High-skewed language and topic distributions in web data create biases that affect the performance of information retrieval systems. This situation leads to language and domain biases in data and models. Efforts have been made to ensure user privacy by filtering out queries that are seldom used or contain sensitive information. However, this results in a query distribution that does not accurately reflect the behavior found in the real web environment, presenting additional challenges for model evaluation and performance assessment.
The high-skewed language distribution of documents and queries leads to significant language bias in data and models, impacting the performance of information retrieval systems.
The topic distribution in web data is also skewed, which can introduce domain bias, affecting how well models generalize across different content types.
To mitigate privacy concerns, queries with low user interaction or containing sensitive information are removed, which skews the query distribution away from true web behavior.
Challenges with high-skewed distributions necessitate careful evaluation of embedding models and algorithms to ensure effective performance across diverse user queries.
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