10 citations · 38 across the 45 of their papers we have counts for
47 papers
Two routes to the Hadamard condition
Eleanor March
In quantum field theory on curved spacetime, the Hadamard condition is often presented as a necessary condition on 'physically reasonable', 'vacuum-like' states of linear quantum f…
Logit-Contribution Scoring Identifies Non-Literal Retrieval Heads
Aryo Pradipta Gema, Beatrice Alex, Pasquale Minervini
In long-context use, large language models frequently synthesize answers from the meaning of a relevant context span rather than literally copy-pasting them. Identifying which atte…
Rethinking the Harmonic Loss via Non-Euclidean Distance Layers
Maxwell Miller-Golub, Collin Coil, Kamil Faber +4
Cross-entropy loss has long been the standard choice for training deep neural networks, yet it suffers from interpretability limitations, unbounded weight growth, and inefficiencie…
VLM-RobustBench: A Comprehensive Benchmark for Robustness of Vision-Language Models
Rohit Saxena, Alessandro Suglia, Pasquale Minervini
Vision-language models (VLMs) achieve strong performance on standard, high-quality datasets, but we still do not fully understand how they perform under real-world image distortion…
Enhancing Long Document Long Form Summarisation with Self-Planning
Xiaotang Du, Rohit Saxena, Laura Perez-Beltrachini +2
We introduce a novel approach for long context summarisation, highlight-guided generation, that leverages sentence-level information as a content plan to improve the traceability a…
OpenSIR: Open-Ended Self-Improving Reasoner
Wai-Chung Kwan, Joshua Ong Jun Leang, Pavlos Vougiouklis +3
Recent advances in large language model (LLM) reasoning through reinforcement learning rely on annotated datasets for verifiable rewards, which may limit models' ability to surpass…
Universal Properties of Activation Sparsity in Modern Large Language Models
Filip Szatkowski, Patryk Będkowski, Alessio Devoto +5
Activation sparsity is an intriguing property of deep neural networks that has been extensively studied in ReLU-based models, due to its advantages for efficiency, robustness, and…
PiCSAR: Probabilistic Confidence Selection And Ranking for Reasoning Chains
Joshua Ong Jun Leang, Zheng Zhao, Aryo Pradipta Gema +7
Best-of-n sampling improves the accuracy of large language models (LLMs) and large reasoning models (LRMs) by generating multiple candidate solutions and selecting the one with the…
Neurosymbolic Reasoning Shortcuts under the Independence Assumption
Emile van Krieken, Pasquale Minervini, Edoardo Ponti +1
The ubiquitous independence assumption among symbolic concepts in neurosymbolic (NeSy) predictors is a convenient simplification: NeSy predictors use it to speed up probabilistic r…
Inverse Scaling in Test-Time Compute
Aryo Pradipta Gema, Alexander Hägele, Runjin Chen +11
We construct evaluation tasks where extending the reasoning length of Large Reasoning Models (LRMs) deteriorates performance, exhibiting an inverse scaling relationship between tes…
Neurosymbolic Diffusion Models
Emile van Krieken, Pasquale Minervini, Edoardo Ponti +1
Neurosymbolic (NeSy) predictors combine neural perception with symbolic reasoning to solve tasks like visual reasoning. However, standard NeSy predictors assume conditional indepen…
GRADA: Graph-based Reranking against Adversarial Documents Attack
Jingjie Zheng, Aryo Pradipta Gema, Giwon Hong +4
Retrieval Augmented Generation (RAG) frameworks improve the accuracy of large language models (LLMs) by integrating external knowledge from retrieved documents, thereby overcoming…
An Analysis of Decoding Methods for LLM-based Agents for Faithful Multi-Hop Question Answering
Alexander Murphy, Mohd Sanad Zaki Rizvi, Aden Haussmann +4
Large Language Models (LLMs) frequently produce factually inaccurate outputs - a phenomenon known as hallucination - which limits their accuracy in knowledge-intensive NLP tasks. R…
Lost in Time: Clock and Calendar Understanding Challenges in Multimodal LLMs
Rohit Saxena, Aryo Pradipta Gema, Pasquale Minervini
Understanding time from visual representations is a fundamental cognitive skill, yet it remains a challenge for multimodal large language models (MLLMs). In this work, we investiga…
Q-Filters: Leveraging QK Geometry for Efficient KV Cache Compression
Nathan Godey, Alessio Devoto, Yu Zhao +4
Autoregressive language models rely on a Key-Value (KV) Cache, which avoids re-computing past hidden states during generation, making it faster. As model sizes and context lengths…
PosterSum: A Multimodal Benchmark for Scientific Poster Summarization
Rohit Saxena, Pasquale Minervini, Frank Keller
Generating accurate and concise textual summaries from multimodal documents is challenging, especially when dealing with visually complex content like scientific posters. We introd…
Self-Training Large Language Models for Tool-Use Without Demonstrations
Ne Luo, Aryo Pradipta Gema, Xuanli He +3
Large language models (LLMs) remain prone to factual inaccuracies and computational errors, including hallucinations and mistakes in mathematical reasoning. Recent work augmented L…
With Great Backbones Comes Great Adversarial Transferability
Erik Arakelyan, Karen Hambardzumyan, Davit Papikyan +4
Advances in self-supervised learning (SSL) for machine vision have improved representation robustness and model performance, giving rise to pre-trained backbones like \emph{ResNet}…
When Can Proxies Improve the Sample Complexity of Preference Learning?
Yuchen Zhu, Daniel Augusto de Souza, Zhengyan Shi +4
We address the problem of reward hacking, where maximising a proxy reward does not necessarily increase the true reward. This is a key concern for Large Language Models (LLMs), as…
Mixtures of In-Context Learners
Giwon Hong, Emile van Krieken, Edoardo Ponti +2
In-context learning (ICL) adapts LLMs by providing demonstrations without fine-tuning the model parameters; however, it does not differentiate between demonstrations and quadratica…
Aligning Generalisation Between Humans and Machines
Filip Ilievski, Barbara Hammer, Frank van Harmelen +22
Recent advances in AI -- including generative approaches -- have resulted in technology that can support humans in scientific discovery and forming decisions, but may also disrupt…
DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate Hallucinations
Aryo Pradipta Gema, Chen Jin, Ahmed Abdulaal +5
Large Language Models (LLMs) often hallucinate, producing unfaithful or factually incorrect outputs by misrepresenting the provided context or incorrectly recalling internal knowle…
Unveiling and Consulting Core Experts in Retrieval-Augmented MoE-based LLMs
Xin Zhou, Ping Nie, Yiwen Guo +7
Retrieval-Augmented Generation (RAG) significantly improved the ability of Large Language Models (LLMs) to solve knowledge-intensive tasks. While existing research seeks to enhance…
An Auditing Test To Detect Behavioral Shift in Language Models
Leo Richter, Xuanli He, Pasquale Minervini +1
As language models (LMs) approach human-level performance, a comprehensive understanding of their behavior becomes crucial. This includes evaluating capabilities, biases, task perf…