12 citations · 26 across the 8 of their papers we have counts for
9 papers
Overview of the TREC 2025 Million Large Language Models track
Evangelos Kanoulas, Panagiotis Eustratiadis, Jamie Callan +5
Agentic AI envisions ecosystems of intelligent agents collaboratively solving complex tasks with minimal human intervention. In such ecosystems, each agent possesses specialized ex…
Agent-centric Information Access
Evangelos Kanoulas, Panagiotis Eustratiadis, Yongkang Li +5
As large language models (LLMs) become more specialized, we envision a future where millions of expert LLMs exist, each trained on proprietary data and excelling in specific domain…
Table Question Answering for Low-resourced Indic Languages
Vaishali Pal, Evangelos Kanoulas, Andrew Yates +1
TableQA is the task of answering questions over tables of structured information, returning individual cells or tables as output. TableQA research has focused primarily on high-res…
QFMTS: Generating Query-Focused Summaries over Multi-Table Inputs
Weijia Zhang, Vaishali Pal, Jia-Hong Huang +2
Table summarization is a crucial task aimed at condensing information from tabular data into concise and comprehensible textual summaries. However, existing approaches often fall s…
MultiTabQA: Generating Tabular Answers for Multi-Table Question Answering
Vaishali Pal, Andrew Yates, Evangelos Kanoulas +1
Recent advances in tabular question answering (QA) with large language models are constrained in their coverage and only answer questions over a single table. However, real-world q…
Parameter-Efficient Sparse Retrievers and Rerankers using Adapters
Vaishali Pal, Carlos Lassance, Hervé Déjean +1
Parameter-Efficient transfer learning with Adapters have been studied in Natural Language Processing (NLP) as an alternative to full fine-tuning. Adapters are memory-efficient and…
Parameter-Efficient Abstractive Question Answering over Tables or Text
Vaishali Pal, Evangelos Kanoulas, Maarten de Rijke
A long-term ambition of information seeking QA systems is to reason over multi-modal contexts and generate natural answers to user queries. Today, memory intensive pre-trained lang…
ConfNet2Seq: Full Length Answer Generation from Spoken Questions
Vaishali Pal, Manish Shrivastava, Laurent Besacier
Conversational and task-oriented dialogue systems aim to interact with the user using natural responses through multi-modal interfaces, such as text or speech. These desired respon…
Modeling ASR Ambiguity for Dialogue State Tracking Using Word Confusion Networks
Vaishali Pal, Fabien Guillot, Manish Shrivastava +2
Spoken dialogue systems typically use a list of top-N ASR hypotheses for inferring the semantic meaning and tracking the state of the dialogue. However ASR graphs, such as confusio…