Papers by Fred Blain
Forged-GAN-BERT: Authorship Attribution for LLM-Generated Forged Novels (2024.eacl-srw)
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| Challenge: | generative Large Language Models (LLMs) are capable of producing human-like texts, but they pose challenges related to the authenticity of the text documents. |
| Approach: | They propose a modified GANBERT-based model to improve the classification of forged novels via the Forged Novels Generator and the generator in GAN. |
| Outcome: | The proposed model improves classification of forged novels in two data-augmentation aspects. |
What do Large Language Models Need for Machine Translation Evaluation? (2024.emnlp-main)
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Shenbin Qian, Archchana Sindhujan, Minnie Kabra, Diptesh Kanojia, Constantin Orasan, Tharindu Ranasinghe, Fred Blain
| Challenge: | Existing research shows that large language models can perform better in machine translation tasks. |
| Approach: | They propose to use large language models for machine translation evaluations . authors explore what translation information is needed for LLMs to evaluate MT quality . |
| Outcome: | The proposed model performs comparable to fine-tuned multilingual pre-trained models. |
Quality Estimation-Assisted Automatic Post-Editing (2023.findings-emnlp)
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| Challenge: | Existing APE and QE combination strategies have not shown significant performance gains in the field of automatic post-editing (APE). |
| Approach: | They propose to train a model on APE and QE tasks to improve the APE performance by using a multi-task learning methodology that treats both tasks as a 'bargaining game' they also investigate various existing combination strategies and show that their approach achieves state-of-the-art performance for a ‘distant’ language pair, viz., English-Marathi. |
| Outcome: | The proposed model improves on two different language pairs, viz., English-Marathi and English-German. |
An Interdisciplinary Approach to Human-Centered Machine Translation (2025.emnlp-main)
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Marine Carpuat, Omri Asscher, Kalika Bali, Luisa Bentivogli, Fred Blain, Lynne Bowker, Monojit Choudhury, Hal Daumé Iii, Kevin Duh, Ge Gao, Alvin C Grissom II, Marzena Karpinska, Elaine C Khoong, William D. Lewis, Andre Martins, Mary Nurminen, Douglas W. Oard, Maja Popovic, Michel Simard, François Yvon
| Challenge: | Despite progress in MT, a gap persists between how the technology is developed and how it is used in real-world contexts. |
| Approach: | They propose a human-centered approach to machine translation (MT) they argue that MT should be evaluated with diverse goals and contexts of use . |
| Outcome: | The proposed approach emphasizes alignment of evaluation and design with diverse communicative goals and contexts of use. |