20 citations · 31 across the 14 of their papers we have counts for
8 papers · 1 filter
Inference-Time Reward Hacking in Large Language Models
Hadi Khalaf, Claudio Mayrink Verdun, Alex Oesterling +2
A common paradigm to improve the performance of large language models is optimizing for a reward model. Reward models assign a numerical score to an LLM's output that indicates, fo…
Who Gets Credit or Blame? Attributing Accountability in Modern AI Systems
Shichang Zhang, Hongzhe Du, Jiaqi W. Ma +1
Modern AI systems are typically developed through multiple stages-pretraining, fine-tuning rounds, and subsequent adaptation or alignment, where each stage builds on the previous o…
Evaluating Adversarial Robustness of Concept Representations in Sparse Autoencoders
Aaron J. Li, Suraj Srinivas, Usha Bhalla +1
Sparse autoencoders (SAEs) are commonly used to interpret the internal activations of large language models (LLMs) by mapping them to human-interpretable concept representations. W…
Towards Interpretable Soft Prompts
Oam Patel, Jason Wang, Nikhil Shivakumar Nayak +2
Soft prompts have been popularized as a cheap and easy way to improve task-specific LLM performance beyond few-shot prompts. Despite their origin as an automated prompting method,…
Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability
Shichang Zhang, Tessa Han, Usha Bhalla +1
The increasing complexity of AI systems has made understanding their behavior critical. Numerous interpretability methods have been developed to attribute model behavior to three k…
Towards Unifying Interpretability and Control: Evaluation via Intervention
Usha Bhalla, Suraj Srinivas, Asma Ghandeharioun +1
With the growing complexity and capability of large language models, a need to understand model reasoning has emerged, often motivated by an underlying goal of controlling and alig…
All Roads Lead to Rome? Exploring Representational Similarities Between Latent Spaces of Generative Image Models
Charumathi Badrinath, Usha Bhalla, Alex Oesterling +2
Do different generative image models secretly learn similar underlying representations? We investigate this by measuring the latent space similarity of four different models: VAEs,…
Fair Machine Unlearning: Data Removal while Mitigating Disparities
Alex Oesterling, Jiaqi Ma, Flavio P. Calmon +1
The Right to be Forgotten is a core principle outlined by regulatory frameworks such as the EU's General Data Protection Regulation (GDPR). This principle allows individuals to req…