8 citations · 14 across the 6 of their papers we have counts for
5 papers · 1 filter
A Study into Investigating Temporal Robustness of LLMs
Jonas Wallat, Abdelrahman Abdallah, Adam Jatowt +1
Large Language Models (LLMs) encapsulate a surprising amount of factual world knowledge. However, their performance on temporal questions and historical knowledge is limited becaus…
Correctness is not Faithfulness in RAG Attributions
Jonas Wallat, Maria Heuss, Maarten de Rijke +1
Retrieving relevant context is a common approach to reduce hallucinations and enhance answer reliability. Explicitly citing source documents allows users to verify generated respon…
Temporal Blind Spots in Large Language Models
Jonas Wallat, Adam Jatowt, Avishek Anand
Large language models (LLMs) have recently gained significant attention due to their unparalleled ability to perform various natural language processing tasks. These models, benefi…
GeneMask: Fast Pretraining of Gene Sequences to Enable Few-Shot Learning
Soumyadeep Roy, Jonas Wallat, Sowmya S Sundaram +2
Large-scale language models such as DNABert and LOGO aim to learn optimal gene representations and are trained on the entire Human Reference Genome. However, standard tokenization…
The Effect of Masking Strategies on Knowledge Retention by Language Models
Jonas Wallat, Tianyi Zhang, Avishek Anand
Language models retain a significant amount of world knowledge from their pre-training stage. This allows knowledgeable models to be applied to knowledge-intensive tasks prevalent…