Papers with self-supervision tasks
TechING: Towards Real World Technical Image Understanding via VLMs (2026.findings-eacl)
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| Challenge: | Modern day vision language models struggle when it comes to understanding technical diagrams . a large synthetically generated corpus is needed to train and evaluate VLMs on hand-drawn images . |
| Approach: | They propose a large synthetically generated corpus for training VLMs and evaluate them on hand-drawn images. |
| Outcome: | The proposed model improves ROUGE-L performance of Llama 3.2 11B-instruct by 2.14x on synthetic images on real-world images. |
PTUM: Pre-training User Model from Unlabeled User Behaviors via Self-supervision (2020.findings-emnlp)
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| Challenge: | Existing methods for user modeling cannot exploit useful information in unlabeled data . Existing models only model task-specific user information and do not exploit universal user information encoded in user behaviors. |
| Approach: | They propose to pre-train user models from large-scale unlabeled user behavior data. |
| Outcome: | The proposed method can model relatedness between historical and future behaviors on two real-world datasets. |
Fine-Tuning Language Models on Multiple Datasets for Citation Intention Classification (2024.findings-emnlp)
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Zeren Shui, Petros Karypis, Daniel Karls, Mingjian Wen, Saurav Manchanda, Ellad Tadmor, George Karypis
| Challenge: | Prior research has shown that pretrained language models (PLMs) can achieve state-of-the-art performance on CIC benchmarks. |
| Approach: | They propose a multi-task learning framework that fine-tunes pretrained language models on a dataset of primary interest together with multiple auxiliary CIC datasets to take advantage of additional supervision signals. |
| Outcome: | The proposed framework outperforms current state-of-the-art models on small datasets while aligning with the best-performing model on a large dataset. |