Hallucination by Design: How Embedding Models Misunderstand Language

The semantic richness of human language is not captured by conventional keyword-based text processing techniques. Large tech firms have spent billions on creating ever-more-advanced embedding models. Despite their extensive use, we still don't fully grasp how these embeding models function in practical settings.

The Silent Flaws of Embeddings: Why Your AI Is Getting It Wrong

Unstructured text data has proliferated recently, ranging from technical documentation and scientific literature to social media posts and customer evaluations. Businesses from various sectors are realizing more and more that this text offers priceless insights, but obtaining, arranging, and utilizing this data is difficult.

The semantic richness of human language is not captured by conventional keyword-based text processing techniques, which have shown themselves to be insufficient. For example, a search for "car issues" might not turn up pertinent papers concerning "automobile problems", and a ...