With the emergence of various information access systems exhibiting increasing complexity,
there is a critical need for sound and scalable means of automatic evaluation. To address …

In this resource paper, we investigate the usefulness of generative Large Language Models
(LLMs) in generating training data for cross-encoder re-rankers in a novel direction …

Generating synthetic training data based on large language models (LLMs) for ranking
models has gained attention recently. Prior studies use LLMs to build pseudo query …

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 …

In most recent studies, gender bias in document ranking is evaluated with the NFaiRR
metric, which measures bias in a ranked list based on an aggregation over the …

The first edition of the workshop on Generative Information Retrieval (Gen-IR 2023) took
place in July 2023 in a hybrid fashion, co-located with the ACM SIGIR Conference 2023 in …

Digital libraries aim to provide value to users by housing content that is accessible and
searchable. Often such access is afforded through external web search engines. In this …

Quantifying bias in retrieval functions through document retrievability scores is vital for
assessing recall-oriented retrieval systems. However, many studies investigating retrieval …

With tremendous evolution in the internet world, the internet has become a household thing.
Internet users use search engines or personal assistants to request information from the …

This chapter reviews existing regulations on discrimination, provides a multifaceted
categorization of biases and fairness criteria, discusses techniques to measure popularity …