Search Results for author:
Found 7 papers, 6 papers with code
Temporal Blind Spots in Large Language Models
In this study, we aim to investigate the underlying limitations of general-purpose LLMs when deployed for tasks that require a temporal understanding.
GeneMask: Fast Pretraining of Gene Sequences to Enable Few-Shot Learning
Large-scale language models such as DNABert and LOGO aim to learn optimal gene representations and are trained on the entire Human Reference Genome.
The Effect of Masking Strategies on Knowledge Retention by Language Models
To foster reproducibility, the code, as well as the data used in this paper, are openly available.
A Review of the Role of Causality in Developing Trustworthy AI Systems
This review aims to provide the reader with an overview of causal methods that have been developed to improve the trustworthiness of AI models.
Explainable Information Retrieval: A Survey
Explainable information retrieval is an emerging research area aiming to make transparent and trustworthy information retrieval systems.
BERTnesia: Investigating the capture and forgetting of knowledge in BERT
We found that ranking models forget the least and retain more knowledge in their final layer compared to masked language modeling and question-answering.
BERTnesia: Investigating the capture and forgetting of knowledge in BERT
We found that ranking models forget the least and retain more knowledge in their final layer.
