MulDimIF: A Multi-Dimensional Constraint Framework for Evaluating and Improving Instruction Following in Large Language Models

Documenti analoghi

• 60 Data Points are Sufficient to Fine-Tune LLMs for Question-Answering
  di: Ye, Junjie, et al.
  Pubblicazione: (2024)
• CCTU: A Benchmark for Tool Use under Complex Constraints
  di: Ye, Junjie, et al.
  Pubblicazione: (2026)
• ToolSword: Unveiling Safety Issues of Large Language Models in Tool Learning Across Three Stages
  di: Ye, Junjie, et al.
  Pubblicazione: (2024)
• Analyzing the Effects of Supervised Fine-Tuning on Model Knowledge from Token and Parameter Levels
  di: Ye, Junjie, et al.
  Pubblicazione: (2025)
• RoTBench: A Multi-Level Benchmark for Evaluating the Robustness of Large Language Models in Tool Learning
  di: Ye, Junjie, et al.
  Pubblicazione: (2024)