Papers by Mara Finkelstein
MQM Re-Annotation: A Technique for Collaborative Evaluation of Machine Translation (2026.acl-long)
Copied to clipboard
| Challenge: | a critical component of machine translation model development is evaluating model quality. |
| Approach: | They propose a two-stage version of the current translation evaluation paradigm (MQM) they propose re-annotation, which uses raters to review and edit annotations . |
| Outcome: | The proposed method improves annotation quality by finding errors missed in the first pass. |
WMT24++: Expanding the Language Coverage of WMT24 to 55 Languages & Dialects (2025.findings-acl)
Copied to clipboard
Daniel Deutsch, Eleftheria Briakou, Isaac Rayburn Caswell, Mara Finkelstein, Rebecca Galor, Juraj Juraska, Geza Kovacs, Alison Lui, Ricardo Rei, Jason Riesa, Shruti Rijhwani, Parker Riley, Elizabeth Salesky, Firas Trabelsi, Stephanie Winkler, Biao Zhang, Markus Freitag
| Challenge: | In order to evaluate large language models (LLMs), it is important to collect benchmark datasets in order to assess their multilingual performance. |
| Approach: | They extend the WMT24 dataset to cover 55 languages by collecting new human-written references and post-edits for 46 new languages/dialects. |
| Outcome: | The proposed dataset covers 55 languages and provides best-performing MT systems in all 55 languages. |
LLMRefine: Pinpointing and Refining Large Language Models via Fine-Grained Actionable Feedback (2024.findings-naacl)
Copied to clipboard
Wenda Xu, Daniel Deutsch, Mara Finkelstein, Juraj Juraska, Biao Zhang, Zhongtao Liu, William Yang Wang, Lei Li, Markus Freitag
| Challenge: | Recent large language models (LLMs) are leveraging human feedback to improve their output quality. however, human feedback is costly to collect, especially at inference time when the model provides new, unseen input. |
| Approach: | They propose an inference-time optimization method to refine large language models' output based on fine-grained feedback to pinpoint defects and guide iterative refinement . |
| Outcome: | The proposed method consistently outperforms baseline approaches on three text generation tasks, including machine translation, long-form question answering, and topical summarization. |