Large Language Model Evaluation in 2025: 10+ Metrics & Methods

Large Language Model evaluation (i.e., LLM eval) refers to 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:

Top models & metrics for specific aims

We created a summary of the best datasets and metrics for your specific aims:

5 steps of benchmarking LLMs

1. Benchmark selection 

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. Hugging Face has a leaderboard to evaluate, discuss, and list current Open LLMs.¹

Sticking to the same benchmarking methods and datasets can create metric problems in LLM evaluation and lead to unchanging results. We advise updating your benchmark and evaluation metrics to capture LLM’s various capabilities. Some of the most popular benchmarking datasets are:

• MMLU-Pro refines the MMLU dataset by offering ten choices per question, requiring more reasoning and reducing noise through expert review.²
• GPQA features challenging questions designed by domain experts, validated for difficulty and factuality, and is accessible only through gating mechanisms to prevent contamination.³
• MuSR consists of algorithmically generated complex problems, requiring models to use reasoning and long-range context parsing, with few models performing better than random.⁴
• MATH is a compilation of difficult high-school-level competition problems, formatted for consistency, focusing on the hardest questions.⁵
• IFEval tests models’ ability to follow explicit instructions and formatting using strict metrics for evaluation.⁶
• BBH includes 23 challenging tasks from the BigBench dataset, measuring objective metrics and language understanding, and correlates well with human preference.⁷
• HumanEval evaluates the performance of an LLM in code generation, focusing particularly on its functional correctness.⁸
• TruthfulQA addresses hallucination problems by measuring an LLM’s ability to generate true answers.⁹
• General Language Understanding Evaluation (GLUE) and SuperGLUE test the performance of natural language processing (NLP) models, particularly for language-understanding tasks.¹⁰

Key research takeaways also include the need for better benchmarking, collaboration, and innovation to push the boundaries of LLM capabilities. 

2. Dataset preparation

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.

3. Model training and fine-tuning 

Models trained as 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.

4. Model evaluation

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.

5. Comparative analysis 

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.¹¹

Evaluation metrics

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.

General performance metrics

• Accuracy is the percentage of correct responses in binary tasks.
• Recall is the actual number of true positives versus false ones in LLM responses.
• F1 score blends accuracy and recall into one metric. F1 scores range from 0–1, with 1 signifying excellent recall and precision.
• Latency is the model’s efficiency and speed.
• Toxicity shows the immunity of the model to harmful or offensive content in the outputs.
• Elo ratings for AI models rank language models based on competitive performance in shared tasks, similar to how chess players are ranked. Models compete by generating outputs for the same tasks, and ratings are adjusted as new models or tasks are introduced.

Text-specific metrics

• Coherence is the score of the logical flow and consistency of the generated text.
• Diversity measures assess the variety and uniqueness of the generated responses. It involves analyzing metrics such as n-gram diversity or measuring the semantic similarity between generated responses. Higher diversity scores indicate more diverse and unique outputs.
• Perplexity is a measure used to evaluate the performance of language models. It quantifies how well the model predicts a sample of text. Lower perplexity values indicate better performance.

Figure 2: Examples of perplexity evaluation.

• BLEU (Bilingual Evaluation Understudy) is a metric used in machine translation tasks. It compares the generated output with one or more reference translations and measures their similarity. BLEU scores range from 0 to 1, with higher scores indicating better performance.

• ROUGE (Recall-Oriented Understudy for Gissing Evaluation) is a set of metrics used to evaluate the quality of summaries. It compares the generated summary with one or more reference summaries and calculates precision, recall, and F1 scores (Figure 3). ROUGE scores provide insights into the language model’s summary generation capabilities.

Figure 3: An example of a ROUGE evaluation process.¹²

Evaluation metrics can be judged by a model or a human. Both have their own advantages and use cases:

LLM evaluating LLMs

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.

Human-in-the-loop evaluation

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 delicate industries like healthcare, finance, and legal services.

LLM evaluation tools & frameworks

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.

Open-source frameworks

Comprehensive evaluation frameworks

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.

• LEval (Language Model Evaluation) is a framework for evaluating LLMs on long-context understanding.¹³ LEval is a benchmark suite featuring 411 questions across eight tasks, with contexts from 5,000 to 200,000 tokens. It evaluates how well models perform information retrieval and reasoning with lengthy documents. The suite includes tasks like academic summarization, technical document generation, and multi-turn dialogue coherence, allowing researchers to test models on practical applications rather than isolated linguistic tasks.
• Prometheus is an open-source framework that uses LLMs as judges with systematic prompting strategies.¹⁴ It’s designed to produce evaluation scores that align with human preferences and judgment.

Testing approaches

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.

• DAG (Deep Acyclic Graph) evaluation workflows use directed acyclic graphs to represent evaluation pipelines, though it’s not a specific evaluation tool.
• Dynamic prompt testing evaluates models by exposing them to evolving, real-world scenarios that mimic user interaction. This method evaluates how models respond to complex, multi-layered queries & ambiguous prompts.
• The energy and hardware efficiency benchmark framework measures the energy consumption and computational efficiency of models during training and inference. It focuses on sustainability metrics, such as carbon emissions and power usage.

Commercial evaluation platforms

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.

• DeepEval (Confident AI) is a developer-focused testing framework that helps evaluate LLM applications using predefined metrics for accuracy, bias, and performance. It interfaces with CI/CD pipelines for automated testing.
• Azure AI Studio Evaluation (Microsoft) offers built-in evaluation tools for comparing different models and prompts, with automatic metric tracking and human feedback collection capabilities.
• Prompt Flow (Microsoft) is a development tool for building, evaluating, and deploying LLM applications. Its built-in evaluation capabilities allow for systematic testing across models and prompts.
• LangSmith (LangChain) is a platform for debugging, testing, and monitoring LLM applications, with features for comparing models and tracing execution paths.
• TruLens (TruEra) is an open-source toolkit for evaluating and explaining LLM applications, with features for tracking hallucinations, relevance, and groundedness.
• Vertex AI Studio (Google) provides tools to test and evaluate model outputs, with both automatic metrics and human evaluation capabilities within Google’s AI ecosystem.
• Amazon Bedrock includes evaluation capabilities for foundation models, allowing developers to test and compare different models before deployment.
• Parea AI is a platform for evaluating and monitoring LLM applications with a specific focus on data quality and model performance.

Pre-evaluated benchmarks

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:

• Hallucination – Evaluates the accuracy and factual consistency of generated content.
• AI Coding – Measures coding ability, correctness, and execution.
• AI Reasoning – Assesses logical inference and problem-solving capabilities.

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.

Evaluation use cases

1. Performance assessment

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.

2. Model comparison

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.

3. Bias detection and mitigation

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.

4. User satisfaction and trust

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.

5. Evaluation of RAG systems

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.

What are the common challenges with existing LLM evaluation methods?

While existing evaluation methods for Large Language Models (LLMs) provide valuable insights, they are imperfect. The common issues associated with them are: 

Overfitting

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, highlighting the need for additional testing methods like GSM1k.

Lack of diverse metrics

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.

Subjectivity & high cost of human evaluations

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.

Biases in automated evaluations

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).

• Order bias: First items favored.
• Compassion fade: Names are favored vs. anonymized code words
• Ego bias: Similar responses are favored
• Salience bias: Longer responses are preferred
• Bandwagon effect: Majority belief is preferred
• Attention bias: Sharing more irrelevant information is preferred

Limited reference data

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.

Generalization to real-world scenarios

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.

Adversarial attacks

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.

Complexity and cost of multi-dimensional evaluation

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.

Best practices to overcome problems of large language model evaluation methods

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.

Known training data

Leverage foundation models that share their training data to prevent contamination.

Multiple evaluation metrics

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:

• Fluency
• Coherence 
• Relevance 
• Diversity 
• Context understanding 

Enhanced human evaluation

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.

Diverse reference data

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.

Incorporating multiple metrics

Encourage the generation of diverse responses and evaluate the uniqueness of generated text through methods such as n-gram diversity or semantic similarity measurements.

Real-world evaluation

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.

Robustness evaluation

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.

Leverage LLMOps

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.

What do leading researchers think about evals?

Trust is eroding in evaluations that are no longer capable of accurately evaluating model performance:

Conclusion

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.

Further reading

Learn more on ChatGPT to understand LLMs better by reading:

• AI Transformation: 6 Real-Life Strategies to Succeed
• ChatGPT Education Use Cases, Benefits & Challenges
• How to Use ChatGPT for Business: Top 40 Applications
• GPT-4: In-depth Guide

External Links

• 1. Open LLM Leaderboard - a Hugging Face Space by open-llm-leaderboard. Open LLM Leaderboard
• 2. GitHub - TIGER-AI-Lab/MMLU-Pro: The code and data for "MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark" [NeurIPS 2024].
• 3. GitHub - idavidrein/gpqa: GPQA: A Graduate-Level Google-Proof Q&A Benchmark.
• 4. TAUR-Lab/MuSR · Datasets at Hugging Face. TAUR Lab at UT Austin
• 5. GitHub - hendrycks/math: The MATH Dataset (NeurIPS 2021).
• 6. lm-evaluation-harness/lm_eval/tasks/ifeval/README.md at main · EleutherAI/lm-evaluation-harness · GitHub.
• 7. lukaemon/bbh · Datasets at Hugging Face.
• 8. GitHub - openai/human-eval: Code for the paper "Evaluating Large Language Models Trained on Code".
• 9. domenicrosati/TruthfulQA · Datasets at Hugging Face.
• 10. aps/super_glue · Datasets at Hugging Face.
• 11. Open LLM Leaderboard - a Hugging Face Space by open-llm-leaderboard. Open LLM Leaderboard
• 12. Introduction to Text Summarization with ROUGE Scores | Towards Data Science. Towards Data Science
• 13. GitHub - OpenLMLab/LEval: [ACL'24 Outstanding] Data and code for L-Eval, a comprehensive long context language models evaluation benchmark.
• 14. prometheus-eval/prometheus-13b-v1.0 · Hugging Face.