klotz: information retrieval*

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  1. The article discusses the integration of Large Language Models (LLMs) and search engines, exploring two themes: Search4LLM, which focuses on enhancing LLMs using search engines, and LLM4Search, which looks at improving search engines with LLMs.
  2. This guide explains how to build and use knowledge graphs with R2R. It covers setup, basic example, construction, navigation, querying, visualization, and advanced examples.
  3. This blog post demonstrates how to create a reusable retrieval evaluation dataset using an LLM to judge query-document pairs. It discusses the process, including building a small labeled dataset, aligning LLM judgments with human preferences, and using the LLM to judge a large set of queries and documents.
    2024-07-05 Tags: , , , by klotz
  4. This article discusses the integration of Large Language Models (LLMs) into Vespa, a full-featured search engine and vector database. It explores the benefits of using LLMs for Retrieval-augmented Generation (RAG), demonstrating how Vespa can efficiently retrieve the most relevant data and enrich responses with up-to-date information.
  5. The paper proposes a two-phase framework called TnT-LLM to automate the process of end-to-end label generation and assignment for text mining using large language models, where LLMs produce and refine a label taxonomy iteratively using a zero-shot, multi-stage reasoning approach, and are used as data labelers to yield training samples for lightweight supervised classifiers. The framework is applied to the analysis of user intent and conversational domain for Bing Copilot, achieving accurate and relevant label taxonomies and a favorable balance between accuracy and efficiency for classification at scale.

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