Evaluation of Semantic Search and its Role in Retrieved-Augmented-Generation (RAG) for Arabic Language

• URL: http://arxiv.org/abs/2403.18350v2
• Date: Thu, 30 May 2024 12:16:39 GMT
• Title: Evaluation of Semantic Search and its Role in Retrieved-Augmented-Generation (RAG) for Arabic Language
• Authors: Ali Mahboub, Muhy Eddin Za'ter, Bashar Al-Rfooh, Yazan Estaitia, Adnan Jaljuli, Asma Hakouz,
• Abstract summary: This paper endeavors to establish a straightforward yet potent benchmark for semantic search in Arabic. To precisely evaluate the effectiveness of these metrics and the dataset, we conduct our assessment of semantic search within the framework of retrieval augmented generation (RAG)
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The latest advancements in machine learning and deep learning have brought forth the concept of semantic similarity, which has proven immensely beneficial in multiple applications and has largely replaced keyword search. However, evaluating semantic similarity and conducting searches for a specific query across various documents continue to be a complicated task. This complexity is due to the multifaceted nature of the task, the lack of standard benchmarks, whereas these challenges are further amplified for Arabic language. This paper endeavors to establish a straightforward yet potent benchmark for semantic search in Arabic. Moreover, to precisely evaluate the effectiveness of these metrics and the dataset, we conduct our assessment of semantic search within the framework of retrieval augmented generation (RAG).

Related papers

• CodeXEmbed: A Generalist Embedding Model Family for Multiligual and Multi-task Code Retrieval [103.116634967815]
  We introduce CodeXEmbed, a family of large-scale code embedding models ranging from 400M to 7B parameters. Our novel training pipeline unifies multiple programming languages and transforms various code-related tasks into a common retrieval framework. Our 7B model sets a new state-of-the-art (SOTA) in code retrieval, outperforming the previous leading model, Voyage-Code, by over 20% on CoIR benchmark.
  arXiv  Detail & Related papers  (2024-11-19T16:54:45Z)
• VectorSearch: Enhancing Document Retrieval with Semantic Embeddings and Optimized Search [1.0411820336052784]
  We propose VectorSearch, which leverages advanced algorithms, embeddings, and indexing techniques for refined retrieval. By utilizing innovative multi-vector search operations and encoding searches with advanced language models, our approach significantly improves retrieval accuracy. Experiments on real-world datasets show that VectorSearch outperforms baseline metrics.
  arXiv  Detail & Related papers  (2024-09-25T21:58:08Z)
• Hybrid Semantic Search: Unveiling User Intent Beyond Keywords [0.0]
  This paper addresses the limitations of traditional keyword-based search in understanding user intent. It introduces a novel hybrid search approach that leverages the strengths of non-semantic search engines, Large Language Models (LLMs), and embedding models.
  arXiv  Detail & Related papers  (2024-08-17T16:04:31Z)
• CoIR: A Comprehensive Benchmark for Code Information Retrieval Models [56.691926887209895]
  We present textbfname (textbfInformation textbfRetrieval Benchmark), a robust and comprehensive benchmark specifically designed to assess code retrieval capabilities. name comprises textbften meticulously curated code datasets, spanning textbfeight distinctive retrieval tasks across textbfseven diverse domains. We evaluate nine widely used retrieval models using name, uncovering significant difficulties in performing code retrieval tasks even with state-of-the-art systems.
  arXiv  Detail & Related papers  (2024-07-03T07:58:20Z)
• MINERS: Multilingual Language Models as Semantic Retrievers [23.686762008696547]
  This paper introduces the MINERS, a benchmark designed to evaluate the ability of multilingual language models in semantic retrieval tasks. We create a comprehensive framework to assess the robustness of LMs in retrieving samples across over 200 diverse languages. Our results demonstrate that by solely retrieving semantically similar embeddings yields performance competitive with state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-06-11T16:26:18Z)
• LIST: Learning to Index Spatio-Textual Data for Embedding based Spatial Keyword Queries [53.843367588870585]
  List K-kNN spatial keyword queries (TkQs) return a list of objects based on a ranking function that considers both spatial and textual relevance. There are two key challenges in building an effective and efficient index, i.e., the absence of high-quality labels and the unbalanced results. We develop a novel pseudolabel generation technique to address the two challenges.
  arXiv  Detail & Related papers  (2024-03-12T05:32:33Z)
• A General and Flexible Multi-concept Parsing Framework for Multilingual Semantic Matching [60.51839859852572]
  We propose to resolve the text into multi concepts for multilingual semantic matching to liberate the model from the reliance on NER models. We conduct comprehensive experiments on English datasets QQP and MRPC, and Chinese dataset Medical-SM.
  arXiv  Detail & Related papers  (2024-03-05T13:55:16Z)
• Dense X Retrieval: What Retrieval Granularity Should We Use? [56.90827473115201]
  Often-overlooked design choice is the retrieval unit in which the corpus is indexed, e.g. document, passage, or sentence. We introduce a novel retrieval unit, proposition, for dense retrieval. Experiments reveal that indexing a corpus by fine-grained units such as propositions significantly outperforms passage-level units in retrieval tasks.
  arXiv  Detail & Related papers  (2023-12-11T18:57:35Z)
• Large Search Model: Redefining Search Stack in the Era of LLMs [63.503320030117145]
  We introduce a novel conceptual framework called large search model, which redefines the conventional search stack by unifying search tasks with one large language model (LLM) All tasks are formulated as autoregressive text generation problems, allowing for the customization of tasks through the use of natural language prompts. This proposed framework capitalizes on the strong language understanding and reasoning capabilities of LLMs, offering the potential to enhance search result quality while simultaneously simplifying the existing cumbersome search stack.
  arXiv  Detail & Related papers  (2023-10-23T05:52:09Z)
• 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.