Tiny QA Benchmark++: Ultra-Lightweight, Synthetic Multilingual Dataset
  Generation & Smoke-Tests for Continuous LLM Evaluation
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.
682c49699953a079cc8964a0
Vincent Koc
extracted_confirmed
2025-05-20T09:20:49.606Z
643bc6ea5ec6af9c331ad3f9
https://cdn-avatars.huggingface.co/v1/production/uploads/643bc6ea5ec6af9c331ad3f9/ZFppIidaJ_dKgk70bU6f6.png
Vincent Koc
user
vincentkoc
Tiny QA Benchmark++: Ultra-Lightweight, Synthetic Multilingual Dataset
  Generation & Smoke-Tests for Continuous LLM Evaluation
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.
TQB++ offers a lightweight multilingual dataset for LLM pipelines to conduct quick and cost-effective unit tests, detecting errors and issues before comprehensive benchmarks.
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