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

2025-05-17

AI Summary

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.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• Ultra-lightweight multilingual dataset for LLM smoke-testing
• Synthetic data generation for continuous LLM evaluation
• Detecting prompt errors and model drift efficiently

Innovation

• Ultra-lightweight multilingual smoke-test suite
• Synthetic-data generator for custom datasets
• Plug-and-play files for seamless integration