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

2025-02-21

AI Summary

To address low retrieval quality, irrelevant responses, and inefficiency in handling real-time, multi-source data streams within dynamic domains such as smart grids, this paper proposes an iterative Retrieval-Augmented Generation (RAG) framework. The method integrates sparse (BM25) and dense (BGE) retrieval, fine-tuned large language models (LLMs), context-aware re-ranking, and prompt engineering. Key innovations include: (1) a novel pre-retrieval query expansion mechanism; (2) a dual-path hybrid retrieval architecture combining BM25 and BGE; and (3) an LLM-driven post-retrieval re-ranking and self-verification generation module. This end-to-end framework enables controllable, high-quality question answering. Experiments demonstrate statistically significant improvements over state-of-the-art baselines: +2.37% in faithfulness, +2.19% in context recall, and +3.58% in relevance and accuracy. The approach substantially enhances decision-support capabilities and human–AI interaction efficiency in smart grid applications.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Enhances Q&A systems for smart grids
• Improves retrieval quality and response relevance
• Optimizes handling of large-scale real-time data

Innovation

• Iterative retrieval optimization scheme
• Combined BM25 and BGE retrieval
• Fine-tuned large language model