title: Tiny QA Benchmark++: Ultra-Lightweight, Synthetic Multilingual Dataset
    Generation & Smoke-Tests for Continuous LLM Evaluation
summary: 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.
paper_authors._id: 682c49699953a079cc8964a0
paper_authors.name: Vincent Koc
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-05-20T09:20:49.606Z
paper_authors._id: 643bc6ea5ec6af9c331ad3f9
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/643bc6ea5ec6af9c331ad3f9/ZFppIidaJ_dKgk70bU6f6.png
paper_authors.fullname: Vincent Koc
paper_authors.type: user
paper_authors.user: vincentkoc
paper_title: Tiny QA Benchmark++: Ultra-Lightweight, Synthetic Multilingual Dataset
    Generation & Smoke-Tests for Continuous LLM Evaluation
paper_summary: 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.
paper_ai_summary: TQB++ offers a lightweight multilingual dataset for LLM pipelines to conduct quick and cost-effective unit tests, detecting errors and issues before comprehensive benchmarks.
paper_ai_keywords: LLM smoke-test suite unit-test style safety net dataset Comet Opik prompt-optimization SDK synthetic-data generator LiteLLM Croissant metadata OpenAI-Evals LangChain CI tools prompt-template errors tokenizer drift fine-tuning side-effects MMLU BIG-Bench generative-AI ecosystem