4 citations · 5 across the 7 of their papers we have counts for
3 papers · 1 filter
Language Agents Meet Causality -- Bridging LLMs and Causal World Models
John Gkountouras, Matthias Lindemann, Phillip Lippe +2
Large Language Models (LLMs) have recently shown great promise in planning and reasoning applications. These tasks demand robust systems, which arguably require a causal understand…
Strengthening Structural Inductive Biases by Pre-training to Perform Syntactic Transformations
Matthias Lindemann, Alexander Koller, Ivan Titov
Models need appropriate inductive biases to effectively learn from small amounts of data and generalize systematically outside of the training distribution. While Transformers are…
Optimising Calls to Large Language Models with Uncertainty-Based Two-Tier Selection
Guillem Ramírez, Alexandra Birch, Ivan Titov
Researchers and practitioners operating on a limited budget face the cost-performance trade-off dilemma. The challenging decision often centers on whether to use a large LLM with b…