Information Retrieval (IR) systems are crucial tools for users to access information, widely
applied in scenarios like search engines, question answering, and recommendation …

Large language models (LLMs) have gained significant attention in various fields but prone
to hallucination, especially in knowledge-intensive (KI) tasks. To address this, retrieval …

As the field of Large Language Models (LLMs) continues to evolve, the context length in
inference is steadily growing. Key-Value Cache (KVCache), a crucial component in LLM …

With the information explosion on the Web, search and recommendation are foundational
infrastructures to satisfying users' information needs. As the two sides of the same coin, both …

Learnable embedding vector is one of the most important applications in machine learning,
and is widely used in various database-related domains. However, the high dimensionality …

The advent of large language models (LLMs) has showcased their efficacy across various
domains, yet they often hallucinate, especially in knowledge-intensive tasks that require …

Generative retrieval, which has demonstrated effectiveness in text-to-text retrieval, utilizes a
sequence-to-sequence model to directly generate candidate identifiers based on natural …

This paper introduces LiNR, LinkedIn's large-scale, GPU-based retrieval system. LiNR
supports a billion-sized index on GPU models. We discuss our experiences and challenges …

The First Search Futures Workshop, in conjunction with the Fourty-sixth European
Conference on Information Retrieval (ECIR) 2024, looked into the future of search to ask …

Generative retrieval for search and recommendation is a promising paradigm for retrieving
items, offering an alternative to traditional methods that depend on external indexes and …