Search Results for author:
Found 6 papers, 5 papers with code
Comparing Explanation Faithfulness between Multilingual and Monolingual Fine-tuned Language Models
Previous studies have explored how different factors affect faithfulness, mainly in the context of monolingual English models.
ReAGent: A Model-agnostic Feature Attribution Method for Generative Language Models
Our method updates the token importance distribution in a recursive manner.
Investigating Hallucinations in Pruned Large Language Models for Abstractive Summarization
Despite the remarkable performance of generative large language models (LLMs) on abstractive summarization, they face two significant challenges: their considerable size and tendency to hallucinate.
Incorporating Attribution Importance for Improving Faithfulness Metrics
Widely used faithfulness metrics, such as sufficiency and comprehensiveness use a hard erasure criterion, i. e. entirely removing or retaining the top most important tokens ranked by a given FA and observing the changes in predictive likelihood.
On the Impact of Temporal Concept Drift on Model Explanations
Explanation faithfulness of model predictions in natural language processing is typically evaluated on held-out data from the same temporal distribution as the training data (i. e. synchronous settings).
SS-BERT: Mitigating Identity Terms Bias in Toxic Comment Classification by Utilising the Notion of "Subjectivity" and "Identity Terms"
Toxic comment classification models are often found biased toward identity terms which are terms characterizing a specific group of people such as "Muslim" and "black".
