Portrait of Soumya Sharma is unavailable

Soumya Sharma

soumya.sharma@mila.quebec
PhD - McGill University
Supervisor
Golnoosh Farnadi
Co-supervisor
Adriana Romero Soriano
Research Topics
Creativity
Deep Learning
Natural Language Processing
Reasoning
Website
Google Scholar
GitHub

Publications

IDP-Bench: Benchmarking ability of LLMs to protect personal information in interdependent privacy contexts
Ayana Hussain
Soumya Sharma
Golnoosh Farnadi
Nicholas Vincent
Héber Hwang Arcolezi
Ulrich Aivodji
Large language models (LLMs) are becoming widely deployed as personal AI assistants with access to sensitive user data, making privacy a maj… (see more)or challenge for their design and evaluation. Prior work focuses mainly on individual-level risks, overlooking \textbf{interdependent privacy (IDP)}--where one person's data may be revealed by others without their knowledge or consent. We address this gap by introducing \textbf{IDP-Bench}: the first LLM benchmark for IDP scenarios, grounded in the Contextual Integrity (CI) framework. We evaluate eight open-source LLMs on their understanding of IDP scenarios across three levels of IDP reasoning using two LLM judges. Results show strong co-ownership recognition (6/8 models exceed 90\%) but persistent weaknesses in identifying CI parameters (information attribute, primary subject) and IDP-specific parameters such as secondary subjects, where 7/8 models score below 74\%. Models also struggle to judge sharing appropriateness (5/8 scoring below 77\%). While the ability to judge the appropriateness of sharing improves with scale, performance tends to decline in smaller models, and prompt sensitivity remains high on IDP-specific questions--highlighting the need for more targeted study of IDP in LLM privacy research. Data \& code available \href{https://github.com/tisl-lab/Interdependent_Privacy_Bench}{here}.
2026-06-05
arXiv (preprint)
doi.org
arxiv.org
IDEAFix: Evaluation Framework for Creative Defixation Prompting in LLMs
Florian Carichon
Soumya Sharma
Meaghan Girard
Romain Rampa
Golnoosh Farnadi
Large language models (LLMs) are increasingly used for tasks involving creative problem solving and idea generation. However, there is a lac… (see more)k of consensus concerning their creative capabilities: some studies report superior performances compared to humans, while others highlight structural limitations such as fixation and the homogenization of outputs. Existing evaluation approaches either rely on narrow, decontextualized tasks that do not capture goal-oriented generation or on broader settings that confound multiple aspects of the creative process, making it difficult to isolate the effects of task formulation, prompting, and evaluation design. Significantly, the role of structured prompting strategies in shaping idea generation remains underexplored. Therefore, we introduce IDEAFix, an evaluation framework for analyzing divergent thinking in open-ended idea generation tasks. We prompt models to generate multiple original solutions to controlled variations of short design scenarios, task attributes, and defixation prompting strategies. This design enables systematic analysis of how structured guidance influences LLMs' idea generation. Our results show that both task formulation and attribute selection significantly affect models' performance, and that simple prompting strategies can boost the originality of solutions. However, we also observe persistent output homogenization across models, confirming inherent limits in their ability to generate diverse solutions. Overall, IDEAFix provides a controlled, extensible framework for studying the mechanisms underlying LLMs' creativity.
2026-05-29
arXiv (preprint)
doi.org
arxiv.org
Robust Reward Modeling via Causal Rubrics
Pragya Srivastava
Harman Singh
Rahul Madhavan
Gandharv Patil
Sravanti Addepalli
Arun Suggala
Rengarajan Aravamudhan
Soumya Sharma
Anirban Laha
Aravindan Raghuveer
Karthikeyan Shanmugam
Doina Precup
Reward models (RMs) for LLM alignment often exhibit reward hacking, mistaking spurious correlates (e.g., length, format) for causal quality … (see more)drivers (e.g., factuality, relevance), leading to brittle RMs. We introduce CROME (Causally Robust Reward Modeling), a causally-grounded framework using targeted augmentations to mitigate this. CROME employs: (1) Causal Augmentations, pairs isolating specific causal attribute changes, to enforce sensitivity, and (2) Neutral Augmentations, tie-labeled pairs varying spurious attributes while preserving causal content, to enforce invariance. Crucially, augmentations target LLM-identified causal rubrics, requiring no prior knowledge of spurious factors. CROME significantly outperforms baselines on RewardBench (Avg +5.4\%, Safety +13.2\%, Reasoning +7.2\%) and demonstrates enhanced robustness via improved Best-of-N performance across RewardBench, WildGuardTest, and GSM8k.
2025-06-09
ICML.cc/2025/Workshop/MoFA (poster)
doi.org
openreview.net