2 citations · 2 across the 5 of their papers we have counts for
5 papers
Reproducing and Extending Causal Insights Into Term Frequency Computation in Neural Rankers
Cile van Marken, Roxana Petcu
Neural ranking models have shown outstanding performance across a variety of tasks, such as document retrieval, re-ranking, question answering and conversational retrieval. However…
Interpreting Multilingual and Document-Length Sensitive Relevance Computations in Neural Retrieval Models through Axiomatic Causal Interventions
Oliver Savolainen, Dur e Najaf Amjad, Roxana Petcu
This reproducibility study analyzes and extends the paper "Axiomatic Causal Interventions for Reverse Engineering Relevance Computation in Neural Retrieval Models," which investiga…
Beyond Reproducibility: Advancing Zero-shot LLM Reranking Efficiency with Setwise Insertion
Jakub Podolak, Leon Peric, Mina Janicijevic +1
This study presents a comprehensive reproducibility and extension analysis of the Setwise prompting methodology for zero-shot ranking with Large Language Models (LLMs), as proposed…
Leveraging Graph Structures to Detect Hallucinations in Large Language Models
Noa Nonkes, Sergei Agaronian, Evangelos Kanoulas +1
Large language models are extensively applied across a wide range of tasks, such as customer support, content creation, educational tutoring, and providing financial guidance. Howe…
Efficient data selection employing Semantic Similarity-based Graph Structures for model training
Roxana Petcu, Subhadeep Maji
Recent developments in natural language processing (NLP) have highlighted the need for substantial amounts of data for models to capture textual information accurately. This raises…