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SimCT: A Simple Consistency Test Protocol in LLMs Development Lifecycle

Fufangchen Zhao, Guoqiang Jin, Rui Zhao, Jiangheng Huang, Fei Tan

TL;DR

SimCT introduces the LDLC framework and a principled, scalable consistency testing protocol to ensure cross-stage alignment in industrial LLM development without model artifacts. It defines two complementary tests—response-wise CT and model-wise CT—implemented via a LightGBM classifier and a paired t-test, respectively, and supports training with a crafted Chinese Wikipedia-based dataset. Empirical results show SimCT outperforms baselines, with ablation confirming the importance of query-type signals, and case studies illustrating its practical discriminative power. The work offers a concrete SOP for LDLC, enabling faster, more reliable deployment of LLM services and providing open datasets and code for community use.

Abstract

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.

SimCT: A Simple Consistency Test Protocol in LLMs Development Lifecycle

TL;DR

SimCT introduces the LDLC framework and a principled, scalable consistency testing protocol to ensure cross-stage alignment in industrial LLM development without model artifacts. It defines two complementary tests—response-wise CT and model-wise CT—implemented via a LightGBM classifier and a paired t-test, respectively, and supports training with a crafted Chinese Wikipedia-based dataset. Empirical results show SimCT outperforms baselines, with ablation confirming the importance of query-type signals, and case studies illustrating its practical discriminative power. The work offers a concrete SOP for LDLC, enabling faster, more reliable deployment of LLM services and providing open datasets and code for community use.

Abstract

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.
Paper Structure (25 sections, 2 equations, 5 figures, 6 tables)

This paper contains 25 sections, 2 equations, 5 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Model Generation Analysis
  4. LLM Consistency
  5. SimCT Protocol
  6. Response-wise CT
  7. Model-wise CT
  8. Dataset Craft
  9. Training dataset
  10. Test Dataset
  11. Implementation Instance
  12. Experiment
  13. Baselines
  14. Main Result
  15. Ablation Study
  16. ...and 10 more sections

Figures (5)

  • Figure 1: Diagram of LDLC and an example illustrating the necessity of consistency test for LLM-based systems in typical development stages.
  • Figure 2: The overall framework of SimCT involves the response-wise test and the model-wise test (aggregating all response pairs).
  • Figure 3: Dataset Construction Rules
  • Figure 4: SimCT Demo
  • Figure 5: Illustrative cases for SimCT Demo.