5 papers
Neurosymbolic Embodied Agents
Mohammad Albinhassan, Yuming Feng, Alessandra Russo +1
Language and vision-language models generate plausible embodied plans but do not guarantee executability, as their outputs can violate environment dynamics or act on incorrectly gr…
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…
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…
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…