9 citations · 10 across the 4 of their papers we have counts for
6 papers
Aligning Language Models Towards Counterfactual Invariance
Basar Demir, Emily Shen, Ksheeraj Sai Vepuri +5
Small variations in prompt characteristics can meaningfully alter the semantics, tone, and contextual assumptions of large language model outputs. Such characteristics can involve…
MaD-RL: Matching Distributions for Calibrating LLMs with Reinforcement Learning
Sourabh Kulkarni, Ksheeraj Sai Vepuri, Basar Demir +8
Reinforcement learning (RL) is widely used in language-model post-training to maximize rewards assigned to individual model outputs, such as scores from binary verifiers or reward…
TRAPSBench: Vision-Language Models Encode but Fail to Express Epistemic Restraint
Fnu Pramono, John Cai, Sourabh Kulkarni
When visual evidence is occluded or chaotic, models should abstain. In this paper, we show that Vision-Language Models (VLMs) can internally distinguish when abstention is required…
Efficient State-space Exploration in Massively Parallel Simulation Based Inference
Sourabh Kulkarni, Csaba Andras Moritz
Simulation-based Inference (SBI) is a widely used set of algorithms to learn the parameters of complex scientific simulation models. While primarily run on CPUs in HPC clusters, th…
Hardware-accelerated Simulation-based Inference of Stochastic Epidemiology Models for COVID-19
Sourabh Kulkarni, Mario Michael Krell, Seth Nabarro +1
Epidemiology models are central in understanding and controlling large scale pandemics. Several epidemiology models require simulation-based inference such as Approximate Bayesian…
PPL Bench: Evaluation Framework For Probabilistic Programming Languages
Sourabh Kulkarni, Kinjal Divesh Shah, Nimar Arora +9
We introduce PPL Bench, a new benchmark for evaluating Probabilistic Programming Languages (PPLs) on a variety of statistical models. The benchmark includes data generation and eva…