407 citations · 489 across the 48 of their papers we have counts for
7 papers · 1 filter
TableGPT2: A Large Multimodal Model with Tabular Data Integration
Aofeng Su, Aowen Wang, Chao Ye +30
The emergence of models like GPTs, Claude, LLaMA, and Qwen has reshaped AI applications, presenting vast new opportunities across industries. Yet, the integration of tabular data r…
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…
Data Contamination Calibration for Black-box LLMs
Wentao Ye, Jiaqi Hu, Liyao Li +3
The rapid advancements of Large Language Models (LLMs) tightly associate with the expansion of the training data size. However, the unchecked ultra-large-scale training sets introd…
RECOST: External Knowledge Guided Data-efficient Instruction Tuning
Qi Zhang, Yiming Zhang, Haobo Wang +1
In the current landscape of large language models (LLMs), the process of instruction tuning serves as an essential step. Considering the high computing power overhead, data-efficie…
Understanding and Mitigating the Bias in Sample Selection for Learning with Noisy Labels
Qi Wei, Lei Feng, Haobo Wang +1
Learning with noisy labels aims to ensure model generalization given a label-corrupted training set. The sample selection strategy achieves promising performance by selecting a lab…
Energy-based Automated Model Evaluation
Ru Peng, Heming Zou, Haobo Wang +3
The conventional evaluation protocols on machine learning models rely heavily on a labeled, i.i.d-assumed testing dataset, which is not often present in real world applications. Th…