8 papers
A Balanced Neuro-Symbolic Approach for Commonsense Abductive Logic
Joseph Cotnareanu, Didier Chetelat, Yingxue Zhang +1
Although Large Language Models (LLMs) have demonstrated impressive formal reasoning abilities, they often break down when problems require complex proof planning. One promising app…
InvertiTune: High-Quality Data Synthesis for Cost-Effective Single-Shot Text-to-Knowledge Graph Generation
Faezeh Faez, Marzieh S. Tahaei, Yaochen Hu +4
Large Language Models (LLMs) have revolutionized the ability to understand and generate text, enabling significant progress in automatic knowledge graph construction from text (Tex…
GraphPPD: Posterior Predictive Modelling for Graph-Level Inference
Soumyasundar Pal, Liheng Ma, Amine Natik +2
Accurate modelling and quantification of predictive uncertainty is crucial in deep learning since it allows a model to make safer decisions when the data is ambiguous and facilitat…
Reasoning on a Budget: A Survey of Adaptive and Controllable Test-Time Compute in LLMs
Mohammad Ali Alomrani, Yingxue Zhang, Derek Li +14
Large language models (LLMs) have rapidly progressed into general-purpose agents capable of solving a broad spectrum of tasks. However, current models remain inefficient at reasoni…
Omni-Thinker: Scaling Multi-Task RL in LLMs with Hybrid Reward and Task Scheduling
Derek Li, Jiaming Zhou, Leo Maxime Brunswic +8
The pursuit of general-purpose artificial intelligence depends on large language models (LLMs) that can handle both structured reasoning and open-ended generation. We present Omni-…
InnerThoughts: Disentangling Representations and Predictions in Large Language Models
Didier Chételat, Joseph Cotnareanu, Rylee Thompson +2
Large language models (LLMs) contain substantial factual knowledge which is commonly elicited by multiple-choice question-answering prompts. Internally, such models process the pro…