7 papers
Evolutionary Strategies lead to Catastrophic Forgetting in LLMs
Immanuel Abdi, Akshat Gupta, Micah Mok +3
One of the biggest missing capabilities in current AI systems is the ability to learn continuously after deployment. Implementing such continually learning systems have several cha…
BOA Constrictor: A Mamba-based lossless compressor for High Energy Physics data
Akshat Gupta, Caterina Doglioni, Thomas Joseph Elliott
The petabyte-scale data generated annually by High Energy Physics (HEP) experiments like those at the Large Hadron Collider present a significant data storage challenge. Whilst tra…
The Oracle Has Spoken: A Multi-Aspect Evaluation of Dialogue in Pythia
Zixun Chen, Petr Babkin, Akshat Gupta +2
Dialogue is one of the landmark abilities of large language models (LLMs). Despite its ubiquity, few studies actually distinguish specific ingredients underpinning dialogue behavio…
Disentangling Codemixing in Chats: The NUS ABC Codemixed Corpus
Svetlana Churina, Akshat Gupta, Insyirah Mujtahid +1
Code-mixing involves the seamless integration of linguistic elements from multiple languages within a single discourse, reflecting natural multilingual communication patterns. Desp…
Efficient Knowledge Editing via Minimal Precomputation
Akshat Gupta, Maochuan Lu, Thomas Hartvigsen +1
Knowledge editing methods like MEMIT are able to make data and compute efficient updates of factual knowledge by using a single sentence to update facts and their consequences. How…
Norm Growth and Stability Challenges in Localized Sequential Knowledge Editing
Akshat Gupta, Christine Fang, Atahan Ozdemir +4
This study investigates the impact of localized updates to large language models (LLMs), specifically in the context of knowledge editing - a task aimed at incorporating or modifyi…