Seraphina Goldfarb-Tarrant

According to our database, Seraphina Goldfarb-Tarrant authored at least 14 papers between 2019 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Who Does the Giant Number Pile Like Best: Analyzing Fairness in Hiring Contexts.

Preethi Seshadri, Seraphina Goldfarb-Tarrant

CoRR, January, 2025

2024

Mix Data or Merge Models? Optimizing for Diverse Multi-Task Learning.

Aakanksha, Arash Ahmadian, Seraphina Goldfarb-Tarrant, Beyza Ermis, Marzieh Fadaee, Sara Hooker

CoRR, 2024

MultiContrievers: Analysis of Dense Retrieval Representations.

Seraphina Goldfarb-Tarrant, Pedro Rodriguez, Jane Dwivedi-Yu, Patrick S. H. Lewis

CoRR, 2024

A SMART Mnemonic Sounds like "Glue Tonic": Mixing LLMs with Student Feedback to Make Mnemonic Learning Stick.

Nishant Balepur, Matthew Shu, Alexander Miserlis Hoyle, Alison Robey, Shi Feng, Seraphina Goldfarb-Tarrant, Jordan L. Boyd-Graber

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

The Multilingual Alignment Prism: Aligning Global and Local Preferences to Reduce Harm.

Aakanksha, Arash Ahmadian, Beyza Ermis, Seraphina Goldfarb-Tarrant, Julia Kreutzer, Marzieh Fadaee, Sara Hooker

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

2023

This Prompt is Measuring : Evaluating Bias Evaluation in Language Models.

Seraphina Goldfarb-Tarrant, Eddie Ungless, Esma Balkir, Su Lin Blodgett

CoRR, 2023

Cross-lingual Transfer Can Worsen Bias in Sentiment Analysis.

Seraphina Goldfarb-Tarrant, Björn Ross, Adam Lopez

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

Bias Beyond English: Counterfactual Tests for Bias in Sentiment Analysis in Four Languages.

Seraphina Goldfarb-Tarrant, Adam Lopez, Roi Blanco, Diego Marcheggiani

Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, 2023

This prompt is measuring \textlessmask\textgreater: evaluating bias evaluation in language models.

Seraphina Goldfarb-Tarrant, Eddie Ungless, Esma Balkir, Su Lin Blodgett

Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, 2023

2022

How Gender Debiasing Affects Internal Model Representations, and Why It Matters.

Hadas Orgad, Seraphina Goldfarb-Tarrant, Yonatan Belinkov

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2022

2021

Intrinsic Bias Metrics Do Not Correlate with Application Bias.

Seraphina Goldfarb-Tarrant, Rebecca Marchant, Ricardo Muñoz Sánchez, Mugdha Pandya, Adam Lopez

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021

2020

Scaling Systematic Literature Reviews with Machine Learning Pipelines.

Seraphina Goldfarb-Tarrant, Alexander Robertson, Jasmina Lazic, Theodora Tsouloufi, Louise Donnison, Karen Smyth

Proceedings of the First Workshop on Scholarly Document Processing, 2020

Content Planning for Neural Story Generation with Aristotelian Rescoring.

Seraphina Goldfarb-Tarrant, Tuhin Chakrabarty, Ralph M. Weischedel, Nanyun Peng

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020

2019

Plan, Write, and Revise: an Interactive System for Open-Domain Story Generation.

Seraphina Goldfarb-Tarrant, Haining Feng, Nanyun Peng

Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019