11 citations · 24 across the 10 of their papers we have counts for
10 papers
Higher Satisfaction, Lower Cost: A Technical Report on How LLMs Revolutionize Meituan's Intelligent Interaction Systems
Xuxin Cheng, Ke Zeng, Zhiquan Cao +65
Enhancing customer experience is essential for business success, particularly as service demands grow in scale and complexity. Generative artificial intelligence and Large Language…
RealHiTBench: A Comprehensive Realistic Hierarchical Table Benchmark for Evaluating LLM-Based Table Analysis
Pengzuo Wu, Yuhang Yang, Guangcheng Zhu +10
With the rapid advancement of Large Language Models (LLMs), there is an increasing need for challenging benchmarks to evaluate their capabilities in handling complex tabular data.…
D.Va: Validate Your Demonstration First Before You Use It
Qi Zhang, Zhiqing Xiao, Ruixuan Xiao +2
In-context learning (ICL) has demonstrated significant potential in enhancing the capabilities of large language models (LLMs) during inference. It's well-established that ICL heav…
Automatic Dataset Construction (ADC): Sample Collection, Data Curation, and Beyond
Minghao Liu, Zonglin Di, Jiaheng Wei +15
Large-scale data collection is essential for developing personalized training data, mitigating the shortage of training data, and fine-tuning specialized models. However, creating…
FlowBench: Revisiting and Benchmarking Workflow-Guided Planning for LLM-based Agents
Ruixuan Xiao, Wentao Ma, Ke Wang +5
LLM-based agents have emerged as promising tools, which are crafted to fulfill complex tasks by iterative planning and action. However, these agents are susceptible to undesired pl…
On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey
Lin Long, Rui Wang, Ruixuan Xiao +4
Within the evolving landscape of deep learning, the dilemma of data quantity and quality has been a long-standing problem. The recent advent of Large Language Models (LLMs) offers…
FreeAL: Towards Human-Free Active Learning in the Era of Large Language Models
Ruixuan Xiao, Yiwen Dong, Junbo Zhao +4
Collecting high-quality labeled data for model training is notoriously time-consuming and labor-intensive for various NLP tasks. While copious solutions, such as active learning fo…
Controllable Textual Inversion for Personalized Text-to-Image Generation
Jianan Yang, Haobo Wang, Yanming Zhang +4
The recent large-scale generative modeling has attained unprecedented performance especially in producing high-fidelity images driven by text prompts. Text inversion (TI), alongsid…
ProMix: Combating Label Noise via Maximizing Clean Sample Utility
Ruixuan Xiao, Yiwen Dong, Haobo Wang +4
Learning with Noisy Labels (LNL) has become an appealing topic, as imperfectly annotated data are relatively cheaper to obtain. Recent state-of-the-art approaches employ specific s…
PiCO+: Contrastive Label Disambiguation for Robust Partial Label Learning
Haobo Wang, Ruixuan Xiao, Yixuan Li +4
Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set, which well suits many real-world data annotation s…