Papers by Antoine Chaffin
PPL-MCTS: Constrained Textual Generation Through Discriminator-Guided MCTS Decoding (2022.naacl-main)
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| Challenge: | Large language models (LM) based on transformers generate plausible long texts . a discriminator-guided approach allows to apply constraints more finely and dynamically. |
| Approach: | They propose to use a discriminator-guided approach to generate constrained texts without fine-tuning the LM. |
| Outcome: | The proposed method is easier and cheaper to train than fine-tuning the LM. |
Generating Artificial Texts as Substitution or Complement of Training Data (2022.lrec-1)
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| Challenge: | Existing approaches to generate text for supervised learning tasks use transformers to generate learning data. |
| Approach: | They propose to use transformers to generate supervised learning data for supervised machine learning tasks and propose to train a neural language model trained on the original training texts. |
| Outcome: | The proposed models can be used in a certain extend but require pre-processing to significantly improve performance. |
Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference (2025.acl-long)
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Benjamin Warner, Antoine Chaffin, Benjamin Clavié, Orion Weller, Oskar Hallström, Said Taghadouini, Alexis Gallagher, Raja Biswas, Faisal Ladhak, Tom Aarsen, Griffin Thomas Adams, Jeremy Howard, Iacopo Poli
| Challenge: | Encoder-only transformer models such as BERT offer a great performance-size tradeoff for retrieval and classification tasks compared to larger decoder models. |
| Approach: | They introduce a new transformer model, ModernBERT, which brings modern model optimizations to encoder-only transformer models. |
| Outcome: | The proposed model improves on the BERT transformer model and is faster and more memory efficient than the older models. |