Papers with large-scale training data
Machine Comprehension Improves Domain-Specific Japanese Predicate-Argument Structure Analysis (D19-58)
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| Challenge: | a lack of gold datasets and knowledge about PAS analysis makes it difficult to create accurate PAS analyses. |
| Approach: | They construct a Japanese blog-QA dataset and a reading comprehension QA dataset using crowdsourcing. |
| Outcome: | The proposed method is most effective, pre-training model to acquire domain knowledge and fine-tuning model based on PAS-QA dataset. |
InfiMM: Advancing Multimodal Understanding with an Open-Sourced Visual Language Model (2024.findings-acl)
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Haogeng Liu, Quanzeng You, Yiqi Wang, Xiaotian Han, Bohan Zhai, Yongfei Liu, Wentao Chen, Yiren Jian, Yunzhe Tao, Jianbo Yuan, Ran He, Hongxia Yang
| Challenge: | InfiMM is a multimodal large language model that adapts to complex vision-language tasks. |
| Approach: | They present a Multimodal Large Language Model that adapts to intricate vision-language tasks using large-scale training data and comprehensive training strategies. |
| Outcome: | Empirical evaluations across a variety of benchmarks underscore InfiMM’s remarkable capability in multimodal understanding. |
Language-to-Space Programming for Training-Free 3D Visual Grounding (2025.emnlp-main)
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| Challenge: | Existing methods for 3D visual grounding have been proposed, but they are limited by the scarcity of 3D vision-language datasets and the high cost of annotations. |
| Approach: | They propose a method for training-free 3D visual grounding that uses LLM-generated codes to analyze 3D spatial relations among objects. |
| Outcome: | The proposed method achieves 52.9% accuracy on the Nr3D benchmark and significantly reduces grounding time and token costs. |
RA-RRG: Multimodal Retrieval-Augmented Radiology Report Generation with Key Phrase Extraction (2026.findings-acl)
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| Challenge: | Existing MLLMs are computationally expensive and may produce hallucinated content . RA-RRG uses large language models to generate radiology reports . |
| Approach: | They propose a retrieval-augmented RRG framework that combines multimodal retrieval with large language models to generate radiology reports. |
| Outcome: | RA-RRG uses large language models to generate radiology reports . it suppresses hallucinations while maintaining strong report generation performance . |
Who Wrote This? The Key to Zero-Shot LLM-Generated Text Detection Is GECScore (2025.coling-main)
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| Challenge: | Existing methods for detecting LLM-generated text require no training data. |
| Approach: | They propose a black-box zero-shot detection approach that calculates the Grammar Error Correction Score for a given text to differentiate between human-written and LLM-generated texts. |
| Outcome: | The proposed method outperforms current state-of-the-art zero-shot and supervised methods, achieving an average AUROC of 98.62% across XSum and Writing Prompts datasets. |