Cognite：2024年工业智能体大语言模型（LLM）与小语言模型（SLM）基准报告（英文版）（10页）.pdf

1、LLM&SLMBenchmark Reportfor Industrial AgentsThe Cognite Atlas AICopyright,Cognite,2024 www.cognite.ai LLM&SLMBenchmark Reportfor Industrial AgentsThe Cognite Atlas AIThe Industrial AI Problem.4The Essential Roleof Natural Language Search.6Results&Analysis:Benchmarking NaturalLanguage Search Modelsfo

2、r Industry-Specifc Tasks.8Industrial Value is Acceleratedby Industrial Agents.12Methodology.14The Industrial AI ProblemLanguage models often generate plausible but incorrect responses,highlighting a key challenge in developing trustworthy AI solutions for industry.This makes rigorous evaluation esse

3、ntial to ensure reliability,accuracy,and effectiveness.Without proper evaluation,its impossible to know if your language model driven solution whether based on prompt engineering,Retrieval Augmented Generation(RAG),GraphRAG(Context Augmented Generation within Cognite Atlas AI),or fine-tuning truly w

4、orks,or how to improve it.General benchmark datasets,while useful,often fall short for specialized tasks.Standard benchmarks like Measuring Massive Multitask Language Understanding(MMLU)assess broad capabilities that may not directly apply to your specific use case.Tailored evaluations,on the other

5、hand,focus on the exact challenges the model is tasked to address.They offer more relevant insights,ensuring that youre measuring practical performance,not just some abstract capabilities.This also reduces the risk of“gaming”the system,a common issue with standardized tests,and provides clearer crit

6、eria for deciding if a new model is worth adopting.The Cognite Atlas AI LLM&SLM Benchmark Report for Industrial Agents addresses the shortcomings of general benchmark datasets by tailoring large language model(LLM)and small language model(SLM)evaluations to focus on specialized industrial tasks,ensu