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The paper introduces Tiny QA Benchmark++ (TQB++), a lightweight, multilingual benchmark suite for rapid LLM evaluation, designed to provide a unit-test style safety net. It combines a small English gold set with a synthetic data generator (built on LiteLLM) that enables users to create custom benchmarks in various languages and domains. TQB++ integrates with existing evaluation frameworks like OpenAI-Evals and LangChain, allowing for continuous quality assurance in LLM pipelines with minimal computational overhead.
Stop waiting for heavyweight benchmarks: TQB++ offers a unit-test style safety net for LLMs that runs in seconds and flags issues like prompt errors and tokenizer drift early.
Tiny QA Benchmark++ (TQB++) presents an ultra-lightweight, multilingual smoke-test suite designed to give large-language-model (LLM) pipelines a unit-test style safety net dataset that runs in seconds with minimal cost. Born out of the tight feedback-loop demands building the Comet Opik prompt-optimization SDK, where waiting on heavyweight benchmarks breaks developer flow. TQB++ couples a 52-item English gold set (less than 20 kB) with a tiny synthetic-data generator pypi package built on provider-agnostic LiteLLM. The generator lets practitioners mint their own tiny packs in any language, domain, or difficulty, while ten ready-made packs already cover Arabic, Chinese, French, German, Japanese, Korean, Portuguese, Russian, Spanish, and Turkish. Every dataset ships with Croissant metadata and plug-and-play files for OpenAI-Evals, LangChain, and standard CI tools, so teams can drop deterministic micro-benchmarks directly into pull-request gates, prompt-engineering loops, and production dashboards without touching GPU budgets. A complete TQB++ run adds only a few seconds to pipeline latency yet reliably flags prompt-template errors, tokenizer drift, and fine-tuning side-effects long before full-scale suites like MMLU or BIG-Bench would finish configuring. The entire framework is released to accelerate continuous, resource-efficient quality assurance across the generative-AI ecosystem.
