8 citations · 49 across the 40 of their papers we have counts for
7 papers · 1 filter
DecompSR: A dataset for decomposed analyses of compositional multihop spatial reasoning
Lachlan McPheat, Navdeep Kaur, Robert Blackwell +3
We introduce DecompSR, decomposed spatial reasoning, a large benchmark dataset (over 5m datapoints) and generation framework designed to analyse compositional spatial reasoning abi…
Noise or Nuance: An Investigation Into Useful Information and Filtering For LLM Driven AKBC
Alex Clay, Ernesto Jiménez-Ruiz, Pranava Madhyastha
RAG and fine-tuning are prevalent strategies for improving the quality of LLM outputs. However, in constrained situations, such as that of the 2025 LM-KBC challenge, such technique…
Chart Question Answering from Real-World Analytical Narratives
Maeve Hutchinson, Radu Jianu, Aidan Slingsby +2
We present a new dataset for chart question answering (CQA) constructed from visualization notebooks. The dataset features real-world, multi-view charts paired with natural languag…
Capturing Visualization Design Rationale
Maeve Hutchinson, Radu Jianu, Aidan Slingsby +2
Prior natural language datasets for data visualization have focused on tasks such as visualization literacy assessment, insight generation, and visualization generation from natura…
IYKYK: Using language models to decode extremist cryptolects
Christine de Kock, Arij Riabi, Zeerak Talat +3
Extremist groups develop complex in-group language, also referred to as cryptolects, to exclude or mislead outsiders. We investigate the ability of current language technologies to…
An Empirical Study of Conformal Prediction in LLM with ASP Scaffolds for Robust Reasoning
Navdeep Kaur, Lachlan McPheat, Alessandra Russo +2
In this paper, we examine the use of Conformal Language Modelling (CLM) alongside Answer Set Programming (ASP) to enhance the performance of standard open-weight LLMs on complex mu…
: Semantically Controlled Decoding
Mohammad Albinhassan, Pranava Madhyastha, Alessandra Russo
Ensuring both syntactic and semantic correctness in Large Language Model (LLM) outputs remains a significant challenge, despite being critical for real-world deployment. In this pa…