1 citations · 1 across the 4 of their papers we have counts for
4 papers
MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers
Huanxi Liu, Kun Hu, Jiaqi Liao +6
As Model Context Protocol (MCP) servers emerge as the core infrastructure for connecting LLMs with external tools, existing benchmarks leverage real-world MCP servers to evaluate L…
Beyond Scores: Diagnostic LLM Evaluation via Fine-Grained Abilities
Xu Zhang, Xudong Gong, Jiacheng Qin +5
Current evaluations of large language models aggregate performance across diverse tasks into single scores. This obscures fine-grained ability variation, limiting targeted model im…
Pay More Attention to the Robustness of Prompt for Instruction Data Mining
Qiang Wang, Dawei Feng, Xu Zhang +4
Instruction tuning has emerged as a paramount method for tailoring the behaviors of LLMs. Recent work has unveiled the potential for LLMs to achieve high performance through fine-t…
Accurate and Efficient Fine-Tuning of Quantized Large Language Models Through Optimal Balance
Ao Shen, Qiang Wang, Zhiquan Lai +2
Large Language Models (LLMs) have demonstrated impressive performance across various domains. However, the enormous number of model parameters makes fine-tuning challenging, signif…