Papers by M Saifur Rahman
Inceptive Transformers: Enhancing Contextual Representations through Multi-Scale Feature Learning Across Domains and Languages (2025.emnlp-main)
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| Challenge: | Encoder transformer models compress information from all tokens into a single [CLS] token to represent global context. |
| Approach: | They propose a 1-D convolution module that augments token representations with multi-scale local features to improve performance. |
| Outcome: | Experiments on five diverse tasks show that the proposed framework outperforms baseline models by 1% to 14% while maintaining efficiency. |
The Art of Saying "Maybe": A Conformal Lens for Uncertainty Benchmarking in VLMs (2026.findings-eacl)
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| Challenge: | Recent advances in large vision-language models have led to remarkable progress in complex visual understanding across scientific and reasoning tasks. |
| Approach: | They evaluate 18 state-of-the-art vision-language models across 6 multimodal datasets with 3 distinct scoring functions and develop instruction-guided likelihood proxies for closed-source models lacking token-level logprob access. |
| Outcome: | The proposed model is able to achieve higher accuracy on multimodal benchmarks while performing poorer on reasoning tasks. |