4 citations · 10 across the 6 of their papers we have counts for
6 papers
CAPE: A Chinese Dataset for Appraisal-based Emotional Generation using Large Language Models
June M. Liu, He Cao, Renliang Sun +3
Generating emotionally appropriate responses in conversations with large language models presents a significant challenge due to the complexities of human emotions and cognitive pr…
Fostering Natural Conversation in Large Language Models with NICO: a Natural Interactive COnversation dataset
Renliang Sun, Mengyuan Liu, Shiping Yang +3
Benefiting from diverse instruction datasets, contemporary Large Language Models (LLMs) perform effectively as AI assistants in collaborating with humans. However, LLMs still strug…
Taiyi-Diffusion-XL: Advancing Bilingual Text-to-Image Generation with Large Vision-Language Model Support
Xiaojun Wu, Dixiang Zhang, Ruyi Gan +6
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 lang…
iDesigner: A High-Resolution and Complex-Prompt Following Text-to-Image Diffusion Model for Interior Design
Ruyi Gan, Xiaojun Wu, Junyu Lu +8
With the open-sourcing of text-to-image models (T2I) such as stable diffusion (SD) and stable diffusion XL (SD-XL), there is an influx of models fine-tuned in specific domains base…
Lyrics: Boosting Fine-grained Language-Vision Alignment and Comprehension via Semantic-aware Visual Objects
Junyu Lu, Dixiang Zhang, Songxin Zhang +6
Large Vision Language Models (LVLMs) have demonstrated impressive zero-shot capabilities in various vision-language dialogue scenarios. However, the absence of fine-grained visual…
Ziya2: Data-centric Learning is All LLMs Need
Ruyi Gan, Ziwei Wu, Renliang Sun +11
Various large language models (LLMs) have been proposed in recent years, including closed- and open-source ones, continually setting new records on multiple benchmarks. However, th…