Pittawat Taveekitworachai

Slash LLM evaluation costs by over 270x without sacrificing accuracy, revealing that even top open-weight models struggle to outperform simple baselines on AML transaction fraud detection.

Stronger reasoning in LLMs using Code Interpreters hinges on the strategic use of crucial tokens and cognitive behaviors, revealing both potential and constraints in model performance.

Forget fancy prompt optimization: carefully crafted rule-based role prompts with enforced function calls dramatically improve the performance of tool-augmented LLM dialogue agents.