13 citations · 13 across the 11 of their papers we have counts for
5 papers · 1 filter
Taxonomy, Opportunities, and Challenges of Representation Engineering for Large Language Models
Jan Wehner, Sahar Abdelnabi, Daniel Tan +2
Representation Engineering (RepE) is a novel paradigm for controlling the behavior of LLMs. Unlike traditional approaches that modify inputs or fine-tune the model, RepE directly m…
COMIX: Compositional Explanations using Prototypes
Sarath Sivaprasad, Dmitry Kangin, Plamen Angelov +1
Aligning machine representations with human understanding is key to improving interpretability of machine learning (ML) models. When classifying a new image, humans often explain t…
Context-Aware Reasoning On Parametric Knowledge for Inferring Causal Variables
Ivaxi Sheth, Sahar Abdelnabi, Mario Fritz
Scientific discovery catalyzes human intellectual advances, driven by the cycle of hypothesis generation, experimental design, evaluation, and assumption refinement. Central to thi…
Can LLMs Separate Instructions From Data? And What Do We Even Mean By That?
Egor Zverev, Sahar Abdelnabi, Soroush Tabesh +2
Instruction-tuned Large Language Models (LLMs) show impressive results in numerous practical applications, but they lack essential safety features that are common in other areas of…
Don't Miss Out on Novelty: Importance of Novel Features for Deep Anomaly Detection
Sarath Sivaprasad, Mario Fritz
Anomaly Detection (AD) is a critical task that involves identifying observations that do not conform to a learned model of normality. Prior work in deep AD is predominantly based o…