5 papers
Scaling Laws for Embedding Dimension in Information Retrieval
Julian Killingback, Mahta Rafiee, Madine Manas +1
Dense retrieval, which encodes queries and documents into a single dense vector, has become the dominant neural retrieval approach due to its simplicity and compatibility with fast…
Benchmarking Information Retrieval Models on Complex Retrieval Tasks
Julian Killingback, Hamed Zamani
Large language models (LLMs) are incredible and versatile tools for text-based tasks that have enabled countless, previously unimaginable, applications. Retrieval models, in contra…
Scaling Sparse and Dense Retrieval in Decoder-Only LLMs
Hansi Zeng, Julian Killingback, Hamed Zamani
Scaling large language models (LLMs) has shown great potential for improving retrieval model performance; however, previous studies have mainly focused on dense retrieval trained w…
Hypencoder: Hypernetworks for Information Retrieval
Julian Killingback, Hansi Zeng, Hamed Zamani
Existing information retrieval systems are largely constrained by their reliance on vector inner products to assess query-document relevance, which naturally limits the expressiven…
ExPerT: Effective and Explainable Evaluation of Personalized Long-Form Text Generation
Alireza Salemi, Julian Killingback, Hamed Zamani
Evaluating personalized text generated by large language models (LLMs) is challenging, as only the LLM user, i.e., prompt author, can reliably assess the output, but re-engaging th…