Same Stories, Different Journeys: Exploring Persona-Grounded Conversational Agents for Supporting Career Exploration with Peers' Posts](https://mlsanity.com/paper/2607.11039/) (2026)
- [Evaluating the Hidden Costs of Personalization in Large Language Models](https://mlsanity.com/paper/2608.28833/) (2026)
- [WildSEEK: Evaluating Language Models for Information-Seeking](https://mlsanity.com/paper/2608.30683/) (2026)

## Abstract (arXiv)

Large language models (LLMs) powered conversational search systems have already been used by hundreds of millions of people, and are believed to bring many benefits over conventional search. However, while decades of research and public discourse interrogated the risk of search systems in increasing selective exposure and creating echo chambers -- limiting exposure to diverse opinions and leading to opinion polarization, little is known about such a risk of LLM-powered conversational search. We conduct two experiments to investigate: 1) whether and how LLM-powered conversational search increases selective exposure compared to conventional search; 2) whether and how LLMs with opinion biases that either reinforce or challenge the user's view change the effect. Overall, we found that participants engaged in more biased information querying with LLM-powered conversational search, and an opinionated LLM reinforcing their views exacerbated this bias. These results present critical implications for the development of LLMs and conversational search systems, and the policy governing these technologies.