6 citations · 11 across the 6 of their papers we have counts for
6 papers
Insight Miner: A Time Series Analysis Dataset for Cross-Domain Alignment with Natural Language
Yunkai Zhang, Yawen Zhang, Ming Zheng +10
Time-series data is critical across many scientific and industrial domains, including environmental analysis, agriculture, transportation, and finance. However, mining insights fro…
IM-RAG: Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologues
Diji Yang, Jinmeng Rao, Kezhen Chen +4
Although the Retrieval-Augmented Generation (RAG) paradigms can use external knowledge to enhance and ground the outputs of Large Language Models (LLMs) to mitigate generative hall…
Higher Layers Need More LoRA Experts
Chongyang Gao, Kezhen Chen, Jinmeng Rao +7
Parameter-efficient tuning (PEFT) techniques like low-rank adaptation (LoRA) offer training efficiency on Large Language Models, but their impact on model performance remains limit…
Evaluation and Enhancement of Semantic Grounding in Large Vision-Language Models
Jiaying Lu, Jinmeng Rao, Kezhen Chen +5
Large Vision-Language Models (LVLMs) offer remarkable benefits for a variety of vision-language tasks. However, a challenge hindering their application in real-world scenarios, par…
Tackling Vision Language Tasks Through Learning Inner Monologues
Diji Yang, Kezhen Chen, Jinmeng Rao +4
Visual language tasks require AI models to comprehend and reason with both visual and textual content. Driven by the power of Large Language Models (LLMs), two prominent methods ha…
LOWA: Localize Objects in the Wild with Attributes
Xiaoyuan Guo, Kezhen Chen, Jinmeng Rao +3
We present LOWA, a novel method for localizing objects with attributes effectively in the wild. It aims to address the insufficiency of current open-vocabulary object detectors, wh…