Sireesh Gururaja

I’m a PhD student at Carnegie Mellon University’s Language Technologies Institute, advised by Emma Strubell. I previously completed a masters degree here under the supervision of Carolyn Rose, and a BA in computer science at Columbia University. My work is supported by the Army Research Lab’s HTMDEC US Citizen Fellowship.

My research focuses on NLP and AI tools that allows users in specialized domains to keep agency in their work. How can we empower people to customize and change their tools to reflect and be useful to how they see their own jobs, rather than how their boss or a tech company with a billion other users does? More concretely, I focus on user-customizable, on-device models, and how to effectively reason their limitations and update them. I’m also interested in the incentives that shape NLP research, whether funding, tooling, or culture.

Before coming to CMU, I spent six years in industry. I started at IBM Watson in 2015 on a team that did bespoke prototypes; I then moved to Kensho Technologies in 2018, where I spent three and a half years, first working as an ML engineer focused on NLP, then as the first lead of the ML Ops and Internal Tools team.

selected publications

To Build Our Future, We Must Know Our Past: Contextualizing Paradigm Shifts in Natural Language Processing

Sireesh Gururaja, Amanda Bertsch, Clara Na, and 2 more authors

In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Dec 2023

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NLP is in a period of disruptive change that is impacting our methodologies, funding sources, and public perception. In this work, we seek to understand how to shape our future by better understanding our past. We study factors that shape NLP as a field, including culture, incentives, and infrastructure by conducting long-form interviews with 26 NLP researchers of varying seniority, research area, institution, and social identity. Our interviewees identify cyclical patterns in the field, as well as new shifts without historical parallel, including changes in benchmark culture and software infrastructure. We complement this discussion with quantitative analysis of citation, authorship, and language use in the ACL Anthology over time. We conclude by discussing shared visions, concerns, and hopes for the future of NLP. We hope that this study of our field’s past and present can prompt informed discussion of our community’s implicit norms and more deliberate action to consciously shape the future.
Linguistic representations for fewer-shot relation extraction across domains

Sireesh Gururaja, Ritam Dutt, Tinglong Liao, and 1 more author

In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Jul 2023

Abs DOI

Recent work has demonstrated the positive impact of incorporating linguistic representations as additional context and scaffolds on the in-domain performance of several NLP tasks. We extend this work by exploring the impact of linguistic representations on cross-domain performance in a few-shot transfer setting. An important question is whether linguistic representations enhance generalizability by providing features that function as cross-domain pivots. We focus on the task of relation extraction on three datasets of procedural text in two domains, cooking and materials science. Our approach augments a popular transformer-based architecture by alternately incorporating syntactic and semantic graphs constructed by freely available off-the-shelf tools. We examine their utility for enhancing generalization, and investigate whether earlier findings, e.g. that semantic representations can be more helpful than syntactic ones, extend to relation extraction in multiple domains. We find that while the inclusion of these graphs results in significantly higher performance in few-shot transfer, both types of graph exhibit roughly equivalent utility.
Llms as workers in human-computational algorithms? replicating crowdsourcing pipelines with llms

Tongshuang Wu, Haiyi Zhu, Maya Albayrak, and 8 more authors

arXiv preprint arXiv:2307.10168, Jul 2023