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3yr ROI
10-20x
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Talent Scout
H
Hexi Jin
University of California, San Diego
S
Stephen Liu
University of California, San Diego
Y
Yuheng Li
University of California, San Diego
S
Simran Malik
University of California, San Diego
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Founder's Pitch
"SourceBench enhances AI answer credibility by benchmarking web source quality."
Commercial Viability Breakdown
0-10 scaleHigh Potential
2/4 signals
Quick Build
4/4 signals
Series A Potential
3/4 signals
Sources used for this analysis
arXiv Paper
Full-text PDF analysis of the research paper
GitHub Repository
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Analysis model: GPT-4o · Last scored: 4/2/2026
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Why It Matters
As AI becomes an integral tool for information dissemination, the accuracy and quality of its outputs are crucial, especially in domains like finance, health, or law where decisions rely on credible sources.
Product Angle
Build a quality assurance tool that integrates with AI platforms to automatically assess and enhance the quality of web sources cited in AI answers.
Disruption
Could replace naive keyword-based source checking systems with a multi-faceted framework, raising the standard for source credibility in AI responses.
Product Opportunity
The tool can serve enterprises and legal professionals who need to ensure the reliability of AI-generated content. Market potential is significant given the growing use of AI in decision-making roles.
Use Case Idea
A browser extension using SourceBench algorithms to identify and label the quality of sources cited in AI-generated answers for professionals in research and legal fields.
Science
SourceBench evaluates web sources that LLMs cite by using an eight-metric framework that includes relevance, accuracy, objectivity, freshness, and clarity, ensuring holistic source quality rather than just textual relevance.
Method & Eval
The system evaluated 3996 web sources across 12 systems using human-aligned automated evaluation metrics in both content and meta-attributes, aiming for high correlation with manual scoring.
Caveats
The reliance on manual labeling for initial calibration could introduce bias, and there's potential difficulty in scaling across different domains or languages.
Author Intelligence
Hexi Jin
University of California, San Diego
Stephen Liu
University of California, San Diego
Yuheng Li
University of California, San Diego
Simran Malik
University of California, San Diego
Yiying Zhang
University of California, San Diego, GenseeAI Inc.
yiying@ucsd.edu