1 citations · 1 across the 6 of their papers we have counts for
7 papers
Homogenization in Multi-Agent Systems
Prakhar Ganesh, Kyra Wilson, Luca Zappella +4
Multi-agent systems (MAS) leverage interactions between agents to perform complex tasks. Despite their success, we show that these interactions can also lead to homogenization, i.e…
Rethinking Hallucinations: Correctness, Consistency, and Prompt Multiplicity
Prakhar Ganesh, Reza Shokri, Golnoosh Farnadi
Large language models (LLMs) are known to "hallucinate" by generating false or misleading outputs. Hallucinations pose various harms, from erosion of trust to widespread misinforma…
Data as a Lever: A Neighbouring Datasets Perspective on Predictive Multiplicity
Prakhar Ganesh, Hsiang Hsu, Golnoosh Farnadi
Multiplicity, the existence of equally good yet competing models, has received growing attention in recent years. While prior work has emphasized modelling choices, the critical ro…
Say It Another Way: Auditing LLMs with a User-Grounded Automated Paraphrasing Framework
Cléa Chataigner, Rebecca Ma, Prakhar Ganesh +4
Large language models (LLMs) are highly sensitive to subtle changes in prompt phrasing, posing challenges for reliable auditing. Prior methods often apply unconstrained prompt para…
Systemizing Multiplicity: The Curious Case of Arbitrariness in Machine Learning
Prakhar Ganesh, Afaf Taik, Golnoosh Farnadi
Algorithmic modeling relies on limited information in data to extrapolate outcomes for unseen scenarios, often embedding an element of arbitrariness in its decisions. A perspective…
The Cost of Arbitrariness for Individuals: Examining the Legal and Technical Challenges of Model Multiplicity
Prakhar Ganesh, Ihsan Ibrahim Daldaban, Ignacio Cofone +1
Model multiplicity, the phenomenon where multiple models achieve similar performance despite different underlying learned functions, introduces arbitrariness in model selection. Wh…
Towards More Realistic Extraction Attacks: An Adversarial Perspective
Yash More, Prakhar Ganesh, Golnoosh Farnadi
Language models are prone to memorizing their training data, making them vulnerable to extraction attacks. While existing research often examines isolated setups, such as a single…