A Comparative Study of Retrieval Methods in Azure AI Search

• URL: http://arxiv.org/abs/2512.08078v1
• Date: Mon, 08 Dec 2025 22:20:02 GMT
• Title: A Comparative Study of Retrieval Methods in Azure AI Search
• Authors: Qiang Mao, Han Qin, Robert Neary, Charles Wang, Fusheng Wei, Jianping Zhang, Nathaniel Huber-Fliflet,
• Abstract summary: This study evaluates retrieval strategies within Microsoft Azure's Retrieval-Augmented Generation framework.<n>We compare the performance of Azure AI Search's keyword, semantic, vector, hybrid, and hybrid-semantic retrieval methods.
• Score: 4.799746336710645
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Increasingly, attorneys are interested in moving beyond keyword and semantic search to improve the efficiency of how they find key information during a document review task. Large language models (LLMs) are now seen as tools that attorneys can use to ask natural language questions of their data during document review to receive accurate and concise answers. This study evaluates retrieval strategies within Microsoft Azure's Retrieval-Augmented Generation (RAG) framework to identify effective approaches for Early Case Assessment (ECA) in eDiscovery. During ECA, legal teams analyze data at the outset of a matter to gain a general understanding of the data and attempt to determine key facts and risks before beginning full-scale review. In this paper, we compare the performance of Azure AI Search's keyword, semantic, vector, hybrid, and hybrid-semantic retrieval methods. We then present the accuracy, relevance, and consistency of each method's AI-generated responses. Legal practitioners can use the results of this study to enhance how they select RAG configurations in the future.

Related papers

• RAC: Retrieval-Augmented Clarification for Faithful Conversational Search [7.0486278653981245]
  We introduce RAC (Retrieval-Augmented Clarification), a framework for generating corpus-faithful clarification questions.<n>After comparing several indexing strategies for retrieval, we fine-tune a large language model to make optimal use of research context.<n>We then apply contrastive preference optimization to favor questions supported by retrieved passages over ungrounded alternatives.
  arXiv  Detail & Related papers  (2026-01-16T19:16:38Z)
• Multi-hop Reasoning via Early Knowledge Alignment [68.28168992785896]
  Early Knowledge Alignment (EKA) aims to align Large Language Models with contextually relevant retrieved knowledge.<n>EKA significantly improves retrieval precision, reduces cascading errors, and enhances both performance and efficiency.<n>EKA proves effective as a versatile, training-free inference strategy that scales seamlessly to large models.
  arXiv  Detail & Related papers  (2025-12-23T08:14:44Z)
• Let the Barbarians In: How AI Can Accelerate Systems Performance Research [80.43506848683633]
  We term this iterative cycle of generation, evaluation, and refinement AI-Driven Research for Systems.<n>We demonstrate that ADRS-generated solutions can match or even outperform human state-of-the-art designs.
  arXiv  Detail & Related papers  (2025-12-16T18:51:23Z)
• Towards Context-aware Reasoning-enhanced Generative Searching in E-commerce [61.03081096959132]
  We propose a context-aware reasoning-enhanced generative search framework for better textbfunderstanding the complicated context.<n>Our approach achieves superior performance compared with strong baselines, validating its effectiveness for search-based recommendation.
  arXiv  Detail & Related papers  (2025-10-19T16:46:11Z)
• Reasoning-enhanced Query Understanding through Decomposition and Interpretation [87.56450566014625]
  ReDI is a Reasoning-enhanced approach for query understanding through Decomposition and Interpretation.<n>We compiled a large-scale dataset of real-world complex queries from a major search engine.<n> Experiments on BRIGHT and BEIR demonstrate that ReDI consistently surpasses strong baselines in both sparse and dense retrieval paradigms.
  arXiv  Detail & Related papers  (2025-09-08T10:58:42Z)
• CoCoNUTS: Concentrating on Content while Neglecting Uninformative Textual Styles for AI-Generated Peer Review Detection [60.52240468810558]
  We introduce CoCoNUTS, a content-oriented benchmark built upon a fine-grained dataset of AI-generated peer reviews.<n>We also develop CoCoDet, an AI review detector via a multi-task learning framework, to achieve more accurate and robust detection of AI involvement in review content.
  arXiv  Detail & Related papers  (2025-08-28T06:03:11Z)
• Augmented Question-guided Retrieval (AQgR) of Indian Case Law with LLM, RAG, and Structured Summaries [0.0]
  This paper proposes the use of Large Language Models (LLMs) to facilitate the retrieval of relevant cases.<n>Our approach combines Retrieval Augmented Generation (RAG) with structured summaries optimized for Indian case law.<n>The system generates targeted legal questions based on factual scenarios to identify relevant case law more effectively.
  arXiv  Detail & Related papers  (2025-07-23T05:24:44Z)
• JudgeRank: Leveraging Large Language Models for Reasoning-Intensive Reranking [81.88787401178378]
  We introduce JudgeRank, a novel agentic reranker that emulates human cognitive processes when assessing document relevance. We evaluate JudgeRank on the reasoning-intensive BRIGHT benchmark, demonstrating substantial performance improvements over first-stage retrieval methods. In addition, JudgeRank performs on par with fine-tuned state-of-the-art rerankers on the popular BEIR benchmark, validating its zero-shot generalization capability.
  arXiv  Detail & Related papers  (2024-10-31T18:43:12Z)
• Judgement Citation Retrieval using Contextual Similarity [0.0]
  We propose a methodology that combines natural language processing (NLP) and machine learning techniques to enhance the organization and utilization of legal case descriptions. Our methodology addresses two primary objectives: unsupervised clustering and supervised citation retrieval. Our methodology achieved an impressive accuracy rate of 90.9%.
  arXiv  Detail & Related papers  (2024-05-28T04:22:28Z)
• Empowering Prior to Court Legal Analysis: A Transparent and Accessible Dataset for Defensive Statement Classification and Interpretation [5.646219481667151]
  This paper introduces a novel dataset tailored for classification of statements made during police interviews, prior to court proceedings. We introduce a fine-tuned DistilBERT model that achieves state-of-the-art performance in distinguishing truthful from deceptive statements. We also present an XAI interface that empowers both legal professionals and non-specialists to interact with and benefit from our system.
  arXiv  Detail & Related papers  (2024-05-17T11:22:27Z)
• Leveraging Cognitive Search Patterns to Enhance Automated Natural Language Retrieval Performance [0.0]
  We show that cognitive reformulation patterns that mimic user search behaviour are highlighted. We formalize the application of these patterns by considering a query conceptual representation. A genetic algorithm-based weighting process allows placing emphasis on terms according to their conceptual role-type.
  arXiv  Detail & Related papers  (2020-04-21T14:13:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.