Papers by Parsa Hejabi
The Subjectivity of Respect in Police Traffic Stops: Modeling Community Perspectives in Body-Worn Camera Footage (2026.acl-long)
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Preni Golazizian, Elnaz Rahmati, Jackson Trager, Zhivar Sourati, Nona Ghazizadeh, Georgios Chochlakis, Jose J. Alcocer, Kerby Bennett, Aarya Vijay Devnani, Parsa Hejabi, Harry G. Muttram, Akshay Kiran Padte, Mehrshad Saadatinia, Chenhao Wu, Alireza Salkhordeh Ziabari, Michael Sierra-Arévalo, Nicholas Weller, Shrikanth Narayanan, Benjamin A.t. Graham, Morteza Dehghani
| Challenge: | a new study examines the perception of police-civilian traffic stops using respect ratings and free-text rationales from multiple perspectives. |
| Approach: | They propose a traffic-stop dataset annotated with respect ratings and rationales from multiple perspectives . they use a criterion-driven preference data construction framework to predict personalized respect ratings . |
| Outcome: | The proposed framework improves rating prediction performance and rationale alignment across all three annotators. |
Flip-Flop Consistency: Unsupervised Training for Robustness to Prompt Perturbations in LLMs (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) often produce inconsistent answers when faced with different phrasings of the same prompt. |
| Approach: | They propose an unsupervised method that flips the phrasings of prompts into a hard pseudo-label . they use Consensus Cross-Entropy to create a consensus, and representation alignment loss to pull lower-confidence predictors toward consensus . |
| Outcome: | The proposed method raises observed agreement by 11.62% and improves mean F1 by 8.94% on 11 datasets spanning four NLP tasks . |
Reinforced Multiple Instance Selection for Speaker Attribute Prediction (2024.naacl-long)
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Alireza Salkhordeh Ziabari, Ali Omrani, Parsa Hejabi, Preni Golazizian, Brendan Kennedy, Payam Piray, Morteza Dehghani
| Challenge: | Current methods for predicting speaker attributes take a speaker’s utterances as input and provide a prediction per speaker attribute. |
| Approach: | They propose a Multiple Instance Learning approach that uses Reinforcement Learning to predict speaker attributes using a set of utterances from social media posts and political ideologies from transcribed speeches. |
| Outcome: | The proposed approach outperforms existing methods on a range of related tasks including predicting speakers’ psychographics and demographics from social media posts and political ideologies from transcribed speeches. |