# Overreliance on AI in Information-seeking from Video Content

arXiv: https://arxiv.org/abs/2603.19843 · HTML: https://mlsanity.com/paper/2603.19843/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 99 references that are in the map.
- First posted on arXiv 20 Mar 2026.

## Builds on (most cited first, 12 of 12)

- [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://mlsanity.com/paper/2305.14251/) (2023)
- [Evaluating Verifiability in Generative Search Engines](https://mlsanity.com/paper/2304.09848/) (2023)
- [Factuality Challenges in the Era of Large Language Models](https://mlsanity.com/paper/2310.05189/) (2023)
- [Factuality of Large Language Models: A Survey](https://mlsanity.com/paper/2402.02420/) (2024)
- [Search Arena: Analyzing Search-Augmented LLMs](https://mlsanity.com/paper/2506.05334/) (2025)
- [Empirica: a virtual lab for high-throughput macro-level experiments](https://mlsanity.com/paper/2006.11398/) (2020)
- [The AI Gap: How Socioeconomic Status Affects Language Technology Interactions](https://mlsanity.com/paper/2505.12158/) (2025)
- [Conversational AI increases political knowledge as effectively as self-directed internet search](https://mlsanity.com/paper/2509.05219/) (2025)
- [Women's Participation in Open Source Software: A Survey of the Literature](https://mlsanity.com/paper/2105.08777/) (2021)
- [The Rise of AI Search: Implications for Information Markets and Human Judgement at Scale](https://mlsanity.com/paper/2602.13415/) (2026)
- [Large Language Models as Search Engines: Societal Challenges](https://mlsanity.com/paper/2512.08946/) (2025)
- [Auditing LLM Editorial Bias in News Media Exposure](https://mlsanity.com/paper/2510.27489/) (2025)

## Built on by (most cited first, 1 of 1)

- [WildSEEK: Evaluating Language Models for Information-Seeking](https://mlsanity.com/paper/2608.30683/) (2026)

## Abstract (arXiv)

The ubiquity of multimedia content is reshaping online information spaces, particularly in social media environments. At the same time, search is being rapidly transformed by generative AI, with large language models (LLMs) routinely deployed as intermediaries between users and multimedia content to retrieve and summarize information. Despite their growing influence, the impact of LLM inaccuracies and potential vulnerabilities on multimedia information-seeking tasks remains largely unexplored. We investigate how generative AI affects accuracy, efficiency, and confidence in information retrieval from videos. We conduct an experiment with around 900 participants on 8,000+ video-based information-seeking tasks, comparing behavior across three conditions: (1) access to videos only, (2) access to videos with LLM-based AI assistance, and (3) access to videos with a deceiving AI assistant designed to provide false answers. We find that AI assistance increases accuracy by 3-7% when participants viewed the relevant video segment, and by 27-35% when they did not. Efficiency increases by 10% for short videos and 25% for longer ones. However, participants tend to over-rely on AI outputs, resulting in accuracy drops of up to 32% when interacting with the deceiving AI. Alarmingly, self-reported confidence in answers remains stable across all three conditions. Our findings expose fundamental safety risks in AI-mediated video information retrieval.