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Found 7 papers, 6 papers with code
“No, They Did Not”: Dialogue Response Dynamics in Pre-trained Language Models
A critical component of competence in language is being able to identify relevant components of an utterance and reply appropriately.
MELO: Enhancing Model Editing with Neuron-Indexed Dynamic LoRA
Large language models (LLMs) have shown great success in various Natural Language Processing (NLP) tasks, whist they still need updates after deployment to fix errors or keep pace with the changing knowledge in the world.
Counterfactual reasoning: Testing language models' understanding of hypothetical scenarios
Current pre-trained language models have enabled remarkable improvements in downstream tasks, but it remains difficult to distinguish effects of statistical correlation from more systematic logical reasoning grounded on the understanding of real world.
Counterfactual reasoning: Do language models need world knowledge for causal understanding?
Current pre-trained language models have enabled remarkable improvements in downstream tasks, but it remains difficult to distinguish effects of statistical correlation from more systematic logical reasoning grounded on understanding of the real world.
"No, they did not": Dialogue response dynamics in pre-trained language models
A critical component of competence in language is being able to identify relevant components of an utterance and reply appropriately.
On the Interplay Between Fine-tuning and Composition in Transformers
Here we investigate the impact of fine-tuning on the capacity of contextualized embeddings to capture phrase meaning information beyond lexical content.
Assessing Phrasal Representation and Composition in Transformers
Deep transformer models have pushed performance on NLP tasks to new limits, suggesting sophisticated treatment of complex linguistic inputs, such as phrases.
