Tiny QA Benchmark++: Ultra-Lightweight, Synthetic Multilingual Dataset Generation&Smoke-Tests for Continuous LLM Evaluation
Continuous quality assessment in large language model (LLM) development incurs high cost, substantial latency, and heavy reliance on resource-intensive benchmarks. Method: This paper introduces an ultra-lightweight, multilingual, synthetically generated QA smoke-testing paradigm. It proposes a novel minimalist, deterministic micro-benchmarking framework enabling on-demand generation of synthetic test cases across arbitrary languages, domains, and difficulty levels. Integrated with the LiteLLM abstraction layer, Croissant metadata standard, and OpenAI-Evals/LangChain ecosystem, it delivers CI/CD-ready, plug-and-play evaluation interfaces. Contribution/Results: We release 52 English gold-standard test sets (<20 kB total) and pre-packaged support for 10 languages. Full validation adds only seconds of latency. Empirical evaluation demonstrates consistent detection of prompt-template errors, tokenizer drift, and fine-tuning side effects—significantly improving quality gating efficiency and observability in LLM pipelines.