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Found 13 papers, 9 papers with code
Taiyi-Diffusion-XL: Advancing Bilingual Text-to-Image Generation with Large Vision-Language Model Support
Recent advancements in text-to-image models have significantly enhanced image generation capabilities, yet a notable gap of open-source models persists in bilingual or Chinese language support.
iDesigner: A High-Resolution and Complex-Prompt Following Text-to-Image Diffusion Model for Interior Design
However, there are few specialized models in certain domains, such as interior design, which is attributed to the complex textual descriptions and detailed visual elements inherent in design, alongside the necessity for adaptable resolution.
MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
We introduce MMMU: a new benchmark designed to evaluate multimodal models on massive multi-discipline tasks demanding college-level subject knowledge and deliberate reasoning.
Ziya2: Data-centric Learning is All LLMs Need
Although many such issues are addressed along the line of research on LLMs, an important yet practical limitation is that many studies overly pursue enlarging model sizes without comprehensively analyzing and optimizing the use of pre-training data in their learning process, as well as appropriate organization and leveraging of such data in training LLMs under cost-effective settings.
A New Benchmark and Reverse Validation Method for Passage-level Hallucination Detection
Contrasting previous studies of zero-resource hallucination detection, our method and benchmark concentrate on passage-level detection instead of sentence-level.
A New Dataset and Empirical Study for Sentence Simplification in Chinese
Sentence Simplification is a valuable technique that can benefit language learners and children a lot.
Teaching the Pre-trained Model to Generate Simple Texts for Text Simplification
In this paper, we propose a new continued pre-training strategy to teach the pre-trained model to generate simple texts.
Human-like Summarization Evaluation with ChatGPT
Evaluating text summarization is a challenging problem, and existing evaluation metrics are far from satisfactory.
Exploiting Summarization Data to Help Text Simplification
One of the major problems with text simplification is the lack of high-quality data.
Nearest Neighbor Knowledge Distillation for Neural Machine Translation
k-nearest-neighbor machine translation (NN-MT), proposed by Khandelwal et al. (2021), has achieved many state-of-the-art results in machine translation tasks.
SimpleBERT: A Pre-trained Model That Learns to Generate Simple Words
We use a small-scale simple text dataset for continued pre-training and employ two methods to identify simple words from the texts.
Document-Level Text Simplification: Dataset, Criteria and Baseline
Finally, we select several representative models as baseline models for this task and perform automatic evaluation and human evaluation.
On the Helpfulness of Document Context to Sentence Simplification
Our model uses neural networks to learn the different effects of the preceding sentences and the following sentences on the current sentence and applies them to the improved transformer model.
