A neural semantic matcher for product search uses a custom loss on behavior data, n-gram pooling, and hashing to beat prior methods by 4.7% Recall@100 and 14.5% MAP.
Query Expansion with Locally-Trained Word Embeddings
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abstract
Continuous space word embeddings have received a great deal of attention in the natural language processing and machine learning communities for their ability to model term similarity and other relationships. We study the use of term relatedness in the context of query expansion for ad hoc information retrieval. We demonstrate that word embeddings such as word2vec and GloVe, when trained globally, underperform corpus and query specific embeddings for retrieval tasks. These results suggest that other tasks benefiting from global embeddings may also benefit from local embeddings.
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A literature review that categorizes bias in LLMs, surveys evaluation and mitigation techniques, and discusses ethical implications.
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Semantic Product Search
A neural semantic matcher for product search uses a custom loss on behavior data, n-gram pooling, and hashing to beat prior methods by 4.7% Recall@100 and 14.5% MAP.
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Bias in Large Language Models: Origin, Evaluation, and Mitigation
A literature review that categorizes bias in LLMs, surveys evaluation and mitigation techniques, and discusses ethical implications.