Vector Calligrapher: Generating Scalable Vector Graphics via Structured Linguistic Supervision (2026.acl-long)
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| Challenge: | Existing approaches to generate SVG-based fonts struggle with semantic ambiguity and inefficiency . edward mcginley: generic text tokenizers fragment coordinate-dense SVG XML into excessively long sequences . |
| Approach: | They propose a system that treats SVG generation as a conditional language modeling task . they propose linguistic supervision framework that decomposes typographic style into interpretable linguistic dimensions . |
| Outcome: | The proposed system improves CLIP score by +23% while reducing geometric error by 48% and boosts generation efficiency by 18% Command-per-Token (C/T) ratio. |
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| Challenge: | Existing work on controllable natural language generation has focused on fine-tuning existing models or using attribute discriminators. |
| Approach: | They propose a lightweight framework for controllable GPT2 generation that utilizes attribute-specific vectors to steer natural language generation. |
| Outcome: | The proposed framework can guide generation towards desired attributes while keeping high linguistic quality. |
Glyph: Scaling Context Windows via Visual-Text Compression (2026.acl-long)
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Jiale Cheng, Yusen Liu, Xinyu Zhang, Yulin Fei, Wenyi Hong, Ruiliang Lyu, Weihan Wang, Zhe Su, Xiaotao Gu, Xiao Liu, Yushi Bai, Jie Tang, Hongning Wang, Minlie Huang
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| Outcome: | The proposed framework renders long texts into compact visual pages and processes them with a vision-language model. |
Style Vectors for Steering Generative Large Language Models (2024.findings-eacl)
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Kai Konen, Sophie Jentzsch, Diaoulé Diallo, Peer Schütt, Oliver Bensch, Roxanne El Baff, Dominik Opitz, Tobias Hecking
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Structured Pruning for Efficient Generative Pre-trained Language Models (2023.findings-acl)
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| Challenge: | Large-scale generative Pre-trained Language Models (PLMs) are limited in their deployment in real-world applications. |
| Approach: | They propose to prune the feed-forward networks of generative pre-trained language models to smaller widths without designing extra operators. |
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Lessons Learned from GPT-SW3: Building the First Large-Scale Generative Language Model for Swedish (2022.lrec-1)
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Ariel Ekgren, Amaru Cuba Gyllensten, Evangelia Gogoulou, Alice Heiman, Severine Verlinden, Joey Öhman, Fredrik Carlsson, Magnus Sahlgren
| Challenge: | a prerequisite for building large-scale generative models for other languages is access to large amounts of high-quality text data and powerful computational resources. |
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ChartCoder: Advancing Multimodal Large Language Model for Chart-to-Code Generation (2025.acl-long)
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| Challenge: | Existing open-source MLLMs fail to fully capture dense information embedded in charts . current models still face significant challenges in understanding and analyzing visual tasks such as captioning and question answering. |
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Boundary Matters: Leveraging Structured Text Plots for Long Text Outline Generation (2025.findings-emnlp)
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| Challenge: | Existing methods for generating readable outlines are inability to segment long texts . |
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| Outcome: | The proposed framework ensures that each structured plot encapsulates complete causality by accurately identifying plot boundaries. |
DeCoVec: Building Decoding Space based Task Vector for Large Language Models via In-Context Learning (2026.findings-acl)
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| Challenge: | Existing approaches to steering large language models require fine-tuning or manipulation of internal states, limiting their flexibility and scalability. |
| Approach: | They propose a framework that constructs task vectors directly in the decoding space by leveraging in-context learning. |
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Adaptive Constraint Propagation: Scaling Structured Inference for Large Language Models via Meta-Reinforcement Learning (2026.acl-long)
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| Challenge: | Large language models increasingly require structured inference, says aaron sagar . meta-learning learns universal constraint propagation policies without task-specific training . standard schedulers are inexpensive but myopic, because they optimize local effects . |
| Approach: | MetaJuLS learns universal constraint propagation policies applicable across languages and tasks without task-specific retraining. |
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Morphology-Aware Multi-Granularity Representation Learning for Agglutinative Languages (2026.acl-srw)
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| Challenge: | Existing methods for learning low-resource agglutinative languages are limited to word and phrase levels. |
| Approach: | They propose a morphology-aware gated multi-granularity pre-training framework for agglutinative languages . framework leverages morphological knowledge and integrates a word-level encoder to capture contextual semantics . |
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