ReSLLM: large language models are strong resource selectors for federated search

An investigation of prompt variations for zero-shot LLM-based rankers

Corpus subsampling: estimating the effectiveness of neural retrieval models on large corpora

Understanding and Mitigating the Threat of Vec2Text to Dense Retrieval Systems

Searching in Professional Instant Messaging Applications: User Behaviour, Intent, and Pain-points

PromptReps: prompting large language models to generate dense and sparse representations for zero-shot document retrieval

Source-Free Domain-Invariant Performance Prediction

Leveraging LLMs for unsupervised dense retriever ranking

Dense retrieval with continuous explicit feedback for systematic review screening prioritisation

FeB4RAG: evaluating federated search in the context of retrieval augmented generation

A setwise approach for effective and highly efficient zero-shot ranking with large language models

Evaluating generative ad hoc information retrieval

Embark on DenseQuest: a system for selecting the best dense retriever for a custom collection

Large language models based stemming for information retrieval: promises, pitfalls and failures

Revisiting document expansion and filtering for effective first-stage retrieval

CoLAL: Co-learning active learning for text classification

How to Forget Clients in Federated Online Learning to Rank?

Zero-shot generative large language models for systematic review screening automation

Stochastic Featurization for Active Learning

Selecting which dense retriever to use for zero-shot search