mlsanityData to 7 Oct 2026

Chats-Grid: An Iterative Retrieval Q&A Optimization Scheme Leveraging Large Model and Retrieval Enhancement Generation in smart grid

arXiv 2502.15583 ↗First posted 21 Feb 2025cs.CLcited by 0

Not on the map. Why?

Abstract (arXiv)

With rapid advancements in artificial intelligence, question-answering (Q&A) systems have become essential in intelligent search engines, virtual assistants, and customer service platforms. However, in dynamic domains like smart grids, conventional retrieval-augmented generation(RAG) Q&A systems face challenges such as inadequate retrieval quality, irrelevant responses, and inefficiencies in handling large-scale, real-time data streams. This paper proposes an optimized iterative retrieval-based Q&A framework called Chats-Grid tailored for smart grid environments. In the pre-retrieval phase, Chats-Grid advanced query expansion ensures comprehensive coverage of diverse data sources, including sensor readings, meter records, and control system parameters. During retrieval, Best Matching 25(BM25) sparse retrieval and BAAI General Embedding(BGE) dense retrieval in Chats-Grid are combined to process vast, heterogeneous datasets effectively. Post-retrieval, a fine-tuned large language model uses prompt engineering to assess relevance, filter irrelevant results, and reorder documents based on contextual accuracy. The model further generates precise, context-aware answers, adhering to quality criteria and employing a self-checking mechanism for enhanced reliability. Experimental results demonstrate Chats-Grid's superiority over state-of-the-art methods in fidelity, contextual recall, relevance, and accuracy by 2.37%, 2.19%, and 3.58% respectively. This framework advances smart grid management by improving decision-making and user interactions, fostering resilient and adaptive smart grid infrastructures.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 29 references that are in the map. First posted on arXiv 21 Feb 2025.

Builds on

19 of 29 references.

References · 19

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  3. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,195
  4. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  5. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
  6. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  7. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  8. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,582
  9. 2021Improving language models by retrieving from trillions of tokenscited by 1,278
  10. 2023In-Context Retrieval-Augmented Language Modelscited by 694
  11. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 508
  12. 2023Active Retrieval Augmented Generationcited by 506
7 more
  1. 2024The Power of Noise: Redefining Retrieval for RAG Systemscited by 275
  2. 2023Lift Yourself Up: Retrieval-augmented Text Generation with Self Memorycited by 103
  3. 2023Augmentation-Adapted Retriever Improves Generalization of Language Models as Generic Plug-Incited by 71
  4. 2023RET-LLM: Towards a General Read-Write Memory for Large Language Modelscited by 64
  5. 2023Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generationcited by 54
  6. 2023Retrieval-Generation Synergy Augmented Large Language Modelscited by 26
  7. 2024BGE Landmark Embedding: A Chunking-Free Embedding Method For Retrieval Augmented Long-Context Large Language Modelscited by 15

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2024RAGONITE: Iterative Retrieval on Induced Databases and Verbalized RDF for Conversational QA over KGs with RAG
  2. 2026ARMOR: Adaptive Retriever Optimization for Low-Resource Telecom Question Answering
  3. 2024Natural Language Interaction with a Household Electricity Knowledge-based Digital Twin
  4. 2025A Graph-based RAG for Energy Efficiency Question Answering
  5. 2025GridCodex: A RAG-Driven AI Framework for Power Grid Code Reasoning and Compliance
  6. 2025KG2QA: Knowledge Graph-enhanced Retrieval-augmented Generation for Communication Standards Question Answering
  7. 2025A Knowledge Graph and a Tripartite Evaluation Framework Make Retrieval-Augmented Generation Scalable and Transparent
  8. 2025Retrieval-Augmented Generation: A Comprehensive Survey of Architectures, Enhancements, and Robustness Frontiers