Idea
Benchmark platform measuring large language models' openness to opposing views for AI developers and researchers.
Research Paper
Core Innovation
This paper introduces MillStone, the first benchmark to systematically evaluate how external arguments affect LLM stances on controversial topics. It uniquely measures openness to opposing viewpoints, agreement levels, and argument persuasiveness across multiple leading models. This approach reveals the influence of authoritative sources on LLM behavior, highlighting manipulation risks.
Market Size (TAM)
$2–10B TAM, $1–2B SAM; assumption: growing adoption of LLMs in AI applications requiring trust and transparency.
Potential Customers & Pain Points
- AI Developers Needing Benchmarks for Model Openness
- Researchers Studying Bias and Persuasion in LLMs
- Companies Using LLMs in Search and Retrieval Concerned About Manipulation Risks
Business Model
Subscription-based access to the benchmark platform with tiered pricing for enterprise and research use.
Competitive Landscape
- OpenAI
- Anthropic
- Cohere
Implementation Challenges
- Data Privacy and Ethical Concerns
- Rapidly Evolving LLM Architectures
- Dependence on Authoritative Source Quality
Validation Strategy
- Deploy benchmark on multiple LLMs and publish comparative results
- Partner with AI labs to integrate MillStone in model evaluation pipelines
- Collect user feedback to refine metrics and usability
Research Paper Overview
MillStone: How Open-Minded Are LLMs?
Summary
MillStone is the first benchmark designed to systematically measure how external arguments influence the stances large language models take on controversial issues. It evaluates nine leading LLMs for their openness to opposing viewpoints, agreement levels, and argument persuasiveness. The study finds LLMs generally open-minded but highlights the critical impact of authoritative sources on their stances, underscoring risks of manipulation in LLM-based search and retrieval systems.