A systematic review of Green AI
With the ever‐growing adoption of artificial intelligence (AI)‐based systems, the carbon
footprint of AI is no longer negligible. AI researchers and practitioners are therefore urged to …
footprint of AI is no longer negligible. AI researchers and practitioners are therefore urged to …
Efficient and effective tree-based and neural learning to rank
As information retrieval researchers, we not only develop algorithmic solutions to hard
problems, but we also insist on a proper, multifaceted evaluation of ideas. The literature on …
problems, but we also insist on a proper, multifaceted evaluation of ideas. The literature on …
The information retrieval experiment platform
We integrate irdatasets, ir_measures, and PyTerrier with TIRA in the Information Retrieval
Experiment Platform (TIREx) to promote more standardized, reproducible, scalable, and …
Experiment Platform (TIREx) to promote more standardized, reproducible, scalable, and …
Bridging the gap between indexing and retrieval for differentiable search index with query generation
The Differentiable Search Index (DSI) is an emerging paradigm for information retrieval.
Unlike traditional retrieval architectures where index and retrieval are two different and …
Unlike traditional retrieval architectures where index and retrieval are two different and …
Doc2Query–: when less is more
Doc2Query—the process of expanding the content of a document before indexing using a
sequence-to-sequence model—has emerged as a prominent technique for improving the …
sequence-to-sequence model—has emerged as a prominent technique for improving the …
An analysis of fusion functions for hybrid retrieval
We study hybrid search in text retrieval where lexical and semantic search are fused
together with the intuition that the two are complementary in how they model relevance. In …
together with the intuition that the two are complementary in how they model relevance. In …
Beyond CO2 emissions: The overlooked impact of water consumption of information retrieval models
As in other fields of artificial intelligence, the information retrieval community has grown
interested in investigating the power consumption associated with neural models …
interested in investigating the power consumption associated with neural models …
A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender Systems
Since the creation of the Web, recommender systems (RSs) have been an indispensable
personalization mechanism in information filtering. Most state-of-the-art RSs primarily …
personalization mechanism in information filtering. Most state-of-the-art RSs primarily …
Efficient neural ranking using forward indexes and lightweight encoders
Dual-encoder-based dense retrieval models have become the standard in IR. They employ
large Transformer-based language models, which are notoriously inefficient in terms of …
large Transformer-based language models, which are notoriously inefficient in terms of …
A quantum annealing instance selection approach for efficient and effective transformer fine-tuning
Deep Learning approaches have become pervasive in recent years due to their ability to
solve complex tasks. However, these models need huge datasets for proper training and …
solve complex tasks. However, these models need huge datasets for proper training and …