LLM Evaluation Guide: A Complete Analysis from Basics to 2025 Latest Benchmarks

In the field of Artificial Intelligence, training or fine-tuning a Large Language Model (LLM) is just the first step. The real challenge often lies in the subsequent question: How exactly do we judge whether this model performs well? The market is flooded with various leaderboards, benchmarks claiming to test reasoning or coding abilities, and academic papers constantly refreshing the “State of the Art” (SOTA). However, what do these scores actually mean?

Based on the experience of the Hugging Face team evaluating over 15,000 models in The LLM Evaluation Guidebook, this article delves into the core mechanisms of LLM evaluation, common pitfalls, and the evaluation tools most worth watching in 2025.

Why is Model Evaluation So Important?

For users in different roles, the purpose of evaluation is vastly different. If you are a Model Builder, the goal is usually to confirm whether a new architecture or data recipe is effective. This requires “Ablations” to compare the impact of different design choices. The evaluation tools needed here must have a high Signal-to-Noise Ratio, capable of running quickly and cheaply for repeated testing during development.

Conversely, for a Model User, the goal is to find the most suitable model for a specific application scenario. In this case, relying solely on general leaderboards might not be precise enough. Users need to focus more on tests highly relevant to actual application scenarios, or even design customized evaluation processes.

Interestingly, the definition of “Artificial General Intelligence” (AGI) is still unclear. Therefore, instead of pursuing a vague intelligence metric, it is better to focus on measuring the model’s performance on specific, clear, and useful tasks.

Understanding the Basics of LLM Operation: Prerequisites for Evaluation

To conduct effective evaluation, one must first understand how the model “reads” and “generates” content. This involves two key concepts: Tokenizer and the reasoning mechanism.

Tokenization: The World in the Eyes of the Model

Large language models are essentially mathematical functions; they cannot process text directly, only numbers. Therefore, input text is first sliced into small units called Tokens. This process is full of details and variables:

• Handling of Numbers: Different tokenizers slice numbers differently. Some treat a number as a single Token, while others split it into multiple digits. This directly affects the model’s ability to perform mathematical reasoning. For example, some models might perform poorly on arithmetic tasks due to tokenization, not a lack of logical ability—they simply “can’t read” the question.
• Multilingual Unfairness: Current mainstream BPE (Byte Pair Encoding) tokenization is usually trained based on English corpora. This leads to non-English languages (such as Thai or Traditional Chinese) often requiring more Tokens to express the same meaning. This not only increases inference costs but may also cause bias during evaluation because the model needs to “memorize” longer sequences.
• Format Sensitivity: Most models in 2025 have undergone Instruction Tuning. If specific Chat Templates are not strictly followed during evaluation—for instance, missing a specific System Prompt or tag—the model’s performance might plummet.

To learn more about how tokenizers work, you can refer to Hugging Face’s NLP Course or related documentation.

Reasoning and Generation: Two Main Evaluation Paths

When evaluating models, there are primarily two methods, suitable for different task scenarios:

1. Log-likelihood Evaluation: This is usually used for multiple-choice questions. The system does not require the model to generate text but calculates the probability of the model selecting options A, B, C, or D. The option with the highest probability is the model’s choice. This method is fast, low-cost, and eliminates issues with mismatched generation formats.
2. Generative Evaluation: This involves letting the model actually generate a text response to a question. This is closer to real-world usage scenarios, especially for code generation, translation, or open-ended Q&A. However, scoring such responses is more difficult because the correct answer can be expressed in myriad ways.

Benchmarks You Must Know in 2025

As model capabilities improve, many old benchmarks have become “saturated,” meaning model scores have surpassed humans or differences are negligible, losing their discriminative power. At the same time, “Data Contamination” is a major issue, with many test questions already included in the models’ training data. Here are the evaluation sets with better reference value in 2025:

1. Reasoning & Commonsense

Early datasets like ARC or HellaSwag are classic but slightly simple for modern models.

• ARC-AGI: A highly challenging abstract reasoning test requiring models to learn rules from very few samples.
• Zebra Logic: Uses logic puzzles to test reasoning abilities. Its feature is the ability to infinitely generate new puzzles, effectively preventing data contamination.

2. Knowledge

MMLU used to be the gold standard for knowledge evaluation but now faces severe saturation and error issues.

• MMLU-Pro: Fixes issues of the original MMLU, increases question complexity and the number of options, making it a better alternative currently.
• GPQA: Contains PhD-level difficult questions in biology, physics, and chemistry, designed so that only experts in the field can answer, and even Google searches struggle to find the answer.
• Humanity’s Last Exam: A relatively new high-difficulty dataset written by experts in various fields, aiming to test model limits.

3. Math & Code

GSM8K has become too simple, with many models even showing “overfitting” to specific question types.

• AIME 24/25: American Invitational Mathematics Examination questions, updated annually, perfect for detecting if a model has “memorized” old question banks.
• LiveCodeBench: Collects questions from contest sites like LeetCode and records the release time. This is a very smart design to evaluate model performance on new questions released “after the training cutoff,” effectively avoiding contamination.
• SWE-Bench: Tests the model’s ability to solve issues in real GitHub Repositories, which is closer to an engineer’s daily work than simply writing a Python function.

4. Long Context & Instruction Following

• RULER & NIAH: Tests the model’s ability to retrieve specific information within long documents (Needle In A Haystack).
• IFEval: An excellent tool for evaluating if a model is obedient. It doesn’t judge content quality but checks if the model followed format requirements (e.g., no punctuation, must exceed 400 words, must use JSON format, etc.). Such evaluations usually provide very objective data.

5. Agentic & Tool Use

With the rise of the Agent concept, evaluating how models use tools has become crucial.

• GAIA: Tests the model’s ability to combine reasoning, tool calling, and retrieval to solve real-world problems.
• TauBench: Simulates retail or airline booking systems to evaluate the accuracy of models updating databases during complex conversations.

Building Your Own Evaluation Process: When General Tests Aren’t Enough

If market benchmarks cannot meet specific needs, building your own evaluation set is an inevitable choice. It sounds laborious, but for commercial applications, it is the action with the highest ROI.

Using Synthetic Data

Using more powerful models (like GPT-4 or Claude 3.5 Sonnet) to generate test data is a trend.

• Rule Generation: For logic or code tasks, scripts can generate an infinite amount of test questions and automatically verify answers.
• Model Generation: Let high-end models read your private documents and generate relevant QA pairs. But remember, even with automated generation, Human Review is indispensable to ensure quality.

Preventing Data Contamination

Assume all data published on the web will eventually be learned by models. To avoid this, use “Canary String” technology by adding specific random strings to your private evaluation set. If a future model completes this string, it proves the model has “peeked” at this exam paper.

The Grading Dilemma: Who Acts as the Judge?

For generative tasks, how to score is a big question.

Automatic Metrics

• Exact Match (EM): The answer must be exactly identical. Effective for math or code, but too strict for open-ended Q&A.
• BLEU / ROUGE: These metrics originating from translation primarily compare word overlap rates. They are fast and cheap but often fail to reflect semantic correctness.

Functional Scorers

This is currently one of the most highly recommended methods. For example, in code generation, directly execute the code to see how many Unit Tests pass. In IFEval, use a program to check if the output meets format constraints. This method is objective and interpretable.

LLM-as-a-Judge

Using powerful models (like GPT-4) to score the output of other models. This is convenient but has implicit biases:

• Position Bias: Judge models often tend to think the first answer presented is better.
• Verbosity Bias: Judge models usually give higher scores to longer, wordier answers, even if the content isn’t entirely correct.
• Self-Preference: Models tend to give high scores to answers similar to their own style.

To mitigate these biases, one can adopt “Pairwise Comparison” and randomly swap answer orders, or use a Jury composed of multiple models. For more techniques on LLM as a judge, refer to research like Prometheus.

FAQ

Q1: What is the essential difference between Log-likelihood Evaluation and Generative Evaluation? Log-likelihood evaluation focuses on the model’s “confidence level” in preset options; it doesn’t require the model to write an answer but looks at which option the model thinks has the highest probability, which is suitable for multiple-choice questions and is fast. Generative evaluation requires the model to actually produce text, which fits real conversation scenarios better and tests the model’s ability to connect expression and reasoning, but scoring is harder and more expensive.

Q2: Why do scores for the same model differ across different leaderboards? This usually stems from differences in “implementation details.” Tiny changes in Prompts, whether Chat Templates are applied correctly, settings of random Seeds, or even code differences in evaluation frameworks (like lm-eval-harness vs. HELM) can cause score fluctuations. Additionally, some models might be over-optimized for the format of specific leaderboards.

Q3: What is Data Contamination, and why is it important? Data contamination refers to test questions used for evaluation being accidentally included in the model’s training data. This is like a student seeing the exam paper and answers before the test; the resulting high score cannot represent true ability. When choosing a model, prioritize results from evaluations with contamination prevention mechanisms (like LiveCodeBench).

Q4: Should I use an LLM to evaluate my model (LLM-as-a-Judge)? This is a trade-off. LLM judges are cheaper and faster than humans, suitable for large-scale preliminary screening. But be aware of the biases mentioned above (like favoring verbosity). It is recommended to use LLM judges in the early development stages or for non-critical tasks, but for critical decisions or final validation, functional tests or expert human evaluation remain indispensable.

Conclusion

LLM evaluation is both a science and an art. In 2025, we have progressed from simple “scoring” to focusing more on model performance in real scenarios, tool usage, and complex reasoning.

No single perfect metric can summarize all capabilities of a model. The key lies in “Critical Thinking”: understanding the limitations of each benchmark, choosing uncontaminated data, and striking a balance between automated evaluation and human verification. When you see an astonishing SOTA score, you might want to ask one more question: Did it really understand the problem, or did it just memorize the answer?