4 papers · 1 filter
GTA-2: Benchmarking General Tool Agents from Atomic Tool-Use to Open-Ended Workflows
Jize Wang, Xuanxuan Liu, Yining Li +7
The development of general-purpose agents requires a shift from executing simple instructions to completing complex, real-world productivity workflows. However, current tool-use be…
Data Whisperer: Efficient Data Selection for Task-Specific LLM Fine-Tuning via Few-Shot In-Context Learning
Shaobo Wang, Xiangqi Jin, Ziming Wang +8
Fine-tuning large language models (LLMs) on task-specific data is essential for their effective deployment. As dataset sizes grow, efficiently selecting optimal subsets for trainin…
SAIL: Sample-Centric In-Context Learning for Document Information Extraction
Jinyu Zhang, Zhiyuan You, Jize Wang +1
Document Information Extraction (DIE) aims to extract structured information from Visually Rich Documents (VRDs). Previous full-training approaches have demonstrated strong perform…
GTA: A Benchmark for General Tool Agents
Jize Wang, Zerun Ma, Yining Li +4
Significant focus has been placed on integrating large language models (LLMs) with various tools in developing general-purpose agents. This poses a challenge to LLMs' tool-use capa…