27 citations · 69 across the 20 of their papers we have counts for
20 papers
Controlled Retrieval-augmented Context Evaluation for Long-form RAG
Jia-Huei Ju, Suzan Verberne, Maarten de Rijke +1
Retrieval-augmented generation (RAG) enhances large language models by incorporating context retrieved from external knowledge sources. While the effectiveness of the retrieval mod…
Evaluation of Attribution Bias in Generator-Aware Retrieval-Augmented Large Language Models
Amin Abolghasemi, Leif Azzopardi, Seyyed Hadi Hashemi +2
Attributing answers to source documents is an approach used to enhance the verifiability of a model's output in retrieval augmented generation (RAG). Prior work has mainly focused…
Generative Retrieval with Few-shot Indexing
Arian Askari, Chuan Meng, Mohammad Aliannejadi +3
Existing generative retrieval (GR) methods rely on training-based indexing, which fine-tunes a model to memorise associations between queries and the document identifiers (docids)…
Multi-Step Reasoning with Large Language Models, a Survey
Aske Plaat, Annie Wong, Suzan Verberne +3
Large language models (LLMs) with billions of parameters exhibit in-context learning abilities, enabling few-shot learning on tasks that the model was not specifically trained for.…
Which topics are best represented by science maps? An analysis of clustering effectiveness for citation and text similarity networks
Juan Pablo Bascur, Suzan Verberne, Nees Jan van Eck +1
A science map of topics is a visualization that shows topics identified algorithmically based on the bibliographic metadata of scientific publications. In practice not all topics a…
Measuring Bias in a Ranked List using Term-based Representations
Amin Abolghasemi, Leif Azzopardi, Arian Askari +2
In most recent studies, gender bias in document ranking is evaluated with the NFaiRR metric, which measures bias in a ranked list based on an aggregation over the unbiasedness scor…
CAUSE: Counterfactual Assessment of User Satisfaction Estimation in Task-Oriented Dialogue Systems
Amin Abolghasemi, Zhaochun Ren, Arian Askari +3
An important unexplored aspect in previous work on user satisfaction estimation for Task-Oriented Dialogue (TOD) systems is their evaluation in terms of robustness for the identifi…
Self-seeding and Multi-intent Self-instructing LLMs for Generating Intent-aware Information-Seeking dialogs
Arian Askari, Roxana Petcu, Chuan Meng +4
Identifying user intents in information-seeking dialogs is crucial for a system to meet user's information needs. Intent prediction (IP) is challenging and demands sufficient dialo…
Answer Retrieval in Legal Community Question Answering
Arian Askari, Zihui Yang, Zhaochun Ren +1
The task of answer retrieval in the legal domain aims to help users to seek relevant legal advice from massive amounts of professional responses. Two main challenges hinder applyin…
Generating Synthetic Documents for Cross-Encoder Re-Rankers: A Comparative Study of ChatGPT and Human Experts
Arian Askari, Mohammad Aliannejadi, Evangelos Kanoulas +1
We investigate the usefulness of generative Large Language Models (LLMs) in generating training data for cross-encoder re-rankers in a novel direction: generating synthetic documen…
Retrieval for Extremely Long Queries and Documents with RPRS: a Highly Efficient and Effective Transformer-based Re-Ranker
Arian Askari, Suzan Verberne, Amin Abolghasemi +2
Retrieval with extremely long queries and documents is a well-known and challenging task in information retrieval and is commonly known as Query-by-Document (QBD) retrieval. Specif…
Injecting the BM25 Score as Text Improves BERT-Based Re-rankers
Arian Askari, Amin Abolghasemi, Gabriella Pasi +2
In this paper we propose a novel approach for combining first-stage lexical retrieval models and Transformer-based re-rankers: we inject the relevance score of the lexical model as…
On the Interpolation of Contextualized Term-based Ranking with BM25 for Query-by-Example Retrieval
Amin Abolghasemi, Arian Askari, Suzan Verberne
Term-based ranking with pre-trained transformer-based language models has recently gained attention as they bring the contextualization power of transformer models into the highly…
Academic information retrieval using citation clusters: In-depth evaluation based on systematic reviews
Juan Pablo Bascur, Suzan Verberne, Nees Jan van Eck +1
The field of scientometrics has shown the power of citation-based clusters for literature analysis, yet this technique has barely been used for information retrieval tasks. This wo…
LeiBi@COLIEE 2022: Aggregating Tuned Lexical Models with a Cluster-driven BERT-based Model for Case Law Retrieval
Arian Askari, Georgios Peikos, Gabriella Pasi +1
This paper summarizes our approaches submitted to the case law retrieval task in the Competition on Legal Information Extraction/Entailment (COLIEE) 2022. Our methodology consists…
Improving BERT-based Query-by-Document Retrieval with Multi-Task Optimization
Amin Abolghasemi, Suzan Verberne, Leif Azzopardi
Query-by-document (QBD) retrieval is an Information Retrieval task in which a seed document acts as the query and the goal is to retrieve related documents -- it is particular comm…
Expert Finding in Legal Community Question Answering
Arian Askari, Suzan Verberne, Gabriella Pasi
Expert finding has been well-studied in community question answering (QA) systems in various domains. However, none of these studies addresses expert finding in the legal domain, w…
Breaking BERT: Understanding its Vulnerabilities for Named Entity Recognition through Adversarial Attack
Anne Dirkson, Suzan Verberne, Wessel Kraaij
Both generic and domain-specific BERT models are widely used for natural language processing (NLP) tasks. In this paper we investigate the vulnerability of BERT models to variation…
Proceedings of the 17th Dutch-Belgian Information Retrieval Workshop
Alex Brandsen, Anne Dirkson, Wessel Kraaij +4
This volume contains the papers presented at DIR 2018: 17th Dutch-Belgian Information Retrieval Workshop (DIR) held on November 23, 2018 in Leiden. DIR aims to serve as an internat…
Author-topic profiles for academic search
Suzan Verberne, Arjen P. de Vries, Wessel Kraaij
We implemented and evaluated a two-stage retrieval method for personalized academic search in which the initial search results are re-ranked using an author-topic profile. In acade…