CompactRAG: Reducing LLM Calls and Token Overhead in Multi-Hop Question Answering
#CompactRAG #Multi-hop RAG #Large Language Models #Token Efficiency #Knowledge Retrieval #arXiv #Entity Grounding
📌 Key Takeaways
- CompactRAG optimizes multi-hop question answering by reducing the frequency of LLM calls.
- The framework decouples offline corpus restructuring from the online reasoning phase to improve speed.
- Existing RAG systems suffer from high token overhead and unstable entity grounding across multiple search steps.
- The new methodology allows for more efficient knowledge retrieval, resulting in lower operational costs for AI systems.
📖 Full Retelling
Researchers specializing in artificial intelligence published a technical paper on arXiv on February 10, 2025, introducing 'CompactRAG,' a new framework designed to solve persistent efficiency issues in multi-hop Retrieval-Augmented Generation (RAG) systems. The team developed this approach to address the high costs and slow response times associated with traditional RAG models, which typically require multiple Large Language Model (LLM) calls to connect fragmented pieces of information across different databases. By reforming how data is processed, the framework aims to streamline complex question-answering tasks that currently burden computational resources.
The core innovation of CompactRAG lies in its ability to decouple offline corpus restructuring from the online reasoning process. Conventional multi-hop RAG systems are often criticized for their 'iterative' nature; they perform a search, reason through the result, and then perform another search for the next 'hop' of information. This back-and-forth cycle leads to excessive token consumption and can cause 'unstable entity grounding,' where the model loses track of specific subjects or facts as it moves between different steps of the logical chain.
To overcome these hurdles, CompactRAG pre-organizes the knowledge base into more manageable and interconnected structures before a user even submits a query. When a complex question is eventually asked, the system can retrieve a comprehensive 'knowledge subgraph' in a single pass. This minimizes the need for repeated LLM interactions, significantly reducing the financial and temporal overhead for organizations deploying these models. This structural shift represents a move toward more sustainable AI deployment, ensuring that knowledge-intensive tasks do not require exponential increases in computing power as they grow in complexity.
🏷️ Themes
Artificial Intelligence, Data Infrastructure, Natural Language Processing
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Original Source
arXiv:2602.05728v1 Announce Type: cross
Abstract: Retrieval-augmented generation (RAG) has become a key paradigm for knowledge-intensive question answering. However, existing multi-hop RAG systems remain inefficient, as they alternate between retrieval and reasoning at each step, resulting in repeated LLM calls, high token consumption, and unstable entity grounding across hops. We propose CompactRAG, a simple yet effective framework that decouples offline corpus restructuring from online reason
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