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

Set-Encoder: Permutation-Invariant Inter-passage Attention for Listwise Passage Re-ranking with Cross-Encoders

DenseReviewer: A Screening Prioritisation Tool for Systematic Review Based on Dense Retrieval

LLM-VPRF: Large language model based vector pseudo relevance feedback

Rank-DistiLLM: Closing the Effectiveness Gap Between Cross-Encoders and LLMs for Passage Re-ranking

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

Understanding and Mitigating the Threat of Vec2Text to Dense Retrieval Systems

TPRF: A transformer-based pseudo-relevance feedback model for efficient and effective retrieval

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

Source-Free Domain-Invariant Performance Prediction

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

ACM SIGIR 2024 Chairs' Welcome

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