Guide to LLM evaluation and its critical impact for businesses

Introduction

In the evolving world of AI, Large Language Models (LLMs) have emerged as a cornerstone technology, powering a wide range of applications from virtual assistants to content generation tools. As businesses increasingly leverage these models to streamline operations and enhance customer experiences, understanding the importance of evaluating LLMs becomes crucial. In this article, we'll explore why evaluation is a critical step in ensuring their effectiveness and reliability, and the solutions that Giskard proposes to address this problem.

Understanding Large Language Models (LLMs) and their evaluation challenges

LLMs are AI models capable of understanding and generating human-like text. They are trained on vast amounts of text data, enabling them to generate coherent and contextually relevant responses. However, due to their complexity and the diversity of the data they are trained on, they can sometimes generate incorrect, biased, or misleading information. This is where evaluation comes into play.

Why LLM Evaluation is important: Ensuring reliable LLM outputs

1. Detecting LLM hallucinations

Evaluating LLMs helps ensure that they are generating accurate and reliable information. This is particularly important for businesses, as inaccurate information can lead to misinformed decisions, negatively impacting operations and reputation.

2. Mitigating Bias in LLM outputs

LLMs can inadvertently learn and replicate biases present in their training data. Evaluation helps identify and mitigate these biases, ensuring that the model's outputs are fair and unbiased.

3. Enhancing LLM Safety and Ethical Use

LLMs can generate harmful, inappropriate, or misleading content, which can significantly impact a business's reputation and operations. LLMs can be vulnerable to adversarial attacks, where inputs are deliberately designed to manipulate or mislead the model. The aim of these attacks can range from damaging the company's image to causing a Denial of Service (DoS), disrupting the model's functionality and availability. These attacks can lead to severe consequences, including loss of customer trust, regulatory penalties, and financial losses.

Evaluation plays a crucial role in identifying these vulnerabilities and strengthening the model's robustness against such attacks.

4. Protecting Sensitive data

LLMs may reveal sensitive information present in their training data (Personal Identifiable Information, API secrets …). These attacks are particularly dangerous as the obtained sensitive data is accessed by someone who knows how to exploit it to damage the company. Evaluation can help identify and mitigate these privacy risks, ensuring that the model does not disclose sensitive company data and adheres to data protection regulations.

5. Optimizing LLM Performance

Regular evaluation provides insights into a model's strengths and weaknesses, enabling businesses to fine-tune and improve its performance over time.

How to Evaluate LLMs: evaluation methods & tools

Evaluating LLMs involves a combination of automatic and manual methods. Automatic methods involve using metrics like perplexity (a measurement of how well the model predicts the next word in a sequence) and BLEU scores (a metric that compares the model's output with a set of reference translations) to assess the model's performance. However, these methods often fail to capture nuances like coherence and factual accuracy. Therefore, manual evaluation, involving human reviewers, is also crucial.

Giskard provides a comprehensive solution for evaluating LLMs, addressing both the automatic and manual aspects of the process.

• Automated Testing: Giskard offers a suite of automated tests to evaluate LLMs systematically. These tests cover a wide range of vulnerabilities, from bias and safety to privacy and ethical concerns. By automating these tests, Giskard enables organizations to evaluate their models efficiently and consistently.
• Customizable Test Cases: Giskard allows users to create and customize test cases based on their specific needs and contexts. This feature ensures that the evaluation process is tailored to the unique requirements of each business.
• Human-in-the-loop Evaluation: Recognizing the importance of human judgment in evaluating LLMs, Giskard incorporates a human-in-the-loop approach. This means that human reviewers can validate the results of the automated tests, ensuring that the evaluation is thorough and accurate.

By leveraging Giskard's testing framework, businesses can ensure comprehensive and effective LLM evaluation. Giskard offers end-to-end support, from automating tests or monitoring performance, to incorporating human judgment.

Automated LLM eval for RAG applications with Giskard

On the automation side, Giskard offers a suite of tools to audit LLMs or applications based on them, such as retrieval-augmented generation (RAG) chatbots.

One of these tools is RAGET (RAG Evaluation Toolkit), a toolkit specifically designed to evaluate RAG agents automatically. RAGET generates a list of domain-specific questions made to target flaws in precise parts of the RAG (Router, Retriever or Text Generation). The resulting test set can then be used to evaluate the RAG agent, streamlining the evaluation process and enabling comprehensive testing.

In addition to RAGET, we also offer a Scan feature in our library that automatically detects hidden vulnerabilities in LLMs. This scan can identify a wide range of issues, including hallucination, harmful content, prompt injection, data leakage, and more.

LLM human evaluation: AI Red Teaming & Giskard Hub

On the human side, our AI Red Team incorporates human expertise by developing holistic threat models and real-world attack scenarios. This helps businesses identify potential threats and weaknesses in their LLM applications, providing actionable insights to mitigate critical risks like misinformation and data leaks.

Complementing this, our LLM Hub acts as a central repository to store model versions, test cases and test datasets. Thanks to its model debugger, it also allows collaboration with business experts, who can provide feedback and domain-specific tests.

By combining automatic testing and human expertise, Giskard offers a well-rounded approach to evaluating LLMs. This holistic evaluation process helps businesses ensure the quality, safety, and reliability of their LLM deployments, unlocking the full potential of these AI systems while effectively managing their risks.

Conclusion

As businesses increasingly adopt LLMs, understanding the importance of evaluation becomes essential. Regular and comprehensive evaluation ensures that these models are accurate, unbiased, safe, and performant, ultimately helping businesses harness the full potential of AI and mitigate its risks.

Failure to properly evaluate and implement guardrails for LLMs can have severe consequences, as demonstrated by the famous Chevrolet chatbot incident in 2023. The dealership's chatbot, aimed at assisting customers, went viral after users discovered they could jailbreak it. Most notably, the bot offered to sell a user a car for just one dollar, even adding "That's a legally binding offer—no takesie backsies." This incident highlighted the risks of deploying LLMs without rigorous testing, safeguards, and evaluation to prevent exploitation and unintended behavior, which could lead to legal and financial implications. 

At Giskard, we specialize in providing robust testing frameworks for LLMs and other AI models. Our tools enable businesses, like L’Oréal, AXA, or Michelin, to thoroughly evaluate their models, ensuring they meet the highest standards of accuracy, fairness, and safety. Reach out to us today to learn more about how we can help you make the most of your AI investments.