# Source Coverage and Citation Bias in LLM-based vs. Traditional Search Engines

arXiv: https://arxiv.org/abs/2512.09483 · HTML: https://mlsanity.com/paper/2512.09483/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 78 references that are in the map.
- First posted on arXiv 10 Dec 2025.

## Builds on (most cited first, 30 of 30)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [WebGPT: Browser-assisted question-answering with human feedback](https://mlsanity.com/paper/2112.09332/) (2021)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [Least-to-Most Prompting Enables Complex Reasoning in Large Language Models](https://mlsanity.com/paper/2205.10625/) (2022)
- [Measuring and Narrowing the Compositionality Gap in Language Models](https://mlsanity.com/paper/2210.03350/) (2022)
- [Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions](https://mlsanity.com/paper/2212.10509/) (2022)
- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [Humans or LLMs as the Judge? A Study on Judgement Biases](https://mlsanity.com/paper/2402.10669/) (2024)
- [Teaching language models to support answers with verified quotes](https://mlsanity.com/paper/2203.11147/) (2022)
- [Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Era](https://mlsanity.com/paper/2404.11457/) (2024)
- [Tranco: A Research-Oriented Top Sites Ranking Hardened Against Manipulation](https://mlsanity.com/paper/1806.01156/) (2018)
- [HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems](https://mlsanity.com/paper/2411.02959/) (2024)
- [Human and LLM Biases in Hate Speech Annotations: A Socio-Demographic Analysis of Annotators and Targets](https://mlsanity.com/paper/2410.07991/) (2024)
- [Accuracy and Political Bias of News Source Credibility Ratings by Large Language Models](https://mlsanity.com/paper/2304.00228/) (2023)
- [CrAM: Credibility-Aware Attention Modification in LLMs for Combating Misinformation in RAG](https://mlsanity.com/paper/2406.11497/) (2024)
- [Measuring Personalization of Web Search](https://mlsanity.com/paper/1706.05011/) (2017)
- [Citations and Trust in LLM Generated Responses](https://mlsanity.com/paper/2501.01303/) (2025)
- [Unsafe LLM-Based Search: Quantitative Analysis and Mitigation of Safety Risks in AI Web Search](https://mlsanity.com/paper/2502.04951/) (2025)
- [Large Language Models vs. Search Engines: Evaluating User Preferences Across Varied Information Retrieval Scenarios](https://mlsanity.com/paper/2401.05761/) (2024)
- [Evaluation Metrics for Measuring Bias in Search Engine Results](https://mlsanity.com/paper/2210.10517/) (2022)
- [DELPHI: Data for Evaluating LLMs' Performance in Handling Controversial Issues](https://mlsanity.com/paper/2310.18130/) (2023)
- [Comparing Traditional and LLM-based Search for Image Geolocation](https://mlsanity.com/paper/2401.10184/) (2024)
- [Design and Evaluation of IPFS: A Storage Layer for the Decentralized Web](https://mlsanity.com/paper/2208.05877/) (2022)
- [Navigating the Post-API Dilemma | Search Engine Results Pages Present a Biased View of Social Media Data](https://mlsanity.com/paper/2401.15479/) (2024)
- [A Comprehensive Analysis of Twitter Trending Topics](https://mlsanity.com/paper/1907.09007/) (2019)
- [A Large Scale Study and Classification of VirusTotal Reports on Phishing and Malware URLs](https://mlsanity.com/paper/2205.13155/) (2022)
- [Decoding News Bias: Multi Bias Detection in News Articles](https://mlsanity.com/paper/2501.02482/) (2025)
- [Social and Political Framing in Search Engine Results](https://mlsanity.com/paper/2507.13325/) (2025)

## Built on by (most cited first, 6 of 6)

- [EcoGEO: Trajectory-Aware Evidence Ecosystems for Web-Enabled LLM Search Agents](https://mlsanity.com/paper/2605.12887/) (2026)
- [Can AI Agents Synthesize Scientific Conclusions?](https://mlsanity.com/paper/2606.11337/) (2026)
- [Verified Misguidance: Measuring Structural Citation Failures in Search-Augmented LLMs](https://mlsanity.com/paper/2605.28565/) (2026)
- [Position: Generative Engine Optimization Creates Underexamined Risks, Governance Must Target Concentration, Disclosure, and Academic Blind Spots](https://mlsanity.com/paper/2606.12439/) (2026)
- [From Stochastic to Stable: Rank Stability and Structural Sufficiency in AI Visibility Measurement](https://mlsanity.com/paper/2607.10341/) (2026)
- [What Drives Citations in Production Large Language Models? An Observational Multi-Method Study of Two Million AI Citations Across Ten Thousand Web Pages](https://mlsanity.com/paper/2609.35077/) (2026)

## Abstract (arXiv)

LLM-based Search Engines (LLM-SEs) introduces a new paradigm for information seeking. Unlike Traditional Search Engines (TSEs) (e.g., Google), these systems summarize results, often providing limited citation transparency. The implications of this shift remain largely unexplored, yet raises key questions regarding trust and transparency. In this paper, we present a large-scale empirical study of LLM-SEs, analyzing 55,936 queries and the corresponding search results across six LLM-SEs and two TSEs. We confirm that LLM-SEs cites domain resources with greater diversity than TSEs. Indeed, 37% of domains are unique to LLM-SEs. However, certain risks still persist: LLM-SEs do not outperform TSEs in credibility, political neutrality and safety metrics. Finally, to understand the selection criteria of LLM-SEs, we perform a feature-based analysis to identify key factors influencing source choice. Our findings provide actionable insights for end users, website owners, and developers.