MP-RNA: Unleashing Multi-species RNA Foundation Model via Calibrated Secondary Structure Prediction (2024.findings-emnlp)
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| Challenge: | Experimental evaluations demonstrate that our RNA FM consistently outperforms existing RNA . |
| Approach: | They propose to use filtered high-fidelity structure annotations to enhance the modeling ability of FMs in single nucleotide resolution tasks. |
| Outcome: | The proposed model outperforms existing RNA FMs on four genomic benchmarks and achieves top-tier results on DNA genomic benchmark. |
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| Challenge: | Existing foundation models can only perform the best in one type of understanding tasks. |
| Approach: | They propose a method for training a general foundation model, X-FM, using text, image, and image-text data. |
| Outcome: | The proposed method outperforms existing foundation models on language, vision, and vision-language understanding tasks. |
mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understanding (2024.findings-emnlp)
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Anwen Hu, Haiyang Xu, Jiabo Ye, Ming Yan, Liang Zhang, Bo Zhang, Ji Zhang, Qin Jin, Fei Huang, Jingren Zhou
| Challenge: | Existing Multimodal Large Language Models lack general structure understanding abilities for text-rich document images. |
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Unleashing the True Potential of Sequence-to-Sequence Models for Sequence Tagging and Structure Parsing (2023.tacl-1)
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| Challenge: | Sequence-to-Sequence (S2S) models have been successful on text generation tasks . however, learning complex structures with S2S models remains challenging . |
| Approach: | They propose to use constrained decoding to model part-of-speech tagging, named entity recognition, constituency, and dependency parsing tasks with 3 lexically diverse linearization schemas and corresponding constrained coding methods. |
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Complicate Then Simplify: A Novel Way to Explore Pre-trained Models for Text Classification (2022.coling-1)
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| Challenge: | Existing frameworks for text classification employing pre-trained models are constrained by the difficulty of the task. |
| Approach: | They propose a framework which implements a two-stage training strategy to fully exploit the knowledge in pre-trained models. |
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Structure-Aware Language Model Pretraining Improves Dense Retrieval on Structured Data (2023.findings-acl)
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| Challenge: | Structure Aware Dense Retrieval (SANTA) model encodes user queries and structured data in one universal embedding space for retrieving structured data. |
| Approach: | They propose to use structured data and unstructured data to encode queries and structured data in one universal embedding space for retrieving structured data. |
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A Survey on Foundation Language Models for Single-cell Biology (2025.acl-long)
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| Challenge: | Existing single-cell foundation language models are based on pre-trained and large language models. |
| Approach: | They review the development of single-cell foundation language models . they discuss data tokenization strategies and pre-training paradigms . |
| Outcome: | The proposed models have shown remarkable performance in a variety of single-cell data analysis tasks. |
Latent Structure Models for Natural Language Processing (P19-4)
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| Challenge: | Latent structure models are a powerful tool for compositional data modeling and pipelines. |
| Approach: | This tutorial will cover recent advances in discrete latent structure models . it will discuss their motivation, potential, and limitations . |
| Outcome: | This tutorial will cover recent advances in discrete latent structure models . it will discuss their motivation, potential, and limitations . |
SR-LLM: Rethinking the Structured Representation in Large Language Model (2025.acl-long)
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Jiahuan Zhang, Tianheng Wang, Ziyi Huang, Yulong Wu, Hanqing Wu, DongbaiChen DongbaiChen, Linfeng Song, Yue Zhang, Guozheng Rao, Kaicheng Yu
| Challenge: | Structured representations have long been pivotal in computational linguistics, but their role remains ambiguous in the Large Language Models (LLMs) era. |
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Leveraging AMR Graph Structure for Better Sequence-to-Sequence AMR Parsing (2024.lrec-main)
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| Challenge: | Recent studies on AMR parsing often regard this task as a seq2seq translation problem. |
| Approach: | They propose to translate AMR graphs into AMR token sequences in pre-processing and recover AMR from sequences after decoding. |
| Outcome: | The proposed approach outperforms baseline and achieves 85.5 0.1 and 84.2 0.2 Smatch scores on AMR 2.0 and AMR 3.0. |
mPMR: A Multilingual Pre-trained Machine Reader at Scale (2023.acl-short)
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| Challenge: | Existing mPLMs only transfer NLU capability from source to target languages . mPMR allows direct inheritance of multilingual NLU capabilities to downstream tasks . |
| Approach: | They propose a method to guide multilingual pre-trained language models to perform natural language understanding in multiple languages. |
| Outcome: | mPMR enables multilingual pre-trained language models to perform natural language understanding (NLU) in multiple languages. |