LPDS quantifies difficulty of logic-preserving problem variations and searches for the hardest ones, producing up to 5x larger performance drops than random sampling and better robustness gains from fine-tuning on difficult examples.
The Twelfth International Conference on Learning Representations , year=
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JTPRO co-optimizes prompts and tool descriptions via reflection to raise overall success rate by 5-20% over baselines on multi-tool benchmarks.
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LPDS: Evaluating LLM Robustness Through Logic-Preserving Difficulty Scaling
LPDS quantifies difficulty of logic-preserving problem variations and searches for the hardest ones, producing up to 5x larger performance drops than random sampling and better robustness gains from fine-tuning on difficult examples.
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JTPRO: A Joint Tool-Prompt Reflective Optimization Framework for Language Agents
JTPRO co-optimizes prompts and tool descriptions via reflection to raise overall success rate by 5-20% over baselines on multi-tool benchmarks.