LLM Assessment in AI: Why and how to assess the performance of language models?
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, rely on complex algorithms whose performance must be measured and adjusted according to the desired objectives.
However, Evaluate a language model is not limited to verifying its ability to produce consistent responses. It is a rigorous process that involves multiple criteria, ranging from precision to robustness, ethics and fairness. Understanding these various parameters is critical to ensuring that LLMs meet the requirements of users and the industries that adopt them.
💡 In this article, we will be doing a Overview of common practices for evaluating AIs and in particular the major language models. Keep in mind that this is a constantly evolving field - this article does not claim to be comprehensive. Also, do not hesitate to contact us submit your ideas or tools to assess the LLM!
What is a large-scale language model (LLM)?
A large-scale language model (LLM) is a type of artificial intelligence based on neural networks deep, designed to understand, generate, and manipulate text at scale. These models, trained on billions of textual records, are capable of capturing complex linguistic nuances and producing consistent 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 tasks of natural language processing (NLP) such as machine translation, text generation, answering questions, or sentiment analysis.
LLMs stand out for their ability to “learn” relationships between words, sentences, and concepts based on the vast amount of data they are trained on.
This allows them to adopt adaptive behavior, improve their performance as they are exposed to more data, and provide relevant results in specific areas, without requiring additional training in those areas. Notable examples of LLM include GPT (Generative Pre-trained Transformer) from OpenAI, BERT (Bidirectional Encoder Representations from Transformers) from Google or Claude from Anthropic.
🤔 You might be wondering what are the challenges posed by AI in terms of bias, energy consumption, and detailed understanding of cultural and ethical contexts ? These are recurring themes when talking about LLMs. Read on: we tell you more about the importance of evaluating language models.
Why is it essential to assess the performance of language models?
Assessing the performance of language models (LLM) is essential for several reasons, both technical and ethical. Here are 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 assess their accuracy, consistency, and ability to understand and generate text in different contexts. This assessment ensures that the models meet user expectations for quality and reliability.
Identify and correct biases
Large-scale language models are formed on huge amounts of data from the Internet, which can introduce biases (don't think everything that's said on Reddit is true... 😁). The assessment of LLMs makes it possible to detect these biases and to implement corrections to avoid the reproduction of stereotypes or prejudices. This is a very important point in creating more ethical and equitable models.
Optimizing performance and robustness
The continuous evaluation of LLMs is necessary to test their ability to adapt to varied situations, to maintain a stable performance on different tasks, and to react to Inputs unexpected. This optimization not only makes it possible to improve the efficiency of the models, but also to compare the new models with the old ones and to ensure 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 the use case. From a technical and commercial point of view, here are some of the most important criteria:
Precision and consistency
Accuracy refers to the ability of the LLM to provide answers that are correct and relevant to the question asked or the task assigned. Coherence, on the other hand, concerns the ability of the model to produce logical and coherent responses over a long series of interactions, without contradicting each other.
Contextual understanding
A good LLM should be able to grasp the context in which a question or order is asked. This includes understanding relationships between words, linguistic nuances, and cultural or domain-specific elements.
Robustness and resilience to biases
A robust LLM should be able to function properly even when confronted with unusual, ambiguous, or incorrect entries. Resilience to bias is also critical, as language models can replicate and amplify the biases present in their training data. The assessment of robustness therefore includes the ability to identify and limit these biases.
Text generation performance
The quality of text generation is a key criterion, especially for applications where models need to produce content, such as chatbots or writing tools. The 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 function effectively on a large scale, i.e. with millions of users or on systems that are limited in resources. Scalability measures the performance of the model based on the use and infrastructure required to make it work.
Ethics and fairness
A language model must also be evaluated on its ethical impact. This includes how it handles sensitive information, its behavior when dealing with ethical issues, and its ability to not promote inappropriate or discriminatory content.
Responsiveness and adaptability
Responsiveness refers to the model's ability to provide quick responses, while adaptability measures its ability to learn new concepts, domains, or situations. This may include adapting to new data sets or unexpected questions without compromising the quality of the answers.
Using these criteria, it is possible to thoroughly assess the quality, the trustworthiness and the efficiency 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 that involves several techniques and tools. Here are the main methods for evaluating this accuracy:
Using standard performance metrics
Several metrics are commonly used to assess the accuracy of language models:
- Accuracy : This measure assesses the percentage of correct answers provided by the model on a set of test data. It's useful for tasks like classifying text or answering closed-ended questions.
- Perplexity : This is a metric that is often used for language models. It measures how likely a model is to attribute to word sequences. The lower the perplexity, the more accurate and confident the model is in its predictions.
- Score BLUE (Bilingual Evaluation Understudy) : It assesses the similarity between a text generated by the model and a reference text. Often used in tasks like machine translation, it measures the accuracy of sentences generated by comparing n-grams (groups of words) with the expected text.
- RED Score (Recall-Oriented Understudy for Gisting Evaluation) : Used to assess automatic summarization tasks, it compares segments of generated text to human summaries, by measuring the surface similarities between words and sentences.
Test on Benchmarks publics
Numerous standardized benchmarks exist to test the accuracy of LLMs on specific natural language processing (NLP) tasks. Among the best known. These benchmarks provide a basis for comparison between the various language models:
- GLUE (General Language Understanding Evaluation) : A set of benchmarks evaluating skills such as text comprehension, classification, and sentence matching.
- SuperGlue: A more challenging version of GLUE, designed to assess advanced models on more complex comprehension tasks.
- SQuaD (Stanford Question Answering Dataset): A benchmark used to assess the accuracy of models on question-and-answer tasks based on a given context.
Human evaluation
In some cases, automatic metrics are not enough to capture all the subtlety of text generated by an LLM. Human evaluation remains a complementary and often indispensable method, in particular for:
- Judging the text quality generated (fluidity, coherence, relevance).
- Evaluate the understanding the context by the model.
- Identify biases Or contextual errors that automated tools might not detect.
Human annotators can thus assess whether the model produces convincing and accurate results in a real environment. It is a job that requires rigor, precision and patience, making it possible to produce reference datasets.
Comparison with reference responses (or “answers”)Gold Standard“)
For tasks such as answering questions or summaries, the results generated by the model are compared to the baseline responses. This makes it possible to directly measure the accuracy of the answers provided according to those expected, taking into account nuances and fidelity to the original content.
Assessment 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 makes it possible to check how the LLM behaves in practical situations, where the data may be more varied or unexpected.
What tools and techniques are used for the assessment of LLMs?
Evaluation of large-scale language models (LLM) is based on a set of tools and techniques that allow different aspects of their performance to be measured. Here are some of the most commonly used tools and techniques:
Tools of benchmarking
Benchmarking platforms allow LLMs to be tested and compared on specific natural language processing (NLP) tasks. Among the most popular tools are:
Hugging Face
This platform offers tools for evaluating language models, in particular through reference data sets and specific tasks. Hugging Face also provides APIs and libraries for testing LLMs on Benchmarks like GLUE, SuperGlue, and sQuAD.
OpenAI Evaluation Suite
Used to assess GPT models, this suite of tools makes it possible to test the abilities of LLMs on a variety of tasks such as text generation, language comprehension, and question answers.
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 detecting inconsistencies.
Eleutherai's Language Model Evaluation Harness
This tool is designed to test language models across a wide range of tasks and datasets. It is used to assess text generation, sentence completion, and other linguistic abilities.
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 businesses assess and demonstrate the reliability, ethics, and regulatory compliance of their AI models. AI Verify makes it possible to test aspects such as robustness, fairness, fairness, explainability, and privacy, by providing a standardized framework to ensure that AI systems operate in a responsible and transparent manner.
Tools for measuring perplexity and similarity scores
Metrics like 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 make it possible to measure the perplexity of a model, i.e. its ability to predict word sequences. Perplexity measures the confidence of the model in its prediction, with lower perplexity indicating better performance.
- BLUE (Bilingual Evaluation Understudy) : A tool used primarily to evaluate machine translations, it measures the similarity between the text generated by the model and a reference text by comparing groups of words (n-grams).
- RED (Recall-Oriented Understudy for Gisting Evaluation) : Used to assess summary tasks (”Summarization“), RED 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 evaluating language models, especially for subjective tasks like text generation. Platforms like SuperAnnotate and Labelbox allow annotators to label and assess responses generated by LLMs according to defined criteria, such as relevance, clarity, and consistency.
In addition to automated metrics, human annotators also assess the quality of responses, detect biases, and measure the suitability of models for specific tasks!
Automatic evaluation of biases and”Fairness“
LLMs can be subject to bias, and several tools are used to identify and assess these biases:
- Fairness Indicators : These indicators, available in frameworks such as TensorFlow or Fairlearn, make it possible to assess whether the language model has biases on sensitive criteria such as gender, race, or ethnic origin.
- Bias Benchmarking Tools : Libraries like CheckList allow language models to be tested on their biases, by simulating real situations where biases can occur.
Error analysis tools
Error analysis makes it possible to diagnose the weaknesses of a model. Tools like Error Analysis Toolkit and Errudite help 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-world testing
Some LLMs are evaluated directly in real environments, such as client applications, virtual assistants, or chatbots. This allows them to test their ability to manage authentic human interactions. Tools like DialoGrpt are often used to assess the quality of responses in these contexts, by measuring criteria such as relevance and engagement.
Conclusion
Assessing large-scale language models (LLMs) is an essential process to ensure 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 like perplexity, Benchmarks such as GLUE, or human evaluations to judge the quality of responses, each approach provides additional insight into the strengths and weaknesses of LLMs.
At Innovatiana, we believe that it is necessary to remain alert to potential biases and by constantly improving models through continuous evaluations, it becomes possible to create language systems that are more efficient, reliable and ethically responsible, capable of meeting the needs of users in various contexts. It is 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. Among the requirements, we find the obligation for companies to disclose the data used to develop their AI models...
