Papers with resource-constrained applications
Grammar-Constrained Decoding Makes Large Language Models Better Logical Parsers (2025.acl-industry)
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| Challenge: | Large Language Models (LLMs) have shown capabilities in various natural language processing tasks, yet struggle with logical reasoning. |
| Approach: | They propose to combine Large Language Models with symbolic reasoners to improve syntactic correctness and semantic accuracy in logical parsing tasks. |
| Outcome: | The proposed approach improves syntactic correctness and semantic accuracy in logical parsing tasks. |
Image Embedding Sampling Method for Diverse Captioning (2025.emnlp-main)
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| Challenge: | Currently, large-scale captioning models are less accessible for resource-constrained applications such as mobile devices and assistive technologies. |
| Approach: | They propose a training-free framework that enhances caption diversity and informativeness by explicitly attending to distinct image regions using a comparably small VLM as the backbone. |
| Outcome: | The proposed framework achieves comparable performance to larger models on MSCOCO, Flickr30k, and Nocaps test datasets while maintaining strong image-caption relevancy and semantic integrity with the human-annotated captions. |
TinyAlign: Boosting Lightweight Vision-Language Models by Mitigating Modal Alignment Bottlenecks (2026.findings-acl)
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Yuanze Hu, Xinyu Wang, Zhichao Yang, Gen Li, Ye Qiu, Zhaoxin Fan, Yifan Sun, Wenjun wu, Jin Dong, Xiaotie Deng
| Challenge: | Lightweight Vision-Language Models (VLMs) are indispensable for resource-constrained applications. |
| Approach: | They propose a framework that retrieves context from a memory bank to enhance alignment . they propose EMI-based approach to align vision and language models . |
| Outcome: | The proposed framework reduces training loss, accelerates convergence, and enhances task performance with negligible computational overhead. |
Segmented Recurrent Transformer: An Efficient Sequence-to-Sequence Model (2023.findings-emnlp)
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| Challenge: | Transformers have shown dominant performance across a range of domains including language and vision, but their computational cost grows quadratically with the sequence length, making their usage prohibitive for resource-constrained applications. |
| Approach: | They propose a segmented recurrent transformer that combines segmente recursion with recursive attention to reduce the computational cost. |
| Outcome: | The proposed model achieves higher ROUGE1 scores and lower computational complexity than current approaches. |