Gets Assured

Building reliable models

For businesses relying on machine learning, quality assurance in model deployment and operation is essential. A crucial part of QA involves understanding evaluation metrics. When making predictions, many models output inference-level confidence scores (e.g. GPT's logprobs), which indicate the probability that the inference is correct. In contrast, the accuracy score is a holistic measure that reflects the percentage of correct predictions out of the total predictions made by the model.

A high-confidence output from a model with low accuracy shouldn't be trusted.

For example, a retail company could use an ML model to predict customer purchase behavior. While the model might confidently predict purchases, the overall accuracy could still vary based on the quality of the training data and even the model architecture. Therefore, businesses should always strive to get and keep models to an acceptable level of accuracy.

harmonic mean ensures models are reliable and accurate by implementing rigorous quality measurement tailored to each business need.

Evaluating Large Language Models

LLMs such as GPT, Llama, and Claude generate natural language text, requiring specific approaches to evaluate their quality and effectiveness:

1. Perplexity:
   Perplexity measures how well the model predicts a sample. It assesses the uncertainty the model has regarding the next word it needs to generate. Lower perplexity indicates better performance and understanding of language patterns.

2. BLEU and ROUGE Scores:
   BLEU (Bilingual Evaluation Understudy) and ROUGE (Recall-Oriented Understudy for Gisting Evaluation) are benchmarks used primarily for tasks like translation and summarization. These metrics compare the generated text against reference texts to assess overlap and n-gram similarities.

3. Human Evaluation:
   For nuanced tasks such as story generation or conversation, human judgment often plays a key role. Evaluators rate outputs based on fluency, coherence, relevance, and quality. This subjective measure is crucial where semantic understanding is required.

4. Task-Specific Metrics:
   Depending on the use case, whether it be summarization, sentiment analysis, or question answering, specific accuracy or success metrics relevant to each task can be determined. For instance, reading comprehension tasks may utilize metrics specific to information retrieval accuracy.

5. Bias and Fairness Evaluation:
   It’s critical to assess models for fairness and bias. Metrics that gauge ethical alignment and absence of biased outputs, however, are subject to ongoing research and may involve custom evaluation frameworks.

6. Prompt Effectiveness:
   Ensuring prompts lead to high-quality outputs is central to refining LLM performance. We use A/B testing and prompt engineering techniques to determine which prompts yield the best results.

Precision, recall, and F1

Precision, recall, and F1—the so-called confusion matrix metrics—are used to evaluate classification models. Precision measures the proportion of correct positive predictions made by the model relative to the total positive predictions it made. Recall, on the other hand, assesses the proportion of actual positives correctly identified out of all possible positives. The F1 score is the harmonic mean of the two, providing an important blended metric, and is often useful when there is an uneven class distribution.

Imagine a healthcare model used to predict disease in patients. If the priority is to minimize false alarms (false positives), then precision becomes a vital metric. Conversely, if it is critical to identify every possible case, even at the risk of false positives, recall becomes more important. For example, in cancer screening, recall is often prioritized to ensure potentially affected patients are not overlooked.

By understanding these nuances, harmonic mean helps businesses define and measure the best-fit success metrics.

Metrics for computer vision

In the realm of computer vision (CV), evaluating model performance requires specialized metrics. Intersection over Union (IoU) is particularly essential for tasks like object detection and segmentation, measuring the overlap between predicted and true bounding boxes (or segmented areas). It’s often expressed as the area of overlap divided by the area of union, ensuring precise localization or segmentation. Another critical metric is the Mean Euclidean Distance (MED), which quantifies the average distance between true vs. predicted keypoints (like facial landmarks), crucial for assessing alignment in models like facial recognition.

Ongoing monitoring

Quality assurance doesn't end with model deployment; continuous monitoring is imperative. As new data emerges, existing model assumptions may no longer hold, making recalibration necessary to maintain efficacy and relevance.

Businesses in the finance sector, for instance, base interest rate predictions or credit scores on historical economic data. However, economic conditions fluctuate, thus necessitating regular updates and fine-tuning of models. In this context, ongoing monitoring ensures ML models deliver consistent performance even amid data variability.

harmonic mean offers ongoing support by maintaining and fine-tuning models, ensuring they adapt to new data and business needs, safeguarding quality across model versions and refinements.

The value of RLHF

Reinforcement Learning with Human Feedback (RLHF) enhances model accuracy and user satisfaction by incorporating human insights into the training loop. RLHF involves an iterative process where human feedback is used to refine and adjust model behaviors, ensuring alignment with user expectations and ethical standards. Implementing RLHF provides an additional layer of quality control.

In AI-driven customer support systems, human feedback can teach models to handle nuanced customer queries more effectively. This quality assurance process helps organizations deliver high-quality, consistent, and human-like interactions with clients.

Conclusion

Incorporating comprehensive quality assurance strategies in machine learning ensures models are robust, reliable, and aligned with business goals. By focusing on key metrics like precision, recall, F1, and incorporating human-centered approaches like RLHF, businesses can confidently leverage ML models to drive performance.