Methodik der Fähigkeitsindizes von Artificial Analysis
Überblick
Die Fähigkeitsindizes von Artificial Analysis messen, wie gut Modelle in bestimmten beruflichen Anwendungsfällen abschneiden, etwa im Rechtswesen, im Gesundheitswesen oder im Finanzbereich.
Wir führen jeden Komponenten-Benchmark unabhängig aus, bevor seine Werte im Index zusammengeführt werden. Wie alle Evaluationsmetriken haben auch Fähigkeitsindizes ihre Grenzen und lassen sich möglicherweise nicht direkt auf jeden Anwendungsfall übertragen. Sie bieten jedoch eine nützliche Zusammenfassung für den Vergleich von Modellen bei den Aufgaben, die für einen bestimmten Bereich relevant sind.
Die zugrunde liegenden Benchmarks sowie ihre jeweilige Durchführung und Bewertung sind in der Methodik für das Intelligenz-Benchmarking dokumentiert.
Aufschlüsselung der Indizes
Jeder Index ist auf einen einzelnen Beruf oder Fachbereich wie Rechtswesen, Gesundheit und Medizin oder Finanzen und Rechnungswesen ausgerichtet. Er gewichtet verschiedene Fähigkeiten danach, wie häufig diese in realen Aufgaben des jeweiligen Bereichs vorkommen.
Unsere Aufgabengewichtungen basieren auf einer an O*NET angelehnten Taxonomie von Arbeitsaktivitäten. Die folgende Tabelle zeigt die Komponenten und Gewichtungen jedes Index.
| Index | Gewichtung | Fähigkeit | Evaluationen | Beschreibung |
|---|---|---|---|---|
| Finance & Accounting Index | 30% | Business Knowledge | AA-Omniscience | Domain recall in accounting, corporate finance, economics, and investments |
| 30% | Agentic Knowledge Work | GDPval-AA v2, AA-Briefcase | Tool use, planning, and multi-step task execution, such as building spreadsheets, running an analysis, or coordinating a workflow | |
| 20% | Reasoning | HLE | Multi-step quantitative and analytic reasoning, used for sensitivity analysis, valuation, and structured problem solving | |
| 10% | Agentic Tool Use | AutomationBench-AA | Completing finance workflows across business apps such as spreadsheets, email, and accounting tools without breaking guardrails | |
| 5% | Long-Context | LCR, GDP.pdf | Reading and reasoning across long financial filings, deal documents, and PDF reports | |
| 5% | Non-Hallucination | AA-Omniscience | Avoiding fabricated figures or citations | |
| Strategy & Ops Index | 30% | Business Knowledge | AA-Omniscience | Working knowledge of business processes, accounting basics, and operational concepts |
| 35% | Agentic Knowledge Work | GDPval-AA v2, AA-Briefcase | Tool use, planning, and orchestrating multi-step office workflows end-to-end | |
| 30% | Agentic Tool Use | AutomationBench-AA | Completing operations, HR, marketing, sales, and support workflows across business apps without breaking guardrails | |
| 5% | Long-Context | LCR, GDP.pdf | Holding context across long threads, policies, records, and PDF documents | |
| Legal Index | 35% | Legal Knowledge | AA-Omniscience | Recall of statutes, doctrines, and procedure across jurisdictions |
| 25% | Agentic Knowledge Work | GDPval-AA v2, AA-Briefcase | Running matter-management workflows, drafting pipelines, and tool-augmented research | |
| 15% | Reasoning | HLE | Multi-step argumentation, statutory interpretation, and weighing conflicting authorities | |
| 10% | Long-Context | LCR, GDP.pdf | Reading and synthesizing across contracts, discovery productions, case-law packets, and PDF filings | |
| 10% | Non-Hallucination | AA-Omniscience | Avoiding fabricated case cites or invented statutes | |
| 5% | Agentic Tool Use | AutomationBench-AA | Completing client support and operations workflows across business apps without breaking guardrails | |
| Healthcare & Medical Index | 30% | Medical & Health Knowledge | AA-Omniscience | Clinical knowledge across diagnosis, pharmacology, and care pathways |
| 25% | Agentic Knowledge Work | GDPval-AA v2, AA-Briefcase | Tool use, planning, and orchestrating EHR/pharmacy workflows end-to-end | |
| 15% | Long-Context Reasoning | MLCR-AA | Synthesising findings across long, fragmented patient records and claims files | |
| 10% | Non-Hallucination | AA-Omniscience | Avoiding fabricated drug interactions, doses, or guidelines | |
| 10% | Reasoning | HLE | Multi-step clinical reasoning across biology and medicine | |
| 10% | Agentic Tool Use | AutomationBench-AA | Completing patient support and operations workflows across business apps without breaking guardrails | |
| Engineering Index | 35% | Engineering Knowledge | AA-Omniscience | Domain recall across civil, electrical, mechanical, and other engineering disciplines |
| 30% | Reasoning | HLE, CritPt | Multi-step quantitative reasoning for derivations, sizing calculations, and design trade-offs | |
| 20% | Agentic Knowledge Work | GDPval-AA v2, AA-Briefcase | Tool use, planning, and multi-step execution of engineering deliverables | |
| 15% | Agentic Terminal Use | Terminal-Bench v4.0 | Operating real terminal environments for builds, scripts, system administration, and debugging | |
| Economics Index | 35% | Economics Knowledge | AA-Omniscience | Recall across micro and macroeconomics, public finance, and markets |
| 35% | Reasoning | HLE | Multi-step quantitative and analytic reasoning for modeling, estimation, and inference | |
| 25% | Agentic Knowledge Work | GDPval-AA v2, AA-Briefcase | Tool use, planning, and multi-step execution of analytical deliverables | |
| 5% | Long-Context Reasoning | LCR | Reading and reasoning across long reports, datasets, and research notes |