AI Research

Validity Without Ground Truth: What Stated-Preference Economics Offers the Evaluation of Language Models | AI Learning Platform

BenchmarkSafety & AlignmentReasoning

Validity Without Ground Truth: What Stated-Preference Economics Offers the Evaluation of Language Models

Daniel Robert Kling Alexander, Catherine Louise Kling1 min readarXivOct 7, 2026

Key Takeaway for Students

Many of the questions now put to large language models have no correct answer to score against: what a policy is worth, which option a user should choose, how to weigh competing values. Stated-preference economics has faced this problem for decades. It judges survey responses without knowing the true value, through a framework of validity and related concepts: content, construc

Reading in Plain English mode — toggle below to switch

This paper proposes a novel framework for evaluating LLMs in scenarios without ground truth, which is crucial for real-world applications where subjective judgments are common. Understanding this framework helps ML engineers build more robust and trustworthy LLMs for complex decision-making tasks.

Read original on arXiv

Career Insight

high demand

Why it matters

Skills to learn

Design LLM evaluation metrics for subjective tasksApply stated-preference economic concepts to AI evaluationDevelop incentive-compatible LLM promptsAnalyze LLM responses for content and construct validityIntegrate interdisciplinary evaluation methodologies into ML workflows

Interview questions

How would you design an evaluation framework for an LLM tasked with providing policy recommendations where no single 'correct' answer exists?
Explain the concept of 'incentive compatibility' in the context of LLM evaluation and how you would test for it.
Propose a system architecture for continuously evaluating an LLM's performance on subjective tasks, incorporating feedback loops and validity checks without relying on ground truth.

Relevant for ~24 months · generated by the enrichment pipeline