Arjun Bedi
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Published in NAACL, 2025
Our results indicate that aggregation is a confounding factor in the modeling of subjective tasks, and advocate focusing on modeling individuals instead. However, aggregation does not explain the entire gap between ICL and the state of the art, meaning other factors in such tasks also account for the observed phenomena. Finally, by rigorously studying annotator-level labels, we find that it is possible for minority annotators to both better align with LLMs and have their perspectives further amplified.
Recommended citation: Chochlakis, Georgios, Alexandros Potamianos, Kristina Lerman, and Shrikanth Narayanan. "Aggregation Artifacts in Subjective Tasks Collapse Large Language Models Posteriors." In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics (NAACL). https://arxiv.org/abs/2410.13776
An ArgumentParser that supports your grid-search needs.
I was invited to talk to the senior AI leadership of CapitalOne about my research and our future directions, stemming from the collaboration of CapitalOne and USC and my fellowship. Given the sensitive nature of the discussions, I unfortunately cannot share more details [or pictures :(].
Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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