Tiny QA Benchmark++: Ultra-Lightweight, Synthetic Multilingual Dataset Generation & Smoke-Tests for Continuous LLM Evaluation

TL;DR

TQB++ targets the bottleneck of expensive, slow LLM evaluations by delivering an ultra-lightweight, deterministic 52-item English core (tiny QA benchmark) complemented by a synthetic data generator for multilingual packs. The framework standardizes metadata with Croissant JSON-LD, enables plug-and-play CI/CD integration, and supports multilingual testing to reveal cross-language drift and domain-specific weaknesses. Through a rigorous evaluation philosophy and a lightweight generation protocol, the authors demonstrate that micro-benchmarks can reliably surface major regressions, model-size effects, and language-specific gaps while retaining traceable provenance via SHA-256 hashes. This approach offers a practical, resource-efficient safety net for LLMOps, enabling rapid feedback loops during prompt engineering, agent development, and production monitoring, with clear pathways for adaptive, telemetry-informed test generation and future improvements.

Abstract

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.

Figures (7)

• Figure 1: Workflow of the TQB$^{++}$ synthetic data generator, illustrating the process from user inputs (language, topic, difficulty) through LLM-based generation with system prompts and few-shot examples, to JSON validation and final QA item output with provenance tracking.
• Figure 2: Conceptual diagram of TQB++ in a CI/CD Pipeline, showing code commit triggering test execution, results logging, decision points, and feedback loops.
• Figure 3: Example Observability Dashboard for TQB++ Monitoring.
• Figure 4: Heatmap of Exact Match (EM) scores across models and datasets (including supplementary challenge datasets sup-ancientlang_en_10 and sup-medicine_en_10). Darker shades indicate higher accuracy. Models are grouped by family and generally ordered by size/capability within families, illustrating performance degradation with decreasing model weight (e.g., Gemma-3 12b vs. 4b) and variance across datasets.
• Figure 5: F1-score versus Levenshtein Ratio (LR) threshold. The plot shows how the F1-score (comparing LR-based acceptance to EM ground truth) changes as the LR threshold is varied. The peak indicates the optimal LR threshold.