Papers by Elnaz Rahmati
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 . |
GE2PE: Persian End-to-End Grapheme-to-Phoneme Conversion (2024.findings-emnlp)
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| Challenge: | Existing text-to-speech systems struggle to produce natural speech from grapheme sequences . Grapheme-to phoneme conversion (G2P) systems face limitations when dealing with Persian texts due to the complexity of Persian transcription. |
| Approach: | They propose to use phonetic information to enhance the input sequence for Persian translations. |
| Outcome: | The proposed model surpasses state-of-the-art models by 1.86% in word error rate and 3.42% in homograph disambiguation accuracy. |