Summary of Elecbench: a Power Dispatch Evaluation Benchmark For Large Language Models, by Xiyuan Zhou et al.
ElecBench: a Power Dispatch Evaluation Benchmark for Large Language Models
by Xiyuan Zhou, Huan Zhao, Yuheng Cheng, Yuji Cao, Gaoqi Liang, Guolong Liu, Wenxuan Liu, Yan Xu, Junhua Zhao
First submitted to arxiv on: 7 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study introduces “ElecBench”, an evaluation benchmark for large language models (LLMs) in the power sector. ElecBench aims to overcome the shortcomings of existing benchmarks by providing comprehensive coverage of sector-specific scenarios, testing professional knowledge, and enhancing decision-making precision. The framework categorizes scenarios into general knowledge and professional business, with six core performance metrics: factuality, logicality, stability, security, fairness, and expressiveness. To ensure transparency, the study evaluates the performance of eight LLMs across various scenarios and metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to test how well large language models (LLMs) can help with energy problems. These models are good at processing natural language, making logical decisions, and learning from experience. But until now, there hasn’t been a standard way to check if they’re working well in the power industry. The authors create an “ElecBench” test that covers different scenarios like general knowledge and business-specific questions. They also use six main categories to see how well the models do: fact-checking, logical thinking, stability, security, fairness, and expressiveness. |
Keywords
» Artificial intelligence » Precision
