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Exploring the Capabilities and Limitations of Large Language Models in the Electric Energy Sector

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arxiv 2403.09125 v5 pith:3XUSXVXC submitted 2024-03-14 eess.SY cs.SY

classification eess.SYcs.SY
keywords llmslanguagecapabilitiesdirectionselectricenergylargelimitations
verification ladder T0 review T1 audit T2 compute T3 formal
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Large Language Models (LLMs) as chatbots have drawn remarkable attention thanks to their versatile capability in natural language processing as well as in a wide range of tasks. While there has been great enthusiasm towards adopting such foundational model-based artificial intelligence tools in all sectors possible, the capabilities and limitations of such LLMs in improving the operation of the electric energy sector need to be explored, and this article identifies fruitful directions in this regard. Key future research directions include data collection systems for fine-tuning LLMs, embedding power system-specific tools in the LLMs, and retrieval augmented generation (RAG)-based knowledge pool to improve the quality of LLM responses and LLMs in safety-critical use cases.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. LA-RCS: LLM-Agent-Based Robot Control System

    cs.RO 2025-05 reject novelty 4.0 of 10

    LA-RCS reports that a dual-agent LLM system controls a small car robot to complete 18 of 20 self-designed commands with the GPT-4o variant, but the supporting evaluation is inconsistent and not reproducible.

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