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Found 13 papers, 9 papers with code
Set-Encoder: Permutation-Invariant Inter-Passage Attention for Listwise Passage Re-Ranking with Cross-Encoders
Cross-encoders are effective passage re-rankers.
Analyzing Adversarial Attacks on Sequence-to-Sequence Relevance Models
Modern sequence-to-sequence relevance models like monoT5 can effectively capture complex textual interactions between queries and documents through cross-encoding.
Investigating the Effects of Sparse Attention on Cross-Encoders
Cross-encoders are effective passage and document re-rankers but less efficient than other neural or classic retrieval models.
Evaluating Generative Ad Hoc Information Retrieval
Recent advances in large language models have enabled the development of viable generative information retrieval systems.
The Information Retrieval Experiment Platform
Standardization is achieved when a retrieval approach implements PyTerrier's interfaces and the input and output of an experiment are compatible with ir_datasets and ir_measures.
The Archive Query Log: Mining Millions of Search Result Pages of Hundreds of Search Engines from 25 Years of Web Archives
The Archive Query Log (AQL) is a previously unused, comprehensive query log collected at the Internet Archive over the last 25 years.
The Infinite Index: Information Retrieval on Generative Text-To-Image Models
Conditional generative models such as DALL-E and Stable Diffusion generate images based on a user-defined text, the prompt.
Sparse Pairwise Re-ranking with Pre-trained Transformers
Pairwise re-ranking models predict which of two documents is more relevant to a query and then aggregate a final ranking from such preferences.
How Train-Test Leakage Affects Zero-shot Retrieval
We investigate the impact of this unintended train-test leakage by training neural retrieval models on combinations of a fixed number of MS MARCO / ORCAS queries that are highly similar to the actual test queries and an increasing number of other queries.
Clickbait Spoiling via Question Answering and Passage Retrieval
We introduce and study the task of clickbait spoiling: generating a short text that satisfies the curiosity induced by a clickbait post.
SCAI-QReCC Shared Task on Conversational Question Answering
Search-Oriented Conversational AI (SCAI) is an established venue that regularly puts a spotlight upon the recent work advancing the field of conversational search.
The Impact of Main Content Extraction on Near-Duplicate Detection
Commercial web search engines employ near-duplicate detection to ensure that users see each relevant result only once, albeit the underlying web crawls typically include (near-)duplicates of many web pages.
Towards Axiomatic Explanations for Neural Ranking Models
We investigate whether one can explain the behavior of neural ranking models in terms of their congruence with well understood principles of document ranking by using established theories from axiomatic IR.
