Author name cluster

Pushkar Mishra

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

TMLR Journal 2026 Journal Article

Decoding Safety Feedback from Diverse Raters: A Data-driven Lens on Responsiveness to Severity

Pushkar Mishra
Charvi Rastogi
Stephen R Pfohl
Alicia Parrish
Tian Huey Teh
Roma Patel
Mark Diaz
Ding Wang

Ensuring the safety of Generative AI requires a nuanced understanding of pluralistic viewpoints. In this paper, we introduce a novel data-driven approach for analyzing ordinal safety ratings in pluralistic settings. Specifically, we address the challenge of interpreting nuanced differences in safety feedback from a diverse population expressed via ordinal scales (e.g., a Likert scale). We define non-parametric responsiveness metrics that quantify how raters convey broader distinctions and granular variations in the severity of safety violations. Leveraging publicly available datasets of pluralistic safety feedback as our case studies, we investigate how raters from different demographic groups use an ordinal scale to express their perceptions of the severity of violations. We apply our metrics across violation types, demonstrating their utility in extracting nuanced insights that are crucial for aligning AI systems reliably in multi-cultural contexts. We show that our approach can inform rater selection and feedback interpretation by capturing nuanced viewpoints across different demographic groups, hence improving the quality of pluralistic data collection and in turn contributing to more robust AI alignment.

PDF Details

NeurIPS Conference 2025 Conference Paper

Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models

Charvi Rastogi
Tian Huey Teh
Pushkar Mishra
Roma Patel
Ding Wang
Mark Díaz
Alicia Parrish
Aida Mostafazadeh Davani

Current text-to-image (T2I) models often fail to account for diverse human experiences, leading to misaligned systems. We advocate for pluralism in AI alignment, where an AI understands and is steerable towards diverse, and often conflicting, human values. Our work provides three core contributions to achieve this in T2I models. First, we introduce a novel dataset for Diverse Intersectional Visual Evaluation (DIVE) -- the first multimodal dataset for pluralistic alignment. It enables deep alignment to diverse safety perspectives through a large pool of demographically intersectional human raters who provided extensive feedback across 1000 prompts, with high replication, capturing nuanced safety perceptions. Second, we empirically confirm demographics as a crucial proxy for diverse viewpoints in this domain, revealing significant, context-dependent differences in harm perception that diverge from conventional evaluations. Finally, we discuss implications for building aligned T2I models, including efficient data collection strategies, LLM judgment capabilities, and model steerability towards diverse perspectives. This research offers foundational tools for more equitable and aligned T2I systems. Content Warning: The paper includes sensitive content that may be harmful.

PDF Details

NeurIPS Conference 2022 Conference Paper

ReFactor GNNs: Revisiting Factorisation-based Models from a Message-Passing Perspective

Yihong Chen
Pushkar Mishra
Luca Franceschi
Pasquale Minervini
Pontus Lars Erik Saito Stenetorp
Sebastian Riedel

Factorisation-based Models (FMs), such as DistMult, have enjoyed enduring success for Knowledge Graph Completion (KGC) tasks, often outperforming Graph Neural Networks (GNNs). However, unlike GNNs, FMs struggle to incorporate node features and generalise to unseen nodes in inductive settings. Our work bridges the gap between FMs and GNNs by proposing ReFactor GNNs. This new architecture draws upon $\textit{both}$ modelling paradigms, which previously were largely thought of as disjoint. Concretely, using a message-passing formalism, we show how FMs can be cast as GNNs by reformulating the gradient descent procedure as message-passing operations, which forms the basis of our ReFactor GNNs. Across a multitude of well-established KGC benchmarks, our ReFactor GNNs achieve comparable transductive performance to FMs, and state-of-the-art inductive performance while using an order of magnitude fewer parameters.

PDF Details

NeurIPS Conference 2021 Conference Paper

Towards a robust experimental framework and benchmark for lifelong language learning

Aman Hussain
Nithin Holla
Pushkar Mishra
Helen Yannakoudakis
Ekaterina Shutova

In lifelong learning, a model learns different tasks sequentially throughout its lifetime. State-of-the-art deep learning models, however, struggle to generalize in this setting and suffer from catastrophic forgetting of old tasks when learning new ones. While a number of approaches have been developed in an attempt to ameliorate this problem, there are no established, unified or generalized frameworks for rigorous evaluations of proposed solutions; a problem which is particularly pronounced in the domain of NLP. The few existing benchmarks are typically limited to a specific flavor of lifelong learning -- continual open-set classification -- where new classes, as opposed to tasks, are learned incrementally. Moreover, the only general lifelong learning benchmark combines a multi-label classification setup with a multi-class classification setup resulting in misleading gradients during training. We empirically demonstrate that the catastrophic forgetting observed here can be attributed to the experimental design rather than to any inherent modeling limitations. To address these issues, we propose an experimental framework for true, general lifelong learning in NLP. Using this framework, we develop a comprehensive suite of benchmarks that target different properties of lifelong learning (e. g. , forgetting or intransigence); experiment with diverse facets of language learning: multi-domain, multilingual and different levels of linguistic hierarchy; and present a continuous evaluation scheme under a new metric: Area Under the Lifelong Test Curve. Our framework reveals shortcomings of prevalent memory-based solutions, demonstrating they are unable to outperform a simple experience replay baseline under the realistic lifelong learning setup.

PDF Details