Large Language Models (LLMs) are rapidly transforming the financial landscape. From fraud detection and personalized customer service to complex risk assessment, these robust AI systems are revolutionizing how financial institutions operate. This potential, however, necessitates careful oversight. How can organizations ensure their LLMs are reliable, accurate, and ethically sound when handling sensitive financial data?
As an example of how widespread AI usage is in the financial sector, research by the Bank of England shows that 75% of UK financial services firms are already using AI, with a further 10% planning to do so. This widespread adoption underscores the growing importance of LLMs in finance, but also highlights the potential risks. Biased outputs, inaccurate information, or unexpected behavior could lead to substantial financial losses, reputational damage, and regulatory violations.
A rigorous LLM evaluation process is indispensable for mitigating these risks, identifying potential weaknesses, and ensuring compliance with strict industry regulations. Let's explore the steps involved in conducting a comprehensive and useful LLM evaluation.
What Is LLM Evaluation?
LLM evaluation is a series of tests and benchmarks that aim to assess how well a Large Language Model functions in real-world scenarios. These tests might include anything from answering customer questions and generating different creative text formats to analyzing financial data or translating languages.
Essentially, it's a way to put your LLM through its paces and see how it performs. Evaluation is crucial because it helps identify the model's strengths and weaknesses, as well as potential privacy and security risks. This process is closely related to model validation, which focuses on assessing the model's overall quality and fitness for its intended purpose.
Evaluating the LLM against relevant metrics throughout its lifecycle enables developers to ensure the LLM is constantly improving and stays aligned with business goals and intended outcomes.
Who Needs LLM Evaluations, and Why?
Anyone developing or deploying an LLM for real-world applications needs to conduct thorough evaluations. This ensures that the model performs as expected and meets the requirements for its intended use. Different stakeholders have different reasons for prioritizing LLM evaluation.
Here are a few key examples:
Financial Organizations - Rely heavily on precision, compliance, and security, making LLM evaluation crucial for their operations.
Developers and Researchers - Whether they are part of a company developing AI or one that’s only using it, developers and researchers must refine models to understand their strengths and weaknesses to improve training methods, and evaluate whether new training data produces better results.
Enterprise Users - Must ensure the LLM fits their business needs, for example, a customer support chatbot must provide accurate and friendly responses.
Security Teams - Need to identify and mitigate risks like data leaks, prompt injections, and other vulnerabilities that could harm users or systems. Ensuring DevOps compliance is essential for maintaining the security and integrity of LLM development pipelines.
Ethics Committees and Policymakers - Ensure the models align with ethical guidelines, avoiding bias, toxicity, or harmful behavior—which is crucial for building trust in AI and complying with standards like ISO 42001.
Educators and Social Scientists - Enables studying how LLMs interact with humans and understand the societal implications of their use.
8 Critical Metrics for LLM Evaluation
The most common metrics for evaluating LLMs are the same across all implementations, but the relevancy of specific metrics may differ. After all, you don't want your LLM to hallucinate regardless of the field, but it may be more critical when analyzing finances than when generating text. Here are the eight of the most important metrics used in LLM evaluation:
Answer Relevancy - Measures whether the model provides responses that are pertinent to the user’s query.
Correctness - Evaluates the factual accuracy of responses compared to a ground truth, which is a set of known correct answers.
Hallucination Rate - Measures the rate at which the model generates incorrect or nonsensical outputs.
Contextual Relevance - Determines whether a model uses the most relevant context in retrieval-augmented generation.
Toxicity - Ensures outputs are free of offensive or harmful content.
Bias Detection - Identifies prejudiced or unfair outputs based on race, gender, or other sensitive attributes.
Task-Specific Metrics - Includes application-driven metrics like BLEU Score (translation) and ROUGE Score (content summarization).
User Experience Metrics - Measures properties such as response time and user satisfaction.
5 Common LLM Evaluation Methods
There are many LLM evaluation methods, and new techniques are being developed as fast as LLMs are progressing. Here's a brief overview of the most commonly used methods:
1. Automated Evaluation Methods
Automated methods are essential for scalability, as it is impossible to evaluate high-capacity models manually. These include:
Benchmarking with Standard Datasets - GLUE/SuperGLUE and SQuAD use predefined datasets with inputs and expected outputs to measure performance. These methods provide standardized and scalable evaluations with limited flexibility.
Statistical Scorers - BLEU, ROUGE, and METEOR provide statistical analysis by comparing expected output with actual output. Statistical scorers are ineffective in evaluating reasoning, which is why they are considered non-essential tools.
Model-Based Scorers - BERTScore and BLEURT are forms of regression analysis that compare candidates against the output of trained LLMs to provide a reliable, accurate comparison.
G-Eval - A framework that uses high-capability language models, like GPT-4, to evaluate the outputs of other LLMs. G-Eval leverages the advanced reasoning and natural language understanding capabilities of powerful LLMs to provide more nuanced and human-like assessments.
2. Human-Driven Evaluation Methods
Human evaluators are slow and subjective, but they can provide insights into real-world user expectations. These methods include:
Human Judgment - Involve human reviewers to assess LLM outputs based on qualitative criteria. Human evaluators can capture nuances and subjective opinions that translate directly into real-world user experience.
Crowdsourced Feedback - Collecting feedback from a diverse group of users grants a broad range of perspectives and is more scalable than manual testing.
3. Hybrid Evaluation Methods
Hybrid methods combine the efficiency of automated techniques with the nuanced judgment of human evaluation. These approaches offer a balance between scalability and the ability to capture more complex aspects of LLM performance.
QAG (Question-Answer Generation) Scoring - Combines automated question generation with human-like evaluation. This method extracts claims from model responses, compares them with a reference, and scores metrics like accuracy or relevancy.
Dynamic Testing with Adversarial Data - Uses mutation algorithms or attack modules to generate dynamic datasets that challenge the model. This method helps test robustness and resilience to prompt injections and other attacks.
Interactive Testing - Evaluate performance through interactive, multi-turn tasks such as dialogue consistency and information retention in multi-step problem-solving.
4. Specialized Methods for LLM Systems
When evaluating LLM systems, such as retrieval-augmented generators or tool-integrated systems, different components may require tailored approaches, such as:
Retriever Evaluation - Contextual precision and contextual recall are methods used to evaluate LLMs in specialized cases.
Generator Evaluation - Measures faithfulness, coherence, and hallucination rates.
Integrated System Testing - Assess how retrievers and generators work together to deliver meaningful outputs.
5. Real-world Feedback and Observability
Monitoring performance in live environments is essential for gathering actionable insights. Key metrics tracked include:
Response times - How quickly the LLM responds to user queries.
Error rates - The frequency of incorrect or problematic outputs.
User engagement - How users interact with the LLM and whether they find it helpful and satisfactory.
Bias and fairness - Monitoring for any unintended biases in the LLM's outputs.
Security vulnerabilities - Identifying and addressing any potential security risks.
Real-world feedback provides valuable data for ongoing improvement and helps ensure the LLM remains effective and aligned with user needs and ethical considerations.
Comparison of LLM Evaluation Methods
To help you compare these methods, we've summarized their strengths and weaknesses in this table:
Method | Strengths | Weaknesses |
Benchmarking | Standardized and scalable | Limited to predefined tasks |
Statistical Scorers | Simple and efficient | Poor semantic understanding |
Model-Based Scorers | Handles context and meaning well | Inconsistent due to the probabilistic nature |
Human Judgment | Captures nuance and subjectivity | Expensive and slow |
LLM-Eval | High alignment with human preferences | Requires careful prompt engineering |
QAG Scoring | Balances automation with reasoning | Can be resource-intensive |
Adversarial Testing | Reveals weaknesses in robustness | Challenging to design effective tests |
Real-world Feedback | Reflects true user experiences | Requires ongoing monitoring and updates |
6 Essential Steps for a Useful LLM Evaluation
1. Leverage Citrusˣ for a Useful LLM Evaluation
Citrusˣ simplifies the complexities of LLM evaluation by providing you with the tools and expertise to ensure your models are accurate, fair, and robust. The platform offers many capabilities to support your evaluation journey, including automated frameworks, customizable metrics, and robust reporting tools.
Beyond just measuring performance, Citrusx helps you manage the risks associated with deploying LLMs. This includes identifying and mitigating potential biases, ensuring compliance with relevant regulations, and providing tools for ongoing monitoring and risk assessment.
2. Choose an Evaluation Metric
Start by selecting the metric you wish to evaluate, such as correctness, relevancy, toxicity, bias, or task-specific measures. Remember to choose effective metrics that are quantitative, reliable, and accurate for the best results.
For example, financial applications will require a low hallucination rate, as mistakes may be costly. Citrusˣ can help you identify the most relevant metrics for your specific industry and use case.
3. Produce a “Golden Dataset”
A "golden dataset" is a carefully curated set of high-quality data, including inputs and corresponding outputs, that serves as a benchmark for evaluating your LLM.
Adhering to data governance principles is essential when creating and managing your golden dataset to ensure data quality, integrity, and compliance with relevant regulations. Your data should be relevant, representative, accurate, and unbiased.
4. Curate Benchmark Tasks
Design custom tasks that comprehensively test the model's capabilities according to your needs. Use standard benchmarks and tasks such as GLUE, SuperGLUE, or SQuAD for fast, effective, and scalable testing as a baseline.
Consider including adversarial testing and edge-case scenarios. Citrusˣ complements this process by providing tools to validate the semantic space created by embedding models, ensuring benchmarks effectively test the model across the entire data space. By generating a high-dimensional map of the model’s behavior and creating a database of measurements, Citrusˣ enables the calculation of key metrics, offering deeper insights into model performance beyond standard benchmarks.
5. Run Your Evaluation
Once you have chosen your metrics, methods, and dataset, implement your evaluation pipeline. Consider both automated and human evaluation methods. Automated tools and frameworks like G-Eval or DeepEval enable scalable and efficient testing.
Human evaluations are crucial for capturing nuanced judgments and real-world user satisfaction. Citrusˣ provides a comprehensive platform for running your evaluations, with support for both automated and human-in-the-loop processes.
6. Analyze Results
Review and analyze your evaluation results to identify strengths, weaknesses, and areas for improvement. Focus on actionable insights, such as addressing high hallucination rates or improving response relevancy for specific queries.
Citrusˣ's robust reporting and visualization tools will help you analyze your results, identify trends, and track progress over time. Remember that LLM evaluation is often an iterative process. Use the insights gained from each evaluation to refine your model, adjust your evaluation methods, and conduct further evaluations as needed.
Streamline LLM Evaluation with Citrusˣ
LLM evaluation is crucial but complex, especially for industry-specific applications where standard benchmarks might not be enough to ensure regulatory compliance. Building and maintaining customized evaluation processes can be resource-intensive. Fortunately, platforms like Citrusˣ simplify this process by offering a specialized AI validation and risk management solution.
Citrusˣ streamlines LLM evaluation with tools validation, bias detection and mitigation, and performance monitoring that help organizations deploy LLMs effectively and ethically. It provides:
Comprehensive Evaluation - Validate and monitor AI models for accuracy, robustness, and governance throughout the entire LLM lifecycle.
Industry-Specific Solutions - Go beyond typical evaluation frameworks to meet the specific needs and compliance requirements of your industry.
Increased Efficiency - Reduce the manual effort involved in creating and maintaining customized LLM evaluation processes.
Actionable Insights - Dive deep into local samples in real-time to understand your model's behavior and identify areas for improvement.
Become a beta tester of the new Citrusˣ RAGRails for RAG in LLM to discover how it validates embedding models, monitors LLM results, and ensures compliance.
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6 Essential Steps for a Useful LLM Evaluation
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