📅 2025-02-20 — Session: Enhanced FAISS Retrieval and Experimental Design Insights
🕒 14:50–15:30
🏷️ Labels: FAISS, Vectorstore, Experimental Design, Retrieval, Deep Learning
📂 Project: Dev
Session Goal
The session aimed to analyze and improve the effectiveness of vectorstore retrievers, particularly focusing on FAISS, and to explore key concepts in experimental design.
Key Activities
- Analysis of Vectorstore Retriever Matching: Evaluated how FAISS and embeddings match passages to queries about deep learning models, such as BERT and Wav2Vec 2.0, based on semantic overlaps.
- Mismatch Analysis: Investigated a mismatch between a deep learning query and a statistical inference passage, noting vocabulary overlap and similarity scoring issues.
- Experimental Design Principles: Introduced fundamental concepts in experimental design, including ANOVA and types of experimental designs.
- FAISS Optimization: Provided recommendations for improving FAISS retrieval quality, including index type selection and query refinements.
- Addressing Length Mismatches: Discussed challenges with length differences in FAISS retrieval and proposed mitigation strategies.
- Best Practices for Search Engines: Outlined best practices for quote finders and paragraph search engines, emphasizing precision and advanced techniques.
Achievements
- Clarified the role of semantic and contextual overlaps in vectorstore retrieval.
- Identified specific improvements for FAISS retrieval setup and strategies for handling length mismatches.
- Provided a comprehensive overview of experimental design principles applicable to statistical analysis.
Pending Tasks
- Implement the recommended FAISS improvements and evaluate their impact on retrieval accuracy.