alibaba-multimodal-industrial-ai

alibaba-multimodal-industrial-ai / IndustryBench

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A multi-lingual benchmark for evaluating industrial domain knowledge of LLMs.

28
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100% credibility
Found May 13, 2026 at 28 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

IndustryBench provides a dataset of source-grounded industrial procurement questions and an evaluation script to test large language models' knowledge across multiple languages using official standards.

How It Works

1
🔍 Discover IndustryBench

You come across this benchmark while looking for ways to test how well AI understands real-world industrial products and standards from a research paper or online search.

2
📖 Explore the details

You read about the collection of 2,000+ questions on industrial procurement, grounded in official standards, available in Chinese, English, Russian, and Vietnamese.

3
📥 Get the question set

You easily grab the full set of questions, correct answers, and background facts from the Hugging Face page without needing to download anything extra.

4
🧪 Prepare to test your AI

You save the questions into a simple file and connect your AI service so it can try answering them like an industrial expert.

5
▶️ Run the tests

You launch the evaluation, watching as your AI answers each question, gets scored for accuracy on a 0-3 scale, and checked for safety issues.

6
📊 Check the outcomes

You review the results showing average scores, breakdowns by difficulty and industry, plus any safety flags.

🎉 AI benchmark unlocked

You now have clear insights into your AI's strengths and gaps in industrial knowledge, ready to improve or compare models.

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AI-Generated Review

What is IndustryBench?

IndustryBench is a Python-based, multi-lingual benchmark for evaluating industrial domain knowledge in LLMs through source-grounded procurement QA. It offers 2,049 items drawn from Chinese national standards and product records, with human-aligned questions and answers in Chinese, English, Russian, and Vietnamese. Load the dataset from Hugging Face, export to CSV, and run the evaluation script against any OpenAI-compatible API to generate closed-book responses, score them via an LLM judge (0-3 scale), and apply safety violation checks.

Why is it gaining traction?

Unlike generic LLM benchmarks, IndustryBench ties questions to real GB/T standards and includes difficulty labels across 7 capabilities and 10 industries, enabling precise knowledge probing. The script handles multilingual evals out-of-the-box, supports concurrency and checkpoints for large runs, and outputs detailed CSVs with scores, reasons, and token usage—reproducing the paper's protocol without hassle. Developers appreciate the safety review that zeros scores on norm violations, adding rigor for industrial apps.

Who should use this?

AI engineers benchmarking LLMs for manufacturing, procurement, or supply chain tools. Researchers testing multilingual models on specialized knowledge like equipment specs or safety norms. Teams deploying LLMs in regulated industries needing grounded evals beyond standard NLP benchmarks.

Verdict

Grab it if you're evaluating LLMs for industrial use—solid docs, HF integration, and paper-backed protocol make it immediately usable despite low maturity (28 stars, 1.0% credibility). Skip for general-purpose benchmarking; it's niche but executes flawlessly on its promise.

(198 words)

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