3 citations · 4 across the 5 of their papers we have counts for
6 papers
Can we trust LLM Self-Explanations for Entity Resolution?
Tommaso Teofili, Donatella Firmani, Nick Koudas +2
Large Language Models (LLMs) have recently shown strong performance on Entity Resolution (ER). Additionally, akin to their prowess in providing accurate predictions, these models o…
Anserini Gets Dense Retrieval: Integration of Lucene's HNSW Indexes
Xueguang Ma, Tommaso Teofili, Jimmy Lin
Anserini is a Lucene-based toolkit for reproducible information retrieval research in Java that has been gaining traction in the community. It provides retrieval capabilities for b…
Effective Explanations for Entity Resolution Models
Tommaso Teofili, Donatella Firmani, Nick Koudas +3
Entity resolution (ER) aims at matching records that refer to the same real-world entity. Although widely studied for the last 50 years, ER still represents a challenging data mana…
TrustyAI Explainability Toolkit
Rob Geada, Tommaso Teofili, Rui Vieira +2
Artificial intelligence (AI) is becoming increasingly more popular and can be found in workplaces and homes around the world. The decisions made by such "black box" systems are oft…
Lucene for Approximate Nearest-Neighbors Search on Arbitrary Dense Vectors
Tommaso Teofili, Jimmy Lin
We demonstrate three approaches for adapting the open-source Lucene search library to perform approximate nearest-neighbor search on arbitrary dense vectors, using similarity searc…
Affect Enriched Word Embeddings for News Information Retrieval
Tommaso Teofili, Niyati Chhaya
Distributed representations of words have shown to be useful to improve the effectiveness of IR systems in many sub-tasks like query expansion, retrieval and ranking. Algorithms li…