Prompt optimization (PO) generates prompts to guide Large Language Models (LLMs) in performing tasks. Existing methods, such as PromptAgent, rely on a single static prompt, which struggles with disjoint cases in complex tasks. Although MoP uses multiple prompts, it fails to account for variations in task complexity. Inspired by programmatic control flow, we introduce a nested if-else structure to address both varying similarities and complexities across diverse cases. We propose Tree-of-Prompts (ToP), which implements this structure by recursively expanding child prompts from a parent prompt. Sibling prompts tackle disjoint cases while inheriting shared similarities from their parent, and handle cases more complex than the parent. Evaluated on Gorilla (understanding), MATH (reasoning), and a subset of BBH benchmarks, ToP outperforms PromptAgent and MoP, with improvements of 1.4% and 4.6% over PromptAgent and 3.2% and 4.5% over MoP, when tested with GPT-4o-mini and Llama 3.2-3B, respectively.

We present MASSIVE-Agents, a new benchmark for assessing multilingual function calling across 52 languages. We created MASSIVE-Agents by cleaning the original MASSIVE dataset and then reformatting it for evaluation within the Berkeley Function-Calling Leaderboard (BFCL) framework. The full benchmark comprises 47,020 samples with an average of 904 samples per language, covering 55 different functions and 286 arguments. We benchmarked 21 models using Amazon Bedrock and present the results along with associated analyses. MASSIVE-Agents is challenging, with the top model Nova Premier achieving an average Abstract Syntax Tree (AST) Accuracy of 34.05% across all languages, with performance varying significantly from 57.37% for English to as low as 6.81% for Amharic. Some models, particularly smaller ones, yielded a score of zero for the more difficult languages. Additionally, we provide results from ablations using a custom 1-shot prompt, ablations with prompts translated into different languages, and comparisons based on model latency.