CLSEJul 24, 2024

SimCT: A Simple Consistency Test Protocol in LLMs Development Lifecycle

arXiv:2407.17150v21 citationsh-index: 8
Originality Synthesis-oriented
AI Analysis

This work addresses quality assurance in LLM development for industrial practitioners, though it appears incremental as it builds on existing testing concepts.

The authors tackled the problem of ensuring consistency across development stages for Large Language Models (LLMs) in industry, proposing SimCT, a simple consistency test protocol that reduces alignment communications among teams and expedites delivery.

In this work, we report our efforts to advance the standard operation procedure of developing Large Language Models (LLMs) or LLMs-based systems or services in industry. We introduce the concept of Large Language Model Development Lifecycle (LDLC) and then highlight the importance of consistency test in ensuring the delivery quality. The principled solution of consistency test, however, is usually overlooked by industrial practitioners and not urgent in academia, and current practical solutions are insufficiently rigours and labor-intensive. We thus propose a simple yet effective consistency test protocol, named SimCT. SimCT is mainly to proactively check the consistency across different development stages of "bare metal" LLMs or associated services without accessing the model artifacts, in an attempt to expedite the delivery by reducing the back-and-forth alignment communications among multiple teams involved in different development stages. Specifically, SimCT encompasses response-wise and model-wise tests. We implement the protocol with LightGBM and Student's t-test for two components respectively, and perform extensive experiments to substantiate the effectiveness of SimCT and the involved components.

Foundations

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