BitCal-TTS: Confidence Calibration and Adaptive Stopping Techniques for Quantized Inference of Large Models

BitCal-TTS optimizes the performance of quantized large models for inference under fixed budgets through bit-aware confidence calibration and adaptive stopping mechanisms, without retraining the base model. It addresses the issues of insufficient confidence calibration and suboptimal inference efficiency in quantized models.

With the widespread application of Large Language Models (LLMs) across various fields, the efficiency and cost control of model inference have become key challenges. Quantization technology significantly reduces memory usage and computational overhead by lowering the bit-width of model parameters (e.g., from FP16 to INT8/INT4), enabling large models to be deployed in resource-constrained environments. However, quantized models often face issues of insufficient confidence calibration and suboptimal inference efficiency—especially how to maximize output quality under fixed inference budgets is an important research topic.

BitCal-TTS focuses on solving two core problems of quantized large models for inference: confidence calibration and adaptive inference stopping. Its core technologies include:

1. Bit-aware Confidence Calibration: Dynamically adjusts confidence estimation based on quantization bit-width, analyzing the statistical characteristics of outputs at different bit-widths to accurately evaluate prediction reliability;
2. Adaptive Stopping Mechanism: Dynamically decides whether to terminate inference early based on the confidence of intermediate outputs, prioritizing resource allocation to complex inputs under fixed budgets;
3. No Retraining Advantage: Uses a post-processing calibration strategy that can be directly applied to quantized models, avoiding costly retraining processes.

Confidence Estimation and Calibration

The system collects the output distribution of the quantized model on the validation set, analyzes the relationship between predicted confidence and actual accuracy, and constructs a calibration function to convert raw confidence into reliable estimates. It also considers the impact of quantization bit-width, using corresponding calibration parameters for different bit-widths.

Dynamic Stopping Strategy

The adaptive stopping module evaluates the confidence of the current output at each step of inference, terminating when the confidence exceeds a preset threshold or the maximum number of steps is reached. The threshold can be adjusted based on scenarios: conservative thresholds for high-reliability tasks, and relaxed standards for scenarios with high real-time requirements.

BitCal-TTS is suitable for the following scenarios:

• Edge Device Deployment: Achieve better inference results under fixed computing budgets when running quantized large models on mobile/embedded systems;
• High Concurrency Services: Improve the throughput of online inference services and reduce average response latency;
• Cost-Sensitive Applications: Reduce unnecessary inference steps and lower the operational costs of token-based billing APIs;
• Inference Tasks: Confidence calibration helps identify whether the model truly understands the problem, avoiding hallucinated outputs.

Compared to other quantization optimization solutions, BitCal-TTS has the following advantages:

1. Plug-and-Play: Can be directly applied to existing quantized models without modifying or retraining them;
2. Bit-Width Adaptability: Supports multiple quantization bit-widths, with strong versatility;
3. Resource-Friendly: Minimal additional computational overhead for calibration and stopping logic;
4. Interpretability: The confidence-based decision process has good interpretability.

Limitations

• The calibration effect depends on the representativeness of the validation set; if the distribution of deployed data differs significantly from the validation set, the effect may degrade;
• The threshold of the adaptive stopping strategy needs to be tuned for specific tasks.

Outlook

• Integrate more advanced calibration algorithms (e.g., temperature scaling, Platt scaling);
• Explore learning-based adaptive stopping strategies;
• Extend the technology to multimodal quantized models.

BitCal-TTS provides a practical optimization solution for the actual deployment of quantized large models. Through bit-aware confidence calibration and adaptive stopping mechanisms, it effectively improves the inference efficiency and reliability of quantized models without increasing model training costs, offering a valuable reference implementation for developers and researchers exploring edge deployment or cost optimization of large models.