Papers by Benjamin Adams
Pre-Trained Language Models Represent Some Geographic Populations Better than Others (2024.lrec-main)
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| Challenge: | Existing studies have focused on measuring the degree to which pre-trained language models capture purely linguistic knowledge and reasoning abilities and world knowledge. |
| Approach: | They use geography to demarcate different populations around the world and comparable corpora to measure how well two families of LLMs perform across these different populations. |
| Outcome: | The results show that pre-trained models perform better for some populations than others. |
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. |