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About this paper
Qiang Mao, Han Qin, Robert Neary, Charles Wang, Fusheng Wei, Jianping Zhang, Nathaniel Huber-Fliflet
Researchers at Ankura Consulting Group's Legal Technology & Data Analytics team empirically compared five retrieval methods in Azure AI Search for Retrieval-Augmented Generation (RAG) systems used in legal eDiscovery, finding that retrieval quality significantly impacts LLM accuracy and that optimal performance depends on query type rather than a universally superior method. The study demonstrated method-specific strengths and weaknesses and the role of prompt engineering in mitigating attribution errors.
Key insights
- Many errors often attributed to 'LLM hallucination' are fundamentally rooted in deficiencies and misconfigurations of the underlying retrieval method, rather than solely the LLM itself.
- No single retrieval method universally outperforms others; the optimal choice is contingent on the specific nature of the query and the distribution of information within the dataset.
- Prompt engineering, especially incorporating explicit negative constraints, strongly impacts retrieval behavior and can effectively refine the LLM's output to mitigate misattribution errors.
Summary by alphaXiv.
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