With the rapid (and massive) adoption of generative AI in various consumer applications, the evaluation of large-scale language models (LLM) has become a central issue in the field of artificial intelligence (AI). These models, capable of generating, understanding and transforming text with an unprecedented degree of sophistication, are based on complex algorithms whose performance must be measured and adjusted according to the objectives being pursued.
Yet evaluating a language model is more than just checking its ability to produce coherent answers. It's a rigorous process involving multiple criteria, from accuracy and robustness to ethics and fairness. Understanding these different parameters is essential to ensure that LLMs meet the requirements of the users and industries that adopt them.
💡 In this article, we'll take a look at current practices for evaluating AI and, in particular, large language models. Keep in mind that this is an ever-evolving field - this article does not claim to be exhaustive. So don't hesitate to submit your ideas or tools for evaluating LLMs!
What is a large-scale language model (LLM)?
A large-scale language model (LLM) is a type of artificial intelligence based on neural networks. neural networks designed to understand, generate and manipulate text on a large scale. These models, trained on billions of textual data, are capable of capturing complex linguistic nuances and producing coherent responses in a variety of contexts, including translation from one language to another.
Thanks to their size and the quantity of parameters they contain, LLMs can perform natural language processing (NLP) tasks such as machine translation, text generation, question answering and evensentiment analysis.
LLMs are distinguished by their ability to "learn" relationships between words, phrases and concepts based on the vast amount of data they are trained on.
This enables them to adopt adaptive behavior, improve their performance as they are exposed to more data, and deliver relevant results in specific domains, without requiring additional training on those domains. Notable examples of LLMs include OpenAI's GPT (Generative Pre-trained Transformer), Google's BERT (Bidirectional Encoder Representations from Transformers) and even Claude from Anthropic.
🤔 You may be wondering what challenges AI poses in terms of bias, energy consumption, and fine-grained understanding of cultural and ethical contexts ? These are recurring themes when we talk about LLMs. Read on: we tell you more about the importance of evaluating language models.
Why is it essential to evaluate the performance of language models?
Evaluating the performance of language models (LLMs) is essential for a number of reasons, both technical and ethical. Here are just a few of them:
Ensuring the reliability of LLM-based applications
Language models are used in many sensitive applications such as virtual assistants, translation systems and content production. It is therefore essential to evaluate their accuracy, consistency and ability to understand and generate text in different contexts. This evaluation ensures that models meet user expectations in terms of quality and reliability.
Identify and correct biases
Large-scale language models are trained on immense quantities of data from the Internet, which can introduce biases (don't think that everything said on Reddit is true... 😁). LLM evaluation makes it possible to detect these biases and implement corrections to avoid the reproduction of stereotypes or prejudices. This is a very important point for creating more ethical and fair models.
Optimizing performance and robustness
Ongoing evaluation of LLMs is necessary to test their ability to adapt to varied situations, maintain stable performance on different tasks, and react to unexpected inputs . This optimization not only enhances the efficiency of the models, but also enables new models to be compared with old ones, guaranteeing continuous improvement.
What are the main criteria for evaluating an LLM?
The main criteria for evaluating a large-scale language model (LLM) are varied and depend on the specific objectives of the model or use case. From a technical and business point of view, here are some of the most important criteria:
Precision and consistency
Accuracy refers to the LLM's ability to provide correct answers that are relevant to the question asked or the task assigned. Consistency, on the other hand, concerns the model's ability to produce logical and coherent responses over a long series of interactions, without contradicting itself.
Contextual understanding
A good LLM must be able to grasp the context in which a question or order is posed. This includes understanding word relationships, linguistic nuances, and cultural or domain-specific elements.
Robustness and resilience to bias
A robust LLM must be able to function correctly even when confronted with unusual, ambiguous or incorrect inputs. Resilience to bias is also critical, as language models can reproduce and amplify biases present in their training data. Robustness assessment therefore includes the ability to identify and limit these biases.
Text generation performance
Text generation quality is a key criterion, especially for applications where models have to produce content, such as chatbots or authoring tools. Evaluations focus on the fluency, grammar and relevance of the responses generated.
Scalability and computational performance
An often underestimated criterion is the ability of an LLM to operate effectively on a large scale, i.e. with millions of users or on resource-limited systems. Scalability measures the model's performance in relation to the usage and infrastructure required to run it.
Ethics and fairness
A language model must also be assessed for its ethical impact. This includes how it handles sensitive information, how it deals with ethical issues and how it avoids promoting inappropriate or discriminatory content.
Responsiveness and adaptability
Responsiveness refers to the model's ability to provide quick answers, while adaptability measures its ability to learn new concepts, domains or situations. This can include adapting to new data sets or unexpected questions without compromising the quality of answers.
🪄 Using these criteria, it's possible to thoroughly assess the quality, reliability andeffectiveness of LLMs in different contexts!
How do you measure the accuracy of a language model?
Measuring the accuracy of a language model (LLM) is a complex process involving several techniques and tools. Here are the main methods for assessing accuracy:
Use of standard performance metrics
Several metrics are commonly used to assess the accuracy of language models:
- Accuracy: This measure evaluates the percentage of correct answers provided by the model on a set of test data. It is useful for tasks such as text classification or answers to closed questions.
- Perplexity: This is a metric often used for language models. It measures the probability a model assigns to word sequences. The lower the perplexity, the more accurate and confident the model is in its predictions.
- BLEU score (Bilingual Evaluation Understudy): Evaluates the similarity between a text generated by the model and a reference text. Often used in tasks such as machine translation, it measures the accuracy of generated sentences by comparing n-grams (groups of words) with the expected text.
- ROUGE Score (Recall-Oriented Understudy for Gisting Evaluation): Used to evaluate automatic summarization tasks, it compares generated text segments with human summaries, measuring surface similarities between words and phrases.
Testing on public benchmarks
Numerous standardized benchmarks exist for testing the accuracy of LLMs on specific natural language processing (NLP) tasks. Here are some of the best-known. These benchmarks provide a basis for comparison between different language models:
- GLUE (General Language Understanding Evaluation): A set of benchmarks assessing skills such as reading comprehension, classification and sentence matching.
- SuperGLUE A more challenging version of GLUE, designed to evaluate state-of-the-art models on more complex comprehension tasks.
- SQuAD (Stanford Question Answering Dataset) A benchmark used to evaluate the accuracy of models on context-based question-answering tasks.
Human evaluation
In some cases, automatic metrics are not enough to capture all the subtlety of a text generated by an LLM. Human evaluation remains a complementary and often indispensable method, particularly for :
- Assess the quality of the generated text (fluidity, coherence, relevance).
- Assess the model's understanding of the context.
- Identify biases or contextual errors that automated tools may not detect.
The human annotators can assess whether the model produces convincing and accurate results in a real environment. It's a job that demands rigor, precision and patience, enabling the production of reference datasets.
Comparison with reference responses (or"gold standard" responses)
For tasks such as answering questions or making summaries, the results generated by the model are compared with reference answers. This makes it possible to directly measure the accuracy of the answers provided in relation to those expected, taking into account nuances and fidelity to the original content.
Evaluation on real cases
Finally, to measure accuracy in a more pragmatic way, models are often tested in real environments or on concrete use cases. This enables us to check how the LLM behaves in practical situations, where data may be more varied or unexpected.
What tools and techniques are used to assess LLMs?
The evaluation of large-scale language models (LLMs) relies on a set of tools and techniques to measure different aspects of their performance. Here are some of the most commonly used tools and techniques:
Benchmarking tools
Benchmarking platforms enable LLMs to be tested and compared on specific natural language processing (NLP) tasks. Among the most popular tools are :
Hugging Face
This platform provides tools for evaluating language models, including benchmark datasets and specific tasks. Hugging Face also provides APIs and libraries for testing LLMs on benchmarks such as GLUE, SuperGLUE, and SQuAD.
OpenAI Evaluation Suite
Used to evaluate GPT models, this suite of tools can be used to test LLM capabilities on a variety of tasks such as text generation, language comprehension and question answering.
SuperGLUE and GLUE
These benchmarks are widely used to assess the language comprehension skills of LLMs. They measure performance on tasks such as text classification, paraphrasing and inconsistency detection.
EleutherAI's Language Model Evaluation Harness
This tool is designed to test language models on a wide range of tasks and datasets. It is used to evaluate text generation, sentence completion and other linguistic capabilities.
AI Verify
AI Verify is a testing and validation tool for artificial intelligence systems, developed by Singapore's Infocomm Media Development Authority (IMDA). Launched in 2022, it aims to help companies assess and demonstrate the reliability, ethics and regulatory compliance of their AI models. AI Verify enables aspects such as robustness, fairness, explainability and privacy to be tested, providing a standardized framework to ensure that AI systems operate responsibly and transparently.
Tools for measuring perplexity and similarity scores
Metrics such as perplexity or similarity scores, such as BLUE and RED, are used to assess the quality of the predictions generated by the models.
- Perplexity Calculators: Tools to measure a model's perplexity, i.e. its ability to predict word sequences. Perplexity measures the model's confidence in its prediction, with lower perplexity indicating better performance.
- BLEU (Bilingual Evaluation Understudy): A tool used mainly to evaluate machine translations, it measures the similarity between the text generated by the model and a reference text by comparing word groups (n-grams).
- ROUGE (Recall-Oriented Understudy for Gisting Evaluation): Used to evaluatesummarization tasks, ROUGE compares the similarity between the generated text and the expected summary in terms of sentence overlap.
Data annotation and human evaluation
Data annotation plays a central role in the evaluation of language models, particularly for subjective tasks such as text generation. Platforms such as SuperAnnotate and Labelbox enable annotators to label and evaluate LLM-generated responses according to defined criteria, such as relevance, clarity and consistency.
In addition to automated metrics, human annotators also evaluate the quality of responses, detect bias and measure the suitability of models for specific tasks!
Automatic assessment of bias andfairness
LLMs can be subject to bias, and several tools are used to identify and evaluate these biases:
- Fairness Indicators: These indicators, available in frameworks such as TensorFlow or Fairlearn, can be used to assess whether the language model is biased towards sensitive criteria such as gender, race or ethnic origin.
- Bias Benchmarking Tools: Libraries like CheckList can be used to test language models for biases, by simulating real-life situations where biases can occur.
Error analysis tools
Error analysis helps diagnose model weaknesses. Tools such as Error Analysis Toolkit and Errudite help to understand why a model fails on certain tasks, by exploring errors by category or data type. This makes it possible to target model improvements.
Real-life testing
Some LLMs are evaluated directly in real environments, such as customer applications, virtual assistants or chatbots. This tests their ability to handle authentic human interactions. Tools like DialogRPT are often used to assess the quality of responses in these contexts, measuring criteria such as relevance and engagement.
Conclusion
The evaluation of large-scale language models (LLMs) is an essential process for ensuring their effectiveness, robustness and ethics. As these models play an increasingly important role in a variety of applications, sophisticated tools and techniques are needed to measure their performance.
Whether through metrics such as perplexity, benchmarks such as GLUE, or human assessments to judge the quality of responses, each approach sheds additional light on the strengths and weaknesses of LLMs.
At Innovatianawe believe that it is necessary to remain attentive to potential biases, and by constantly improving models through ongoing evaluation, it becomes possible to create more efficient, reliable and ethically responsible language systems, capable of meeting the needs of users in a variety of contexts. It's also important to master the AI supply chain, starting with datasets: as such, the Governor of California recently signed three bills related to artificial intelligence. One of the requirements is for companies to disclose the data used to develop their AI models...
