Large Language Model evaluation (i.e. LLM eval) is the multidimensional assessment of large language models (LLMs). Effective evaluation is crucial for selecting and optimizing LLMs.
Enterprises have a range of base models and their variations to choose from, but achieving success is uncertain without precise performance measurement. To ensure the best results, it is vital to identify the most suitable evaluation methods as well as the appropriate data for training and assessment.
See evaluation metrics and methods, how to address challenges with current evaluation models, and solutions to mitigate them.
For quick definitions and references, check out the glossary of key terms.
See the best datasets and metrics for your specific aims:
The best benchmark to use the LLM to complete the real-life task it will face in production. However, due to challenges like data confidentiality, you may not have access to a large set of tasks. Then, it is best to rely on benchmarks.
A combination of benchmarks is often necessary to comprehensively evaluate a language model’s performance. A set of benchmark tasks is selected to cover a wide range of language-related challenges.
These tasks may include language modeling, text completion, sentiment analysis, question answering, summarization, machine translation, and more. LLM benchmarks should represent real-world scenarios and cover diverse domains and linguistic complexities. We have an LLM leaderboard with the latest results for both open source and proprietary LLMs.
Sticking to the same benchmarking methods and datasets can lead to overfitting. We advise updating your benchmark and evaluation metrics to have generalizable results. Some of the most popular benchmarking datasets are:
Key research takeaways also include the need for better benchmarking, collaboration, and innovation to push the boundaries of LLM capabilities.
Using either custom-made or open-source datasets is acceptable. The key point is that the dataset should be recent enough so that the LLMs have not yet been trained on it.
Curated datasets, including training, validation, and test sets, are prepared for each benchmark task. These datasets should be large enough to capture variations in language use, domain-specific nuances, and potential biases. Careful data curation is essential to ensure high-quality and unbiased evaluation.
Models trained as large language models (LLMs) undergo fine-tuning to improve task-specific performance. The process typically begins with pre-training on large text sources like Wikipedia or the Common Crawl, allowing the model to learn language patterns and structures, forming the base for generative AI coding and generating human-like text.
After pre-training, LLMs are fine-tuned on specific benchmark datasets to enhance performance in tasks like translation or summarization. These models vary in size, from small to large, and use transformer-based designs. Alternative training methods are often employed to boost their capabilities.
The trained or fine-tuned LLM models are evaluated on the benchmark tasks using the predefined evaluation metrics. The models’ performance is measured based on their ability to generate accurate, coherent, and contextually appropriate responses for each task. The evaluation results provide insights into the LLM models’ strengths, weaknesses, and relative performance.
The evaluation results are analyzed to compare the performance of different LLM models on each benchmark task. Models are ranked based on their overall performance or task-specific metrics. Comparative analysis allows researchers and practitioners to identify state-of-the-art models, track progress over time, and understand the relative strengths of different models for specific tasks.
Figure 1: Top 10 ranking of different Large Language Models based on their performance metrics.10
Choosing a benchmarking method and evaluation metrics to define the overall evaluation criteria based on the model’s intended use are almost simultaneous tasks. Numerous metrics are used for evaluation.
These particular quantitative or qualitative measurement methods evaluate certain facets of LLM performance. With differing degrees of connection to human assessments, they offer numerical or categorical scores that may be monitored over time and compared between models.
Agents are likely to become the most common LLM use cases. Therefore, evaluating LLMs while they are driving agents is becoming more important:
Success Rate for end-to-end tasks (e.g. identify all growth professionals in companies that fit our ICP)
Tool-Use Accuracy: How often the model calls the correct API with the correct parameters.
Agent Safety: How often the agent undertook harmful actions like deleting a file while trying to solve a task.
Figure 2: Examples of perplexity evaluation.
Figure 3: An example of a ROUGE evaluation process.11
Evaluation metrics can be judged by a model or a human. Both have their own advantages and use cases:
The LLM assesses the caliber of its own products in an examination known as LLM-as-a-judge. This could involve comparing model-generated text to ground-truth data or measuring outcomes with statistical metrics like accuracy and F1.
LLM-as-a-judge provides businesses with high efficiency by quickly assessing millions of outputs at a fraction of the expense of human review. It is suitable for large-scale deployments where speed and resource optimization are crucial success factors because it is adequate at evaluating technical content in situations where qualified reviewers are hard to come by, allows for continuous quality monitoring of AI systems, and produces repeatable results that hold true throughout evaluation cycles.
The evaluation process includes enlisting human evaluators who assess the language model’s output quality. These evaluators rate the generated responses based on different criteria: relevance, fluency, coherence, and overall quality. This approach offers subjective feedback on the model’s performance.
Human evaluation is still crucial for high-stakes enterprise applications where mistakes could cause serious harm to the company’s operations or reputation. Human reviewers are excellent at identifying subtle problems with cultural context, ethical implications, and practical usefulness that automated systems frequently overlook. They also meet regulatory requirements for human oversight in sensitive industries such as healthcare, finance, and legal services.
LLM evaluation can be performed in two ways: you can conduct it yourself using either open-source or commercial frameworks or pre-calculated values from benchmarks or results from open-source frameworks of the base models.
Comprehensive evaluation frameworks are integrated systems that provide a variety of metrics and evaluation techniques in a unified testing environment. They usually offer defined benchmarks, test suites, and reporting systems to evaluate LLMs across a range of capabilities and dimensions.
Testing approaches are methodological techniques for organizing and carrying out assessments that are not dependent on particular metrics or instruments. They specify experimental designs, sample techniques, and testing philosophies that can be applied with different frameworks.
Commercial evaluation platforms are vendor-provided solutions with compliance features, MLOps pipeline integration, and user-friendly interfaces that are intended for enterprise use cases. They frequently have monitoring capabilities and strike a compromise between technical depth and non-technical stakeholders’ accessibility.
Pre-evaluated benchmarks provide valuable insights using specific metrics, making them particularly useful for metric-driven analysis. Our website features benchmarks for leading models, helping you assess performance effectively. Key benchmarks include:
Additionally, the OpenLLM Leaderboard offers a live benchmarking system that evaluates models on publicly available datasets. It aggregates scores from tasks such as machine translation, summarization, and question-answering, providing a dynamic and up-to-date comparison of model performance.
Consider an enterprise that needs to choose between multiple models for its base enterprise generative model. These LLMs must be evaluated to assess how well they generate text and respond to input. Performance assessment metrics can include accuracy, fluency, coherence, and subject relevance.
With the advent of large multimodal models, enterprises can also evaluate models that process and generate multiple data types, such as images, text, and audio, expanding the scope and capabilities of generative AI.
An enterprise may have fine-tuned a model for higher performance in tasks specific to its industry. An evaluation framework helps researchers and practitioners compare LLMs and measure progress, helping them select the most appropriate model for a given application. LLM evaluation’s ability to pinpoint areas for development and opportunities to address deficiencies might result in a better user experience, fewer risks, and even a possible competitive advantage.
LLMs can have biases in their training data, which may lead to the spread of misinformation, representing one of the risks associated with generative AI. A comprehensive evaluation framework helps identify and measure biases in LLM outputs, allowing researchers to develop strategies for bias detection and mitigation.
Evaluation of user satisfaction and trust is crucial to test generative language models. Relevance, coherence, and diversity are evaluated to ensure that models match user expectations and inspire trust. This assessment framework aids in understanding the level of user satisfaction and trust in the responses generated by the models.
LLM evaluation can be used to assess the quality of answers generated by retrieval-augmented generation (RAG) systems. Various datasets can be utilized to verify the accuracy of the answers.
While existing evaluation methods for Large Language Models (LLMs) provide valuable insights, they are imperfect. The common issues associated with them are:
Scale AI found that some LLMs are overfitting on popular AI benchmarks. They created GSM1k, a smaller version of the GSM8k benchmark for math testing. LLMs performed worse on GSM1k than on GSM8k, indicating a lack of genuine understanding. These findings suggest that current AI evaluation methods may be misleading due to overfitting, underscoring the need for additional testing methods, such as GSM1k.
The evaluation techniques used for LLMs today frequently do not capture the whole range of output diversity and innovation. The crucial significance of producing diverse and creative replies is sometimes overlooked by traditional metrics emphasizing accuracy and relevance. Research on the problem of assessing diversity in LLM results is still ongoing. Although perplexity gauges a model’s ability to anticipate text, it ignores crucial elements like coherence, contextual awareness, and relevance. Therefore, depending only on ambiguity could not offer a thorough evaluation of an LLM’s actual quality.
Human evaluation is a valuable method for assessing the outputs of large language models (LLMs). However, it can be subjective, biased, and significantly more expensive than automated evaluations. Different human evaluators may have varying opinions, and the criteria for evaluation may lack consistency. Furthermore, human evaluation can be time-consuming and costly, especially for large-scale assessments. Evaluators often disagree when assessing subjective aspects, such as helpfulness or creativity, making it challenging to establish a reliable ground truth for evaluation.
LLM evaluations suffer from predictable biases. We provided one example for each bias, but the opposite cases are also possible (e.g., some models can favor last items).
Some evaluation methods, such as BLEU or ROUGE, require reference data for comparison. However, obtaining high-quality reference data can be challenging, especially when multiple acceptable responses exist or in open-ended tasks. Limited or biased reference data may not capture the full range of acceptable model outputs.
Evaluation methods typically focus on specific benchmark datasets or tasks that don’t fully reflect the challenges of real-world applications. The evaluation of controlled datasets may not generalize well to diverse and dynamic contexts where LLMs are deployed.
LLMs can be susceptible to adversarial attacks, such as manipulating model predictions and data poisoning, where carefully crafted input can mislead or deceive the model. Existing evaluation methods often do not account for such attacks, and robustness evaluation remains an active area of research.
In addition to these issues, enterprise generative AI models may struggle with legal and ethical issues, which may affect LLMs in your business.
Large Language Models (LLMs) must be evaluated on various dimensions, such as factual accuracy, toxicity, and bias. This often involves trade-offs, making it challenging to develop unified scoring systems. A thorough evaluation of these models across multiple dimensions and datasets demands substantial computational resources, which can limit access for smaller organizations.
Researchers and practitioners are exploring various approaches and strategies to address the problems with large language models’ performance evaluation methods. It may be prohibitively expensive to leverage all of these approaches in every project, but awareness of these best practices can improve LLM project success.
Leverage foundation models that share their training data to prevent contamination.
Instead of relying solely on perplexity, incorporate multiple evaluation metrics for a more comprehensive assessment of LLM performance. Metrics like these can better capture the different aspects of model quality:
Clear guidelines and standardized criteria can improve the consistency and objectivity of human evaluation. Using multiple human judges and conducting inter-rater reliability checks can help reduce subjectivity. Additionally, crowd-sourcing evaluation can provide diverse perspectives and larger-scale assessments.
Create diverse and representative reference data to better evaluate LLM outputs. Curating datasets that cover a wide range of acceptable responses, encouraging contributions from diverse sources, and considering various contexts can enhance the quality and coverage of reference data.
Encourage the generation of diverse responses and evaluate the uniqueness of generated text through methods such as n-gram diversity or semantic similarity measurements.
Augmenting evaluation methods with real-world scenarios and tasks can improve the generalization of LLM performance. Employing domain-specific or industry-specific evaluation datasets can provide a more realistic assessment of model capabilities.
Evaluating LLMs for robustness against adversarial attacks is an ongoing research area. Developing evaluation methods that test the model’s resilience to various adversarial inputs and scenarios can enhance the security and reliability of LLMs.
LLMOps, a specialized branch of MLOps, is dedicated to developing and enhancing LLMs. Employing for testing and customizing LLMs in your business not only saves time but also minimizes errors.
Several organizations have shared their practical experiences with LLM evaluation:
While performance metrics and benchmarking are crucial, enterprises must also consider the ethical implications of LLM evaluation. These include:
By incorporating ethical considerations into evaluation frameworks, organizations can mitigate reputational risks, ensure compliance, and foster trust with users.
Research in LLM evaluation is evolving rapidly. Some notable trends include:
Trust is eroding in evaluations that are no longer capable of accurately evaluating model performance:
My reaction is that there is an evaluation crisis. I don't really know what metrics to look at right now.
MMLU was a good and useful for a few years but that's long over.
SWE-Bench Verified (real, practical, verified problems) I really like and is great but itself too narrow.…
For readers new to the space, here’s a quick reference to essential evaluation metrics:
Evaluating large language models is crucial throughout their entire lifecycle, encompassing selection, fine-tuning, and secure, dependable deployment. As the capabilities of LLMs increase, it becomes inadequate to depend solely on a single metric (like perplexity) or benchmark. Thus, a multidimensional strategy that integrates automated scores (e.g., BLEU/ROUGE, checks for factual consistency), structured human evaluations (with specific guidelines and inter-rater agreement), and custom tests for bias, fairness, and toxicity is vital to assess both quantitative performance and qualitative risks.
Yet significant challenges remain. Public benchmarks can lead to overfitting on well-trodden datasets, while human-in-the-loop evaluations are time-consuming and complicated to scale. Adversarial inputs expose robustness gaps, and energy-intensive models raise sustainability concerns. Addressing these requires curating diverse, domain-specific test suites; integrating red-team and adversarial stress-testing; deploying LLM-as-judge pipelines for rapid, cost-effective assessment; and tracking energy and inference costs alongside accuracy metrics.
By embedding these best practices within an LLMOps framework, organizations can maintain a robust, ongoing view of model behavior in production. This holistic evaluation strategy mitigates risks like bias, hallucination, and security vulnerabilities and ensures that LLMs deliver trustworthy, high-impact outcomes as they evolve.
Organizations usually employ a mix of predetermined evaluation metrics covering a wide range of competencies when assessing LLMs. Quantitative evaluation of model performance is provided by automated measurements such as accuracy on standardized benchmarks (e.g., Massive Multitask Language Understanding, Stanford Question Answering Dataset). Complete assessment frameworks also include human evaluation to evaluate qualitative factors like usefulness and ethical considerations. The most reliable approach integrates human judgment with automated metrics, assessing context-specific evaluation situations, retrieval augmented generation, and the model’s capacity to adhere to prompt templates while also being in line with ground truth.
In the LLM assessment process, evaluation datasets have a fundamentally different function than training data. Evaluation datasets assess the model’s overall comprehension and generalization abilities, whereas training data instructs the model. A wide variety of use cases, including both typical situations and edge circumstances that could put the model architecture to the test, should be represented in effective assessment datasets. Evaluation datasets, in contrast to training data, need to be carefully selected to prevent contamination (overlap with training data) and should contain a variety of instances that assess the model on a number of different aspects, such as logic, factuality, and moral behavior. The primary distinction is that evaluation datasets offer impartial standards by which various LLMs can be methodically contrasted.
The most thorough assessment of LLM’s performance is obtained by a combination of offline testing (controlled experiments) and online evaluation (real-time assessment with actual users). Online testing exposes problems that might not appear in controlled settings by showing how the model performs in erratic real-world scenarios. Meanwhile, offline testing with established benchmarks makes reliable comparisons across models and versions possible. Together, they produce a summary assessment that encompasses the model’s practical usefulness as well as its technical capabilities. This dual approach is especially crucial when assessing big language models for use in artificial intelligence systems, where performance must be dependable in a wide range of circumstances and ethical issues necessitate thorough testing prior to public release.
Learn more on ChatGPT to understand LLMs better by reading:
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