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New ORQA framework tests LLM occupation knowledge; Claude and GPT lead

By PulseAugur Editorial · [1 sources] ·

Researchers have developed ORQA, a novel framework designed to evaluate the occupation-specific knowledge of large language models. This method connects O*NET occupations to authoritative websites, generating source-traceable question-answer pairs. The framework covers 116 occupations and includes 480 questions derived from 187 websites, with a focus on real-world skill relevance. In testing, Claude Opus 4.6, GPT-5.4, and Claude Sonnet 4.6 demonstrated the highest performance, achieving around 58-62% accuracy, while smaller open-weight models performed at 33-41%. AI

IMPACT Establishes a new benchmark for evaluating LLM professional knowledge, highlighting performance disparities across occupations and models.

RANK_REASON The cluster describes a new research paper introducing a framework for evaluating LLM knowledge in professional domains. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New ORQA framework tests LLM occupation knowledge; Claude and GPT lead

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The cluster describes a new research paper introducing a framework for evaluating LLM knowledge in professional domains. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Shreyas Krishnan, Serina Chang, Abhishek Nagaraj ·

    ORQA: An Occupation-Realistic Question and Answer Framework for LLM Professional Knowledge

    arXiv:2609.12366v1 Announce Type: new Abstract: We present ORQA, a method for testing occupation-level knowledge in large language models. Prior methods either map abstract LLM skills to occupations via task definitions or utilize expert knowledge which is difficult to obtain at …

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