115 citations · 280 across the 15 of their papers we have counts for
15 papers
ABC implies that Ramanujan's tau function misses almost all primes
David Kurniadi Angdinata, Evan Chen, Chris Cummins +21
Lehmer conjectured that Ramanujan's tau-function never vanishes. In a related direction, a folklore conjecture asserts that infinitely many primes arise as absolute values of Raman…
ProofOptimizer: Training Language Models to Simplify Proofs without Human Demonstrations
Alex Gu, Bartosz Piotrowski, Fabian Gloeckle +2
Neural theorem proving has advanced rapidly in the past year, reaching IMO gold-medalist capabilities and producing formal proofs that span thousands of lines. Although such proofs…
Continual Learning via Sparse Memory Finetuning
Jessy Lin, Luke Zettlemoyer, Gargi Ghosh +4
Modern language models are powerful, but typically static after deployment. A major obstacle to building models that continually learn over time is catastrophic forgetting, where u…
Detecting Prefix Bias in LLM-based Reward Models
Ashwin Kumar, Yuzi He, Aram H. Markosyan +2
Reinforcement Learning with Human Feedback (RLHF) has emerged as a key paradigm for task-specific fine-tuning of language models using human preference data. While numerous publicl…
What I cannot execute, I do not understand: Training and Evaluating LLMs on Program Execution Traces
Jordi Armengol-Estapé, Quentin Carbonneaux, Tianjun Zhang +8
Code generation and understanding are critical capabilities for large language models (LLMs). Thus, most LLMs are pretrained and fine-tuned on code data. However, these datasets ty…
With Great Backbones Comes Great Adversarial Transferability
Erik Arakelyan, Karen Hambardzumyan, Davit Papikyan +4
Advances in self-supervised learning (SSL) for machine vision have improved representation robustness and model performance, giving rise to pre-trained backbones like \emph{ResNet}…
Don't Transform the Code, Code the Transforms: Towards Precise Code Rewriting using LLMs
Chris Cummins, Volker Seeker, Jordi Armengol-Estapé +3
Tools for rewriting, refactoring and optimizing code should be fast and correct. Large language models (LLMs), by their nature, possess neither of these qualities. Yet, there remai…
Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts
Mikayel Samvelyan, Sharath Chandra Raparthy, Andrei Lupu +9
As large language models (LLMs) become increasingly prevalent across many real-world applications, understanding and enhancing their robustness to adversarial attacks is of paramou…
Using Captum to Explain Generative Language Models
Vivek Miglani, Aobo Yang, Aram H. Markosyan +2
Captum is a comprehensive library for model explainability in PyTorch, offering a range of methods from the interpretability literature to enhance users' understanding of PyTorch m…
Identifying and Disentangling Spurious Features in Pretrained Image Representations
Rafayel Darbinyan, Hrayr Harutyunyan, Aram H. Markosyan +1
Neural networks employ spurious correlations in their predictions, resulting in decreased performance when these correlations do not hold. Recent works suggest fixing pretrained re…
Tell Your Story: Task-Oriented Dialogs for Interactive Content Creation
Satwik Kottur, Seungwhan Moon, Aram H. Markosyan +3
People capture photos and videos to relive and share memories of personal significance. Recently, media montages (stories) have become a popular mode of sharing these memories due…
Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models
Kushal Tirumala, Aram H. Markosyan, Luke Zettlemoyer +1
Despite their wide adoption, the underlying training and memorization dynamics of very large language models is not well understood. We empirically study exact memorization in caus…
Investigation of positive streamers by double pulse experiments, effects of repetition rate and gas
S. Nijdam, E. Takahashi, A. H. Markosyan +1
Streamer discharges are often operated in a repetitively pulsed mode and are therefore influenced by species left over from the previous discharge, especially free electrons and io…
High order fluid model for streamer discharges. II. Numerical solution and investigation of planar fronts
A. H. Markosyan, S. Dujko, U. Ebert
The high order fluid model developed in the preceding paper is employed here to study the propagation of negative planar streamer fronts in pure nitrogen. The model consists of the…
High order fluid model for streamer discharges: I. Derivation of model and transport data
S. Dujko, A. H. Markosyan, R. D. White +1
Streamer discharges pose basic problems in plasma physics, as they are very transient, far from equilibrium and have high ionization density gradients; they appear in diverse areas…