arXiv:2607.01852v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems use the question-answering capabilities of Large Language Models (LLMs) to access information outside their parameters. We evaluate if cluster-based semantic chunking improves retrieval and answer quality compared to fixed-size and recursive chunking evaluating on long, structured academic theses using the Retrieval Augmented Generation Assessment (RAGAs) framework. RAGAs based faithfulness shows limited reliability in this setup. Performance on fixed versus document specific questions varied substan
The rapid deployment and scaling of Retrieval-Augmented Generation (RAG) systems in AI makes research into their foundational components, like chunking strategies, immediately relevant for improving their performance and reliability.
Improving RAG system performance, particularly in accurately processing complex academic texts, is crucial for developing more reliable and trustworthy AI applications in research, education, and knowledge work.
Optimized chunking strategies could lead to more robust and accurate RAG systems, enhancing the utility of LLMs for information retrieval and question-answering in specialized domains beyond general conversation.
- · AI developers
- · Research institutions
- · Knowledge workers
- · RAG system providers
- · Legacy search engines
- · Inefficient RAG implementations
Improved RAG system accuracy and efficiency in handling complex information.
Accelerated research and development through more effective AI-powered knowledge retrieval.
Enhanced trust in AI systems for critical functions, potentially leading to broader adoption in regulated industries.
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Read at arXiv cs.CL