Introduction
The rise of Large Language Models (LLMs) has emerged as a crucial factor in creating and advancing intelligent business operations. However, in real-world scenarios, LLMs are not flawless and missteps on their part can erode stakeholder trust rapidly. A recent McKinsey survey identified that 48% of leading gen AI adopting respondent organizations had cited risk and striving for responsible AI as impediments to value realization. This establishes why LLMs need to be evaluated rigorously for their effectiveness, accuracy, and regulatory compliance before deployment.
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"Comprehensive, continuous, and collaborative evaluation that looks beyond the numbers, proactively addresses emerging hazards, and centers diverse human values will be instrumental in realizing AI's immense potential for good." — Sylvain Duranton, Leader, BCG X, Forbes, March 14, 2024. |
What is LLM Evaluation?
LLM evaluation is the systematic and rigorous testing of LLMs under different conditions to ensure efficacy, ethics, and safety post deployment. How well do they respond to questions? Do they understand the context? The answers to such key questions guarantee long-term model performance and reliability.
This article will act as a guide on LLM evaluation, casting a light upon essential metrics, benchmarks, frameworks, and best practices.
LLM Evaluation Metrics with Real-World Examples:
Objective metrics help understand the consistency and reliability of the LLM’s performance. No single metric can capture the entire spectrum of the model’s performance and many are used in conjunction. Let’s look at a few of the most important LLM evaluation metrics along with examples of live scenarios.
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BLEU (Bilingual Evaluation Understudy) Score |
Evaluates the quality of the generated text by comparing it with the reference source. BLEU score is high if several words overlap between the machine output and human reference. The scores range from 0 to 1, with higher scores indicating a better match. |
Used in e-commerce to assess the quality of auto-generated product descriptions by comparing them to human-written examples. |
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ROUGE (Recall-Oriented Understudy for Gisting Evaluation) |
Primarily used to evaluate text summaries from NLP models. It uses n-grams, sequences, and word pairs, to find overlaps between the output and the reference. |
In retail CRM, ROUGE ensures personalized customer communications are clear and aligned with promotional campaigns. |
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METEOR (Metric for Evaluation of Translation with Explicit Ordering) |
If the objective is to generate accurate translations, METEOR assesses based on a match between the LLM’s and human-produced translations. |
METEOR ensures localized marketing messages are consistent across languages and preserves the tone and meaning of the original text. |
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Levenshtein Distance |
Also referred to as edit distance, it evaluates spelling correctness. It calculates the minimum number of single-character edits required to change the output text into the input text. |
Used in e-commerce to evaluate the accuracy of automated search results by measuring how closely they match search queries, even with typos. |
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Perplexity |
Even though Perplexity doesn’t give an idea about quality or coherence, it measures the probability of the model predicting a sample of text. |
Perplexity can be used in financial forecasting to measure how well a model predicts market trends and economic scenarios. |
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F1 Score |
It evaluates the relevance and completeness of responses. The score ranges from 0 to 1, with 1 implying perfect accuracy. |
It can be used to evaluate the performance of an AI model in identifying medical conditions from patient records, balancing precision and recall for accurate diagnoses. |
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BERTScore |
It relies on pre-trained language models like BERT and focuses more on meaning than comparing the exact word matches. The similarities between the reference text and output are used to produce a final score which indicates a high degree of semantic overlap. |
Helps analyze customer reviews and ensures the system captures the sentiment behind the reviews. |
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Toxicity Score |
It measures the level of harmful content in generated text, along an ascending scale, where higher scores indicate greater toxicity. |
Social media platforms might employ this metric to flag or remove posts that exceed a certain toxicity threshold, creating a safer environment for users. |
Even though such metrics help evaluate LLMs, it is best to pair them with other methods, especially when the activities being handled are complex, critical and require human judgment.
LLM Evaluation Benchmarks:
LLM evaluation benchmarks help evaluate the large language model’s performance by offering a standardized set of tasks. They provide a consistent way to compare the LLM’s performance vis-a-vis other models.
HellaSwag |
This benchmark is designed to see what will happen next in a scenario. The HellaSwag benchmark challenges the LLM model to pick out the situation that will arise out of the many possible options. It has questions that are easy for humans, but might be challenging for models. |
MMLU (Massive Multitask Language Understanding) PRO |
It consists of multiple-choice questions with 10 choices in different domains. Some of the questions even require the reasons behind the choices made. |
SQuAD (Stanford Question Answering Dataset) |
This benchmark has more than 100,000 question-answer pairs that are human-written. There is also a crowd-sourced set of 50,000 unanswerable questions to find out whether the models are capable of determining if the source data itself does not offer a reliable answer. |
IFEval |
Part of the Open LLM Leaderboard by Hugging Face, IFEval evaluates the capability of the LLMs to follow instructions that are provided in natural language. The benchmark contains 500+ prompts with clearly spelled-out instructions. |
BIG-Bench HARD |
It is based on the Beyond the Imitation Game Benchmark. It contains 200 plus tasks across a variety of task types and domains. They focus on 23 BIG-Bench tasks, for which other LLM models couldn’t outperform the average human rater. |
GLUE |
General Language Understanding Evaluation tests LLMs for nine different tasks. They range from answering questions to analyzing sentiments and figuring out the next logical sequence. This benchmark makes it easy to compare the performance of the model for these different tasks since it awards a single score. |
SuperGLUE |
SuperGLUE was introduced when the LLM models scored better than humans on the GLUE framework. It entails tasks that are more complex. However, with improving rigour at every stage of development, LLMS have begun outperforming humans on the SuperGLUE framework as well. |
MT-Bench |
This benchmark focuses on how well the LLM converses by asking follow-up questions and gauging its ability to follow instructions. The multi-turn benchmark evaluates the model’s ability to participate in conversations that are engaging, coherent, and valuable. The model uses the LLM-as-a-judge approach as superior LLMs like GPT-4 are used to determine the quality of the responses. |
MATH |
This dataset contains 12,500 competition-level mathematics problems that can challenge the LLMs. The math problems come with step-by-step solutions. |
PyRIT |
A security-related benchmark, it stands for Python Risk Identification Tool for generative AI (PyRIT). Developed by Microsoft, it evaluates the robustness of the LLMs against harm. |
LLM Evaluation Frameworks:
LLM Evaluation Frameworks are comprehensive methodologies for carrying out LLM model evaluation in a consistent and repeatable manner. They help evaluate how well an LLM is functioning based on various metrics, use cases, and conditions.
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Arize AI Phoenix |
It provides a detailed report of model performance in different segments. It can even identify domains where the model might not work according to expectations. |
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TruLens |
An open-source framework, TruLens is a good choice if the objective is to explain the decision-making process of your LLM model. |
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Prompt Flow |
It can create and evaluate complex LLM workflows and is great for testing multi-step processes. |
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Parea AI |
This framework provides detailed insights into the performance of the LLM model. |
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MLflow |
It offers an intuitive developer experience and can run evaluations in your own evaluation pipelines. |
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RAGAs |
It’s an evaluation framework for Retrieval Augmented Generation (RAG). It uses the latest research into the RAG metrics, but doesn’t boast of evolved features and is also considered inflexible as a framework. |
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Deepchecks |
It offers a unique and evolved perspective on the dashboards and the visualization UI. |
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DeepEval |
It’s designed in such a way that it can be easily integrated into existing ML pipelines. It provides a wide array of evaluation metrics. |
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TruthfulQA |
The framework is designed to test whether LLMs provide accurate, truthful, and non-deceptive responses. It helps identify hallucinations in LLMs and ensures adherence to ethical guidelines in varied real-life scenarios.
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LLM Evaluation Challenges:
Let us look at some of the challenges in LLM evaluation.
Data Contamination:
The test data for the LLMs must be separate from the training data. Otherwise, the reliability of most LLM evaluation exercises drops.
Explainability:
It is difficult to determine the rationale behind the decision-making processes of LLMs. Research in this area is underway but relying on empirical methods remains the answer until then.
Lack of Diversity:
Another challenge is that most datasets used for evaluation do not represent all cultures and groups. While they might work for certain benchmarks, they might not work well in real-world situations.
Scalability:
With LLMs growing larger and more complex, traditional evaluation methods will have to evolve rapidly to keep up with the pace, scale, and performance.
Reference Data:
Getting access to high-quality reference data can be challenging. This is especially so in situations where more than one right answer is applicable.
Attacks:
LLMs are susceptible to threats like data poisoning which can lead the evaluation methods astray. Robust checks and balances should proactively identify and eliminate these dangers.
Real-World Scenarios:
Most evaluation methods focus on specific datasets or tasks under controlled conditions. Real-world scenarios are more diverse and dynamic, and evaluation approaches must factor in the unpredictability.
Subjective Evaluation by Humans:
Human-in-the-loop to evaluate the efficacy of LLM outputs is a great, transparent method. However, since humans are prone to biases, the evaluations might not live up to the mark. They might also be costlier and take longer.
LLM Evaluation Best Practices:
To ensure that you are accurately evaluating the capabilities of LLMs, you must have a strategy in place that tackles your challenges and helps you deliver the analytics and business impact you desire. The following best practices will help you build this strategy.
Define Clear Objectives:
Before starting the LLM evaluation process, ensure that you have clear goals in mind. Consider the requirements of those who are the intended users of the LLM.
Use Diverse Reference Data:
For evaluating LLM outputs, diverse and curated datasets must be used. Ensure they cover a wide array of acceptable responses and various contexts for better quality.
Leverage LLMOps:
LLMOps will effectively streamline processes for evaluation testing.
Use Multiple Metrics, Frameworks, and Benchmarks:
For a comprehensive assessment of LLM performance, don’t rely on a single approach. Use a bouquet of several approaches that best measure the different vulnerabilities of your use cases to accurately determine the quality of the LLM.
Improve Human-in-the-loop Evaluation:
There should be clear criteria and guidelines for an objective human evaluation. There should be multiple human judges and they should compare evaluations to increase efficacy and reliability. For larger-scale assessments, you can also crowd-source evaluators for diverse perspectives.
Evaluation through Real Cases:
To evaluate the performance of LLMs, consider testing them with real-world use cases that are complex. For a better assessment of the LLM’s performance, evaluate using datasets that are specific to your scenarios.
Version Control:
Ensure there is rigorous version control and active documentation of LLMs for selecting the most efficient LLM models. Track changes over time to see the difference in its performance.
Conclusion:
Robust evaluation of LLMs will help improve its ubiquity and effectiveness in advancing AI-powered transformation. There is another reason to evaluate large language models; it will help us understand the complete risk implications. Hence, the focus must be on practicing the improvement of evaluation techniques so that they can perform well in real-world scenarios. This will help in the creation of an AI ecosystem that makes the world a better place to live in.
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FAQs:
How do you evaluate LLMs?
Evaluating an LLM involves determining its performance across multiple dimensions to ensure that it meets the requirements for accuracy, safety and efficiency. There are specific metrics like BLEU, ROUGE, F1 Score, and Perplexity that can be used to evaluate the effectiveness of the LLM’s output.
What are the evaluation tasks for LLM?
LLMs are evaluated on a variety of tasks that include reasoning, summarization, language generation translation, hallucinations, relevance, toxicity, and question-answer accuracy. Such evaluations help build robust and secure models across several dimensions.
Why is it hard to evaluate LLMs?
Data contamination, scalability issues, human biases, explainability, reproducibility, etc., are some of the main reasons why it is hard to evaluate LLMs.
How can we assess the reliability of LLMs?
The metric MONITOR is used to measure the factual reliability of LLMs. It assesses the distance between probability distributions of valid outputs under different prompts and contexts.
What is LLM-as-a-judge?
Here, an LLM evaluates the output of other LLM-powered applications. LLM-as-a-judge rates text based on criteria defined by the user of any LLM-powered product such as agents, chatbots, or Q&A systems.
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