17 citations · 40 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…
Knowing You Don't Know: Learning When to Continue Search in Multi-round RAG through Self-Practicing
Diji Yang, Linda Zeng, Jinmeng Rao +1
Retrieval Augmented Generation (RAG) has shown strong capability in enhancing language models' knowledge and reducing AI generative hallucinations, driving its widespread use. Howe…
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…
Best Practices and Lessons Learned on Synthetic Data
Ruibo Liu, Jerry Wei, Fangyu Liu +8
The success of AI models relies on the availability of large, diverse, and high-quality datasets, which can be challenging to obtain due to data scarcity, privacy concerns, and hig…
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…