27 citations · 69 across the 20 of their papers we have counts for
8 papers · 1 filter
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…