146 citations · 173 across the 10 of their papers we have counts for
11 papers
Towards Reliable Vision-Language Models for Autonomous Driving
Manasa Mariam Mammen, Priyanka Mary Mammen, Zafer Kayatas +1
Vision-Language models (VLMs) are increasingly being explored in autonomous driving for tasks such as scene understanding, driving reasoning, decision-making, and end-to-end drivin…
Do Agents Know When They Succeed? Calibrating Agent Confidence from Internal Representations
Priyanka Mary Mammen, Emil Joswin, Srujananjali Medicherla
As agentic systems getting adopted rapidly in safety critical applications, it is vital to measure the confidence associated with the agentic actions. In comparison to the traditio…
A Mechanistic View of Authority Hierarchy in LLM Sycophancy
Emil Joswin, Srujananjali Medicherla, Priyanka Mary Mammen
Authority bias poses a critical safety concern in language models: models systematically prioritize social cues from authority figures over factual consistency, swaying their answe…
Who Endorsed It? Measuring Authority Bias Across Expertise Levels in Language Models
Priyanka Mary Mammen, Emil Joswin, Shankar Venkitachalam
Prior research demonstrates that performance of language models on reasoning tasks can be influenced by suggestions, hints and endorsements. However, the influence of endorsement s…
AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Shaona Ghosh, Heather Frase, Adina Williams +99
The rapid advancement and deployment of AI systems have created an urgent need for standard safety-evaluation frameworks. This paper introduces AILuminate v1.0, the first comprehen…
Introducing v0.5 of the AI Safety Benchmark from MLCommons
Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed +97
This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safe…
Detecting Natural Language Biases with Prompt-based Learning
Md Abdul Aowal, Maliha T Islam, Priyanka Mary Mammen +1
In this project, we want to explore the newly emerging field of prompt engineering and apply it to the downstream task of detecting LM biases. More concretely, we explore how to de…
SleepMore: Inferring Sleep Duration at Scale via Multi-Device WiFi Sensing
Camellia Zakaria, Gizem Yilmaz, Priyanka Mammen +3
The availability of commercial wearable trackers equipped with features to monitor sleep duration and quality has enabled more useful sleep health monitoring applications and analy…
WiSleep: Inferring Sleep Duration at Scale Using Passive WiFi Sensing
Priyanka Mary Mammen, Camellia Zakaria, Tergel Molom-Ochir +3
Sleep deprivation is a public health concern that significantly impacts one's well-being and performance. Sleep is an intimate experience, and state-of-the-art sleep monitoring sol…
Federated Learning: Opportunities and Challenges
Priyanka Mary Mammen
Federated Learning (FL) is a concept first introduced by Google in 2016, in which multiple devices collaboratively learn a machine learning model without sharing their private data…
Explainable AI: Deep Reinforcement Learning Agents for Residential Demand Side Cost Savings in Smart Grids
Hareesh Kumar, Priyanka Mary Mammen, Krithi Ramamritham
Motivated by recent advancements in Deep Reinforcement Learning (RL), we have developed an RL agent to manage the operation of storage devices in a household and is designed to max…