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Found 6 papers, 2 papers with code
Do Smaller Language Models Answer Contextualised Questions Through Memorisation Or Generalisation?
We train two Language Models in a multitask fashion whereby the second model differs from the first only in that it has two additional datasets added to the training regime that are designed to impart simple numerical reasoning strategies of a sort known to improve performance on some of our evaluation datasets but not on others.
Answering Unseen Questions With Smaller Language Models Using Rationale Generation and Dense Retrieval
When provided with sufficient explanatory context, smaller Language Models have been shown to exhibit strong reasoning ability on challenging short-answer question-answering tasks where the questions are unseen in training.
Teaching Smaller Language Models To Generalise To Unseen Compositional Questions
We equip a smaller Language Model to generalise to answering challenging compositional questions that have not been seen in training.
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.
Relating Blindsight and AI: A Review
If we assume that artificial networks have no form of visual experience, then deficits caused by blindsight give us insights into the processes occurring within visual experience that we can incorporate into artificial neural networks.
