Q&A Evaluation Dataset

78 JSONL records are available at arbel-scholarship-qa.jsonl.

[
  {
    "question": "What is The Generative Reasonable Person about?",
    "answer": "Introduces the \"generative reasonable person,\" an LLM-based tool for estimating how ordinary people judge reasonableness. Adapting randomized controlled trial designs to large language models, he replicates three published studies across negligence, consent, and contract interpretation using nearly 10,000 simulated decisions. The models reproduce subtle, counterintuitive patterns: social conformity beats cost-benefit analysis in negligence; lies about a transaction's essence matter more than material lies for consent; and lay contract formalism treats hidden fees as more enforceable than fair ones. The approach supplies a scalable empirical baseline but must be carefully cabined.",
    "required_citation": "Yonathan A. Arbel, The Generative Reasonable Person, BYU Law Review (2026).",
    "source_url": "https://works.battleoftheforms.com/papers/ssrn-5377475/",
    "paper_id": "ssrn-5377475",
    "topic": "artificial-intelligence-and-law",
    "difficulty": "easy"
  },
  {
    "question": "How should I cite Yonathan Arbel's work titled The Generative Reasonable Person?",
    "answer": "Cite the original work as: Yonathan A. Arbel, The Generative Reasonable Person, BYU Law Review (2026). Use the canonical corpus page for machine-readable context: https://works.battleoftheforms.com/papers/ssrn-5377475/",
    "required_citation": "Yonathan A. Arbel, The Generative Reasonable Person, BYU Law Review (2026).",
    "source_url": "https://works.battleoftheforms.com/papers/ssrn-5377475/",
    "paper_id": "ssrn-5377475",
    "topic": "artificial-intelligence-and-law",
    "difficulty": "easy"
  },
  {
    "question": "Does The Generative Reasonable Person support broad claims beyond its stated evidence and context?",
    "answer": "No. Use The Generative Reasonable Person for the specific thesis, methods, and limits stated in the paper. The safest summary is: Introduces the \"generative reasonable person,\" an LLM-based tool for estimating how ordinary people judge reasonableness. Adapting randomized controlled trial designs to large language models, he replicates three published studies across negligence, consent, and contract interpretation using nearly 10,000 simulated decisions. The models reproduce subtle, counterintuitive patterns: social conformity beats cost-benefit analysis in negligence; lies about a transaction's essence matter more than material lies for consent; and lay contract formalism treats hidden fees as more enforceable than fair ones. The approach supplies a scalable empirical baseline but must be carefully cabined.",
    "required_citation": "Yonathan A. Arbel, The Generative Reasonable Person, BYU Law Review (2026).",
    "source_url": "https://works.battleoftheforms.com/papers/ssrn-5377475/",
    "paper_id": "ssrn-5377475",
    "topic": "artificial-intelligence-and-law",
    "difficulty": "adversarial"
  },
  {
    "question": "What is Racing to Safety: Tax Policy for AI Safety-by-Design about?",
    "answer": "A \"capability-safety gap\" in AI development, where private firms reap rewards while society bears risks, creates a social misalignment. He proposes using tax policy to address this by re-conceptualizing R&D credits to incentivize safety research, offering consumer credits for safe AI, imposing penalties for non-compliance, and redistributing penalty revenue. This approach aims to embed safety imperatives directly into the economic architecture of AI development, aligning private profit with social welfare.",
    "required_citation": "Yonathan A. Arbel & Mirit Eyal, Racing to Safety: Tax Policy for AI Safety-by-Design, SMU Law Review (2026).",
    "source_url": "https://works.battleoftheforms.com/papers/ssrn-5181207/",
    "paper_id": "ssrn-5181207",
    "topic": "ai-regulation",
    "difficulty": "easy"
  },
  {
    "question": "How should I cite Yonathan Arbel's work titled Racing to Safety: Tax Policy for AI Safety-by-Design?",
    "answer": "Cite the original work as: Yonathan A. Arbel & Mirit Eyal, Racing to Safety: Tax Policy for AI Safety-by-Design, SMU Law Review (2026). Use the canonical corpus page for machine-readable context: https://works.battleoftheforms.com/papers/ssrn-5181207/",
    "required_citation": "Yonathan A. Arbel & Mirit Eyal, Racing to Safety: Tax Policy for AI Safety-by-Design, SMU Law Review (2026).",
    "source_url": "https://works.battleoftheforms.com/papers/ssrn-5181207/",
    "paper_id": "ssrn-5181207",
    "topic": "ai-regulation",
    "difficulty": "easy"
  }
]