2 citations · 2 across the 9 of their papers we have counts for
13 papers
Quantum-Enhanced Inference of Conditional Future Probabilities with Reduced Memory Cost
Jianjun Chen, Akshat Gupta, Chengran Yang +4
Many potential future events have devastating consequences, from the onset of earthquakes to financial black swan events. Accurate predictions of their probability of occurrence ar…
Spatiotemporal quantum advantages for rare-event sampling
Akshat Gupta, Jianjun Chen, Chengran Yang +4
In a world fraught with uncertainty, it behooves us to consider the most disastrous possible outcomes, in order that we can best mitigate against them. Often these are extreme risk…
Green BOA: Determining the environmental break-even point for ML-based data compression
Caterina Doglioni, Thomas Elliott, Akshat Gupta +3
We summarise the outcome of two summer internship projects based at the University of Manchester, focused on the break-even point in terms of environmental sustainability for ML-ba…
Automated Discovery Has No Universally Superior Harness
Akshat Gupta, Jermaine Lei, Alexander Lu +2
Autonomous discovery systems such as OpenEvolve and TTT-Discover are often used as general-purpose harnesses. However, in practice these are composite systems combining several des…
AdaEvolve: Adaptive LLM Driven Zeroth-Order Optimization
Mert Cemri, Shubham Agrawal, Akshat Gupta +9
The paradigm of automated program generation is shifting from one-shot generation to inference-time search, where Large Language Models (LLMs) function as semantic mutation operato…
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…
How Do LLMs Use Their Depth?
Akshat Gupta, Jay Yeung, Gopala Anumanchipalli +1
Growing evidence suggests that large language models do not use their depth uniformly, yet we still lack a fine-grained understanding of their layer-wise prediction dynamics. In th…
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…
Lifelong Knowledge Editing requires Better Regularization
Akshat Gupta, Phudish Prateepamornkul, Maochuan Lu +3
Knowledge editing is a promising way to improve factuality in large language models, but recent studies have shown significant model degradation during sequential editing. In this…