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Hallucination by Design: How Embedding Models Misunderstand Language
- Text embeddings are crucial for converting words and sentences into numerical vectors to capture their meaning, used widely in NLP systems.
- Despite their prevalent use, there's a lack of comprehensive understanding of how embedding models operate practically, leading to errors and suboptimal user experiences.
- Issues like missing negations, numerical illiteracy, insensitivity to capitalization, and the handling of spaces and references pose significant challenges in embedding model behavior.
- Industries such as Retail, Medical Care, and Finance can benefit significantly from addressing and understanding these embedding model flaws.
- The article highlights various problematic scenarios, including issues with case sensitivity, numerical distinctions, negations, spaces, references, counterfactuals, and ranges.
- The author outlines a testing framework to evaluate how embedding models handle different text variations and emphasizes the importance of real-world testing before deployment.
- Recommendations include building safeguards for critical blind spots, combining multiple techniques, and being transparent with users about the system limitations.
- Understanding that embedding models interpret language through statistical patterns rather than human-like comprehension is crucial for improving system performance.
- Acknowledging and designing around these inherent blind spots in embedding models can lead to more effective and reliable language processing systems.
- Further investigation into other cases of model limitations and their implications will be covered in the subsequent post by the author.
- The article emphasizes the importance of recognizing and addressing the limitations of embedding models to enhance the efficiency and reliability of language processing systems.
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