Overview of Key Sections:
Evaluation Metrics:
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Recall: Measures the ability of the system to retrieve all relevant documents from a given dataset, emphasizing the system's completeness.
Introduction
This report provides a comprehensive evaluation of information retrieval (IR) systems, focusing on the performance of semantic search and its enhancements through combined methodologies. The study employs two core evaluation metrics—Recall, measuring completeness, and Normalized Discounted Cumulative Gain (NDCG)
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, assessing the relevance and ranking
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of
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Constructing Evaluation Dataset:
Question Generation: Involves creating queries from document chunks using an LLM, designed to simulate real-world user inquiries.
Grouping Chunks: Clustering related chunks based on similarity to address queries that span multiple documents.
Ranking Chunks: Prioritizing chunks based on their relevance to the queries using automated scoring.
Critics: Addressing potential limitations and biases introduced by automated question generation and chunk ranking.
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Evaluation Techniques:
Semantic vs. Combined Search: Comparing traditional semantic search capabilities against a combined method that incorporates full-text search.
Re-ranking: Implementing advanced techniques to re-assess the initial search results, enhancing the precision of document retrieval.
Results and Discussion:
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Detailed presentation of findings, focusing on the effectiveness of various search and re-ranking strategies.
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retrieved results. A novel assessment dataset was constructed using large language models (LLMs) to generate queries and rank document chunks, ensuring the dataset closely simulates real-world scenarios. The evaluation benchmarks semantic search against combined search strategies, further exploring reranking techniques to enhance retrieval precision and relevance. Key findings include improvements in recall and ranking through combined search methods and rerankers, highlighting the practical implications and opportunities for refining IR systems. The report concludes with insights on performance trade-offs and areas for further optimization.
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Dive deep into RAG Assessment and Improvement: |
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This section presents research conducted by Unique on the following subjects