Search Results for author:
Found 6 papers, 4 papers with code
Can Large Language Models Learn Independent Causal Mechanisms?
We show that such causal constraints can improve out-of-distribution performance on abstract and causal reasoning tasks.
Assessing and Enhancing the Robustness of Large Language Models with Task Structure Variations for Logical Reasoning
Despite their high performance on the original publicly available datasets, we find that all models perform poorly on these newly constructed datasets.
Exploring Iterative Enhancement for Improving Learnersourced Multiple-Choice Question Explanations with Large Language Models
When learnersourcing multiple-choice questions, creating explanations for the solution of a question is a crucial step; it helps other students understand the solution and promotes a deeper understanding of related concepts.
Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning
Combining large language models with logical reasoning enhances their capacity to address problems in a robust and reliable manner.
Input-length-shortening and text generation via attention values
Because of the attention mechanism's high computational cost, transformer models usually have an input-length limitation caused by hardware constraints.
Multi-Step Deductive Reasoning Over Natural Language: An Empirical Study on Out-of-Distribution Generalisation
In our model, reasoning is performed using an iterative memory neural network based on RNN with a gated attention mechanism.
