Papers with text alignment
SensorLLM: Aligning Large Language Models with Motion Sensors for Human Activity Recognition (2025.emnlp-main)
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| Challenge: | SensorLLM is a timeseries classification framework that can perform human activity recognition tasks. |
| Approach: | They propose a framework that enables Large Language Models to perform human activity recognition from sensor time-series data. |
| Outcome: | The proposed framework can perform human activity recognition (HAR) tasks with human inputs. |
Table-Text Alignment: Explaining Claim Verification Against Tables in Scientific Papers (2025.findings-emnlp)
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| Challenge: | predicting the final label alone is insufficient and offers limited interpretability. |
| Approach: | They propose to reframe table–text alignment as an explanation task requiring models to identify the table cells essential for claim verification. |
| Outcome: | The proposed taxonomy improves claim verification performance and most LLMs fail to recover human-aligned rationales, suggesting that their predictions do not stem from faithful reasoning. |
DuwatBench: Bridging Language and Visual Heritage through an Arabic Calligraphy Benchmark for Multimodal Understanding (2026.eacl-long)
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Shubham Patle, Sara Ghaboura, Hania Tariq, Mohammad Usman Khan, Omkar Thawakar, Rao Muhammad Anwer, Salman Khan
| Challenge: | a benchmark of 1,272 samples containing about 1,475 unique words is available for Arabic calligraphy . the dataset reflects real-world challenges in Arabic writing, such as calligraphic variation and artistic distortions . |
| Approach: | They evaluated 13 leading Arabic and multilingual multimodal models and paired them with sentence-level annotations to evaluate their calligraphy models. |
| Outcome: | The benchmark evaluates 13 leading Arabic and multilingual multimodal models . it shows they struggle with calligraphic variation, artistic distortions, and precise visual–text alignment. |
Universal Prompt Optimizer for Safe Text-to-Image Generation (2024.naacl-long)
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| Challenge: | Existing studies based on image checker, model fine-tuning and embedding blocking are impractical in real-world applications. |
| Approach: | They propose a novel reward function measuring toxicity and text alignment of generated images and train the optimizer through Proximal Policy Optimization. |
| Outcome: | The proposed model reduces the likelihood of various models in generating inappropriate images, with no significant impact on text alignment. |
Multilingual Text-to-Image Generation Magnifies Gender Stereotypes (2025.acl-long)
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Felix Friedrich, Katharina Hämmerl, Patrick Schramowski, Manuel Brack, Jindřich Libovický, Alexander Fraser, Kristian Kersting
| Challenge: | Text-to-image (T2I) generation models have great results in image quality, flexibility, and text alignment, but they suffer from substantial gender bias. |
| Approach: | They propose a benchmark to study gender bias in multilingual T2I models . they use multilingual prompts to account for grammatical differences influencing gender . |
| Outcome: | The proposed benchmark shows strong gender biases and language-specific differences across models. |