Evaluating Generative AI: Assessing Accuracy and Reliability

Evaluation Methods for Generative AI: Verifying Accuracy and Reliability

Introduction

In recent years, generative AI, exemplified by ChatGPT, has made remarkable progress and is becoming an indispensable tool in our daily lives and business. However, it has also become apparent that the information generated by generative AI is not always accurate. To ensure the reliability of generative AI and make it more effective, it is essential to evaluate its accuracy and reliability appropriately. This article details the evaluation methods for generative AI, specific metrics, techniques, and future challenges and prospects.

The Importance and Challenges of Generative AI Output Reliability

What is “Hallucination” in Generative AI?

Generative AI learns from a vast amount of data and generates new content based on patterns, but it does not always output correct information. The misinformation or uncertain information generated by generative AI is referred to as “hallucination.” Hallucination occurs when an AI model generates information not included in the training data or outputs misinformation contained within the training data. Additionally, if the AI model is biased in learning from specific data, it may generate biased outputs.

Risks of Low-Reliability Outputs

The low reliability of generative AI outputs can pose various risks:

• Decision Making Based on Incorrect Information: If critical business decisions are made based on incorrect information generated by AI, it could harm the company.
• Promotion of Discrimination and Prejudice: AI-generated discriminatory content could perpetuate social inequalities.
• Loss of Trust: Repeated low-reliability outputs from AI can result in a loss of user trust, hindering AI adoption.

The Necessity of Evaluating Generative AI

To ensure the reliability of generative AI and utilize it more effectively, it is essential to evaluate its accuracy and reliability appropriately. Through evaluation, it is possible to improve model performance, identify issues, and implement corrective measures. Additionally, by disclosing evaluation results, it is possible to provide users with information about AI reliability and promote ethical AI usage.

Objectives of Generative AI Evaluation

The evaluation of generative AI is conducted for various purposes:

• Performance Comparison of Models: Comparing the performance of different AI models to evaluate which model is best suited for specific tasks.
• Identifying and Improving Issues: Identifying weaknesses and problems in AI models to help improve them.
• Enhancing User Experience: Evaluating and improving the quality of AI outputs to enable users to utilize AI more effectively.
• Reducing Ethical and Legal Risks: Evaluating AI outputs to ensure that the content generated by AI does not cause ethical or legal issues and correcting it if necessary.

What to Evaluate: Key Metrics for Generative AI

Evaluating the quality of generative AI outputs is essential for improving models and ensuring appropriate use. However, generative AI requires selecting suitable metrics depending on the diverse output formats (text, images, audio, video, etc.) and tasks (translation, summarization , creation, etc.). Here, we will explain the evaluation metrics for text and image generation AI, focusing on automatic evaluation metrics, human evaluation, and task-specific evaluation.

Evaluation Metrics for Text Generation AI

Automatic Evaluation Metrics

Automatic evaluation metrics are calculated by computers, providing the advantage of objective evaluation. Representative automatic evaluation metrics include:

• *BLEU (Bilingual Evaluation Understudy) *: Often used to evaluate machine translation, measuring the similarity between generated and reference sentences.
• *ROUGE (Recall-Oriented Understudy for Gisting Evaluation) *: Often used to evaluate summarization tasks, measuring the similarity between generated and reference summaries.
• *METEOR (Metric for Evaluation of Translation with Explicit ORdering ) *: An improved version of BLEU, considering not only word accuracy but also synonyms and word order.
• ** BERTScore **: Using the BERT (Bidirectional Encoder Representations from Transformers) model to measure the semantic similarity between generated and reference sentences.

Human Evaluation

Human evaluation can assess elements such as fluency, accuracy, relevance, and creativity that automatic evaluation metrics may not capture. However, it can be subjective and time-consuming. Methods for human evaluation include surveys and ratings, where evaluators read generated sentences and score various evaluation items to assess sentence quality.

Task-Specific Evaluation

Task-specific evaluation metrics also exist for specific tasks such as question answering, summarization, and translation.

• Question Answering: SQuAD (Stanford Question Answering Dataset) is a widely used dataset for evaluating question answering tasks, assessing whether generated answers are appropriate responses to questions.
• Summarization: GLUE (General Language Understanding Evaluation) is a benchmark covering various natural language processing tasks, including summarization.
• Translation: WMT (Workshop on Machine Translation) is an international competition that evaluates translation accuracy across various languages.

Practical Approaches to Evaluating Generative AI

Evaluating generative AI requires not only theoretical metrics but also practical approaches to verify its accuracy and reliability. Here, we explain specific steps from constructing evaluation datasets to conducting evaluation experiments and analyzing results.

Constructing Evaluation Datasets

Appropriate evaluation datasets are essential for evaluating AI models. Evaluation datasets are not used for model training and are used to assess the generalization performance (predictive ability on unknown data) of models.

• Data Collection and Selection:
• Data Sources: It is important to collect evaluation datasets from sources different from the model’s training data. For example, various sources such as websites, books, papers, and social media can be used to create diverse datasets.
• Data Types: Evaluation datasets should include various types of data that the model needs to handle. For example, a text generation AI should include news articles, novels, poetry, code, etc., from various genres.
• Data Volume: It is necessary to ensure a sufficient volume of data. Too little data can reduce the reliability of evaluation results. Generally, thousands to tens of thousands of data points are required.
• Data Annotation (Labeling):
• Creating Correct Data: For supervised learning, it is necessary to annotate evaluation datasets with correct labels. For example, in a text classification task, each sentence should be labeled as “positive,” “negative,” “neutral, “etc.
• Using Annotation Tools: Annotation work is time-consuming, but using annotation tools can improve efficiency. For example, tools like Doccano and Label Studio provide functions to support text, image, and audio annotation.
• Splitting Evaluation Datasets:
• Training, Validation, and Test Data: Evaluation datasets are divided into training data, validation data, and test data. Training data is used for model training. Validation data is used for hyperparameter tuning and early stopping decisions. Test data is used for the final performance evaluation of the model.

Conducting Evaluation Experiments

Once the evaluation dataset is constructed, evaluation experiments are conducted using the AI model.

• Comparison with Baseline Models: First, prepare the AI model to be evaluated and baseline models (existing models or models that generate random outputs).
• Comparison of Multiple Models: Comparing multiple AI models can help understand each model’s strengths and weaknesses.
• Comparison with Different Parameter Settings: Changing the AI model’s parameters (learning rate, batch size, etc.) to find the optimal parameter settings.

Analyzing and Interpreting Evaluation Results

The results of evaluation experiments should be analyzed and interpreted by combining quantitative metrics and human evaluation.

• Interpreting Quantitative Evaluation Results: Analyzing numerical data obtained using automatic evaluation metrics to objectively evaluate model performance.
• Analyzing Human Evaluation Results: Analyzing human evaluation results obtained through surveys and ratings to comprehensively evaluate the quality of model outputs.
• Improving Models Based on Evaluation Results: Improving the model’s structure or learning methods based on identified issues and problems from the evaluation results and retraining the model.

Efforts to Improve the Reliability of Generative AI

Improving the reliability of generative AI efforts requires from both technical and social aspects.

Developing Explainable AI (XAI)

Explainable AI (XAI) refers to techniques that make AI’s decision-making basis understandable to humans. XAI contributes to solving the black-box problem of AI and improving AI reliability.

• *LIME (Local Interpretable Model-Agnostic Explanations) *: A technique that identifies the features that most influence model predictions and explains them in an interpretable way.
• *SHAP ( SHapley Additive exPlanations ) *: A technique that uses Shapley values from game theory to quantitatively explain how much each feature contributes to prediction results.
  XAI is particularly important in fields where AI’s decisions impact human lives and property, such as medical diagnosis support, financial risk assessment, and autonomous driving.

Efforts to Mitigate Bias
AI models can reflect biases contained in training data. To mitigate biases, the following efforts are important:

• Using Diverse Datasets: Using diverse datasets that do not bias toward specific groups to train AI models can reduce biases.
• Developing Bias Detection and Correction Algorithms: Algorithms are being developed to automatically detect and correct biases in training data and AI models.
• Introducing Fairness Evaluation Metrics: Introducing metrics to evaluate the fairness of AI models and encouraging developers to be aware of fairness in model development is crucial.

Automated Fact-Checking

Generative AI is also used for fact-checking to prevent the spread of misinformation and fake news. Large-scale language models can reference vast information sources to verify the truthfulness of text and present reliable information sources.

Current State of Fact-Checking by Generative AI

Currently, fact-checking by generative AI is mainly conducted using the following two methods:

• Claim-Based Fact-Checking: Verifying the truthfulness of specific claims or information. For example, AI can automatically check whether a politician’s statements or the content of news articles are factual.
• Evidence-Based Fact-Checking: Presenting evidence to support a specific claim. For example, AI evaluates the credibility of news articles and provides other sources that support the articles.

Future Challenges and Prospects

Fact-checking by generative AI is still in its developmental stages, and several challenges remain:

• Difficulty in Understanding Context: Generative AI often finds it challenging to fully understand context, which can lead to incorrect judgments.
• Multilingual Support: Since generative AI primarily learns from English information, it can be difficult to perform fact-checking in other languages.
• Ethical Issues: The use of generative AI for fact-checking could potentially infringe on freedom of speech and expression.

To address these challenges, it is necessary to develop more advanced natural language processing technologies, enhance multilingual support, and establish ethical guidelines.

Conclusion: Evaluating and Enhancing the Reliability of Generative AI is Essential for Future AI Development

Generative AI is a technology that has the potential to significantly transform our daily lives and business. However, ensuring the accuracy and reliability of its output is crucial for the healthy development of AI. This article has explained various evaluation methods for generative AI, including different metrics, techniques, and future challenges and prospects.

Evaluating generative AI is not just about measuring model performance but also involves enhancing AI reliability, promoting ethical use, and contributing to society. By collaborating among AI developers, users, and the entire society, we can build a future where we can fully enjoy the benefits of AI.

Key Points in Generative AI Evaluation:

• Choosing Appropriate Evaluation Metrics: Depending on the task and purpose, select appropriate evaluation metrics, such as BLEU or ROUGE for text generation AI, and IS or FID for image generation AI.
• Constructing Evaluation Datasets: Build evaluation datasets containing diverse data to accurately assess the generalization performance of the models.
• Human Evaluation: Incorporate human evaluation in addition to automated metrics to perform a more comprehensive assessment.
• Introducing Explainable AI (XAI): By introducing XAI, which can explain the reasoning behind AI decisions, we can increase AI transparency and reliability.
• Ethical Considerations: Always be aware of the ethical issues that accompany the use of generative AI and strive for responsible AI use.

Generative AI is still a developing technology with limitless potential. Future technological innovations are expected to evolve further generative AI, enriching our lives and society.