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JOR-Bench evaluates LLM performance in Japanese optimisation

A new set of five Japanese benchmarks, JOR-Bench, has been introduced to evaluate large language models' ability to formulate and solve optimisation problems.

By the Aheadline editorial team·21 juli 2026·2 min read·Source: arXiv cs.CL (NLP/LLM)Verifierad signalAI-generated
JOR-Bench evaluates LLM performance in Japanese optimisation
JOR-Bench evaluates LLM performance in Japanese optimisation
JOR-Bench evaluates LLM performance in Japanese optimisation
By · Policy- & EU-reporter

What happened?

JOR-Bench is a collection of five Japanese-language benchmarks for evaluating the performance of large language models (LLMs) in formulating and solving Operations Research (OR) problems. The benchmark consists of 1,319 problems translated from existing English benchmarks such as IndustryOR, MAMO Complex LP, NL4OPT, OptiBench, and OptMATH. The problems cover linear programming, mixed-integer optimisation, non-linear programming, and combinatorial optimisation.

Key facts

Antal problem1 319
Antal benchmarks inkluderade5
Datum för publicering25 juli 2026
Områden optimeringLinjär, blandad heltal, icke-linjär, kombinatorisk
Antal utvärderade LLM:er7

”We present JOR-Bench, a collection of five Japanese-language benchmarks for evaluating the ability of large language models (LLMs) to formulate and solve operations research (OR) problems.”

— null, null · arXiv

Why it matters

JOR-Bench provides a standardised method for evaluating LLM performance in Operations Research within a Japanese context. It enables comparisons between various models, including multilingual and Japanese-specialised models, in both English and Japanese. This facilitates the development of more capable LLMs that can handle complex optimisation problems in practical applications.

Who is affected?

Researchers, large language model developers, and companies using LLMs for optimisation problems are affected. Specifically, Japanese researchers and companies can benefit from the new benchmarks to assess and improve their models' performance.

What else you should know

JOR-Bench is solver-agnostic and can be used with any optimisation solver or programming language. The evaluation in the study was standardised using the Python interface for OR-Tools.

Frequently asked questions

Quick answers about this story

Vad har hänt?
Ett nytt benchmark vid namn JOR-Bench har presenterats. Det syftar till att utvärdera stora språkmodellers förmåga att formulera och lösa Operations Research-problem på japanska. Det inkluderar 1 319 problem översatta från fem engelska benchmarks.
När hände det?
JOR-Bench presenterades den 25 juli 2026 i en publikation på arXiv.
Varför spelar det roll?
Detta benchmark är viktigt för att kunna mäta och jämföra prestanda hos LLM:er när det gäller komplexa optimeringsproblem på japanska. Det kan driva fram utvecklingen av mer kapabla AI-modeller för praktiska tillämpningar.
Vilka typer av problem täcker JOR-Bench?
JOR-Bench täcker problem inom linjär programmering, blandad heltalsoptimering, icke-linjär programmering och kombinatorisk optimering.
Nyheten handlar om
JOR-BenchOperations ResearchLLMIndustryORMAMO Complex LPNL4OPTOptiBenchOptMATHarXivOR-Tools
Original source
arXiv cs.CL (NLP/LLM)·arxiv.org

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Topics

#AI-benchmarking#arXiv.org#Large Language Models (LLMs)#Natural Language Processing (NLP)
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