8 citations · 49 across the 40 of their papers we have counts for
34 papers · 1 filter
InSight: A Benchmark for Agentic Claim Verification in Interactive Visualizations
Maeve Hutchinson, Syed Mahbubul Huq, Mohammad Albinhassan +3
Vision Language Models have demonstrated remarkable proficiency in interpreting static visual artifacts, but modern data analysis is inherently dynamic, requiring the active interr…
First Make It Playable, Then Make It Good: Staged Interaction Learning for Small Dialogue-Game Agents
Syed Mahbubul Huq, Pranava Madhyastha
We present Qwen-GuidePlay-2B, a 2B-parameter language model for dialogue-game interaction. We fine-tune Qwen3.5-2B using three steps: a) SFT on only successful game trajectories fr…
Knowing Before Answering: Decoding Language Models for Reliable RAG
Syed Mahbubul Huq, Christopher Child, Tillman Weyde +1
In Retrieval-Augmented Generation (RAG), retrieval may provide insufficient or conflicting information needed to answer a question. The system should not only know when to answer b…
A Cognitively Grounded Bayesian Framework for Misinformation Susceptibility
Pranava Madhyastha
In this (work in progress) paper, we present Bounded Pragmatic Listener (or BPL), a cognitively grounded Bayesian framework for modelling susceptibility to information disorder. BP…
Working Memory Constraints Scaffold Learning in Transformers under Data Scarcity
Pranava Madhyastha, Dagmar Adamcova
We investigate the integration of human-like working memory constraints into the Transformer architecture and implement several cognitively inspired attention variants, including f…
Learning and Enforcing Context-Sensitive Control for LLMs
Mohammad Albinhassan, Pranava Madhyastha, Mark Law +1
Controlling the output of Large Language Models (LLMs) through context-sensitive constraints has emerged as a promising approach to overcome the limitations of Context-Free Grammar…
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…
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…
Towards Robust Aspect-based Sentiment Analysis through Non-counterfactual Augmentations
Xinyu Liu, Yan Ding, Kaikai An +4
While state-of-the-art NLP models have demonstrated excellent performance for aspect based sentiment analysis (ABSA), substantial evidence has been presented on their lack of robus…
Are words equally surprising in audio and audio-visual comprehension?
Pranava Madhyastha, Ye Zhang, Gabriella Vigliocco
We report a controlled study investigating the effect of visual information (i.e., seeing the speaker) on spoken language comprehension. We compare the ERP signature (N400) associa…
Towards preserving word order importance through Forced Invalidation
Hadeel Al-Negheimish, Pranava Madhyastha, Alessandra Russo
Large pre-trained language models such as BERT have been widely used as a framework for natural language understanding (NLU) tasks. However, recent findings have revealed that pre-…
Towards a Unified Model for Generating Answers and Explanations in Visual Question Answering
Chenxi Whitehouse, Tillman Weyde, Pranava Madhyastha
The field of visual question answering (VQA) has recently seen a surge in research focused on providing explanations for predicted answers. However, current systems mostly rely on…
Evaluation of Fake News Detection with Knowledge-Enhanced Language Models
Chenxi Whitehouse, Tillman Weyde, Pranava Madhyastha +1
Recent advances in fake news detection have exploited the success of large-scale pre-trained language models (PLMs). The predominant state-of-the-art approaches are based on fine-t…
Numerical reasoning in machine reading comprehension tasks: are we there yet?
Hadeel Al-Negheimish, Pranava Madhyastha, Alessandra Russo
Numerical reasoning based machine reading comprehension is a task that involves reading comprehension along with using arithmetic operations such as addition, subtraction, sorting,…
BERTGEN: Multi-task Generation through BERT
Faidon Mitzalis, Ozan Caglayan, Pranava Madhyastha +1
We present BERTGEN, a novel generative, decoder-only model which extends BERT by fusing multimodal and multilingual pretrained models VL-BERT and M-BERT, respectively. BERTGEN is a…
Discrete Reasoning Templates for Natural Language Understanding
Hadeel Al-Negheimish, Pranava Madhyastha, Alessandra Russo
Reasoning about information from multiple parts of a passage to derive an answer is an open challenge for reading-comprehension models. In this paper, we present an approach that r…
MultiSubs: A Large-scale Multimodal and Multilingual Dataset
Josiah Wang, Pranava Madhyastha, Josiel Figueiredo +2
This paper introduces a large-scale multimodal and multilingual dataset that aims to facilitate research on grounding words to images in their contextual usage in language. The dat…
Exploiting Multimodal Reinforcement Learning for Simultaneous Machine Translation
Julia Ive, Andy Mingren Li, Yishu Miao +3
This paper addresses the problem of simultaneous machine translation (SiMT) by exploring two main concepts: (a) adaptive policies to learn a good trade-off between high translation…
Cross-lingual Visual Pre-training for Multimodal Machine Translation
Ozan Caglayan, Menekse Kuyu, Mustafa Sercan Amac +4
Pre-trained language models have been shown to improve performance in many natural language tasks substantially. Although the early focus of such models was single language pre-tra…
Curious Case of Language Generation Evaluation Metrics: A Cautionary Tale
Ozan Caglayan, Pranava Madhyastha, Lucia Specia
Automatic evaluation of language generation systems is a well-studied problem in Natural Language Processing. While novel metrics are proposed every year, a few popular metrics rem…
Simultaneous Machine Translation with Visual Context
Ozan Caglayan, Julia Ive, Veneta Haralampieva +3
Simultaneous machine translation (SiMT) aims to translate a continuous input text stream into another language with the lowest latency and highest quality possible. The translation…