---
title: "LLM evaluation"
source: "https://systems-analysis.info/eng/LLM_evaluation"
wiki: "systems-analysis.info/eng"
article: "LLM_evaluation"
language: "en"
categories:
  - "Category:English"
  - "Category:Large language models"
  - "Category:LLM benchmarks"
  - "Category:LLM evaluation"
  - "Category:Machine learning"
  - "Category:Technology"
revision_id: 202
wiki_created_at: 2026-09-06T22:18:50Z
wiki_modified_at: 2026-09-06T22:18:50Z
downloaded_at: 2026-09-07T22:21:49Z
---

# LLM evaluation

**Large Language Model (LLM) evaluation** is a discipline within the field of artificial intelligence that provides standardized methods for measuring the capabilities, limitations, and risks of [language models](https://systems-analysis.info/eng/Large_language_model "Large language model")<sup>[\[1\]](https://systems-analysis.info/eng/LLM_evaluation#cite_note-chang2023-1)</sup>. As LLMs are integrated into key sectors such as healthcare and finance, their objective evaluation becomes essential for ensuring safety, reliability, and fairness<sup>[\[2\]](https://systems-analysis.info/eng/LLM_evaluation#cite_note-ccl-survey-2)</sup>.

LLM evaluation serves several fundamental functions:

- Measuring capabilities: Objectively comparing the performance of different models on standardized tasks.
- Tracking progress: Documenting achievements and identifying areas that require further improvement.
- Minimizing risks: Identifying potentially harmful outcomes, such as bias, hallucinations, and security issues.
- Informing developers and users: Providing transparent information to help select the most suitable model for a specific application.

## Core Approaches and Methodologies

Modern LLM evaluation began with the emergence of comprehensive benchmarks like **[GLUE](https://systems-analysis.info/eng/GLUE_Benchmark "GLUE Benchmark")** (General Language Understanding Evaluation), which set the standard for assessing general language understanding<sup>[\[3\]](https://systems-analysis.info/eng/LLM_evaluation#cite_note-wang2018-3)</sup>. As models began to surpass human performance on GLUE, more challenging successors like **[SuperGLUE](https://systems-analysis.info/eng/SuperGLUE_(benchmark) "SuperGLUE (benchmark)")** were developed<sup>[\[4\]](https://systems-analysis.info/eng/LLM_evaluation#cite_note-understanding-benchmarks-4)</sup>.

A fundamental shift occurred with the introduction of multitask benchmarks such as **[MMLU](https://systems-analysis.info/eng/MMLU_Benchmark "MMLU Benchmark")** and **[BIG-bench](https://systems-analysis.info/eng/BIG-bench_(benchmark) "BIG-bench (benchmark)")**, which test models on a wide range of knowledge and reasoning abilities, going beyond purely linguistic tasks<sup>[\[1\]](https://systems-analysis.info/eng/LLM_evaluation#cite_note-chang2023-1)</sup>.

### Key Metrics and Benchmarks

#### Automatic Metrics

- **[Perplexity](https://systems-analysis.info/eng/Perplexity_(metric) "Perplexity (metric)")**: A fundamental metric that measures how well a model predicts text. A lower perplexity indicates greater confidence in the model's predictions.
- **[BLEU](https://systems-analysis.info/eng/BLEU_(Bilingual_Evaluation_Understudy) "BLEU (Bilingual Evaluation Understudy)")** and **[ROUGE](https://systems-analysis.info/eng/ROUGE_(metric) "ROUGE (metric)")**: N-gram-based metrics that measure the lexical overlap between generated and reference texts. BLEU focuses on precision, while ROUGE focuses on recall<sup>[\[2\]](https://systems-analysis.info/eng/LLM_evaluation#cite_note-ccl-survey-2)</sup>.
- **[BERTScore](https://systems-analysis.info/eng/BERTScore_(metric) "BERTScore (metric)")**: A semantic metric that uses embeddings from [BERT](https://systems-analysis.info/eng/BERTScore_(metric) "BERTScore (metric)") to calculate semantic similarity. It can capture synonymy and paraphrasing, making it more accurate than n-gram-based metrics<sup>[\[5\]](https://systems-analysis.info/eng/LLM_evaluation#cite_note-zhang2019-bertscore-5)</sup>.

#### Specialized Benchmarks

Targeted benchmarks have been developed to assess specific abilities:

- **Code Generation**: **[HumanEval](https://systems-analysis.info/eng/HumanEval_Benchmark "HumanEval Benchmark")** evaluates a model's ability to generate correct software code from a text description by verifying its functionality with unit tests<sup>[\[6\]](https://systems-analysis.info/eng/LLM_evaluation#cite_note-chen2021-humaneval-6)</sup>.
- **Common Sense**: **[HellaSwag](https://systems-analysis.info/eng/HellaSwag_Benchmark "HellaSwag Benchmark")** tests a model's understanding of the physical world and cause-and-effect relationships by having it predict the most likely ending to an everyday situation<sup>[\[7\]](https://systems-analysis.info/eng/LLM_evaluation#cite_note-zellers2019-hellaswag-7)</sup>.
- **Academic Knowledge**: **[MMLU](https://systems-analysis.info/eng/MMLU_Benchmark "MMLU Benchmark")** (Massive Multitask Language Understanding) covers 57 subjects, from elementary mathematics to law and medicine, testing the breadth of a model's knowledge<sup>[\[8\]](https://systems-analysis.info/eng/LLM_evaluation#cite_note-hendrycks2020-mmlu-8)</sup>.
- **Frontiers of Capabilities**: **[BIG-bench](https://systems-analysis.info/eng/BIG-bench_(benchmark) "BIG-bench (benchmark)")** (Beyond the Imitation Game) is a collaborative project that includes 204 tasks designed to identify [emergent abilities](https://systems-analysis.info/eng/Emergence "Emergence")—skills that suddenly appear when a model reaches a critical scale<sup>[\[9\]](https://systems-analysis.info/eng/LLM_evaluation#cite_note-srivastava2022-bigbench-9)</sup>.

### Safety and Ethics Evaluation

- **Bias**: Datasets like **[BBQ](https://systems-analysis.info/eng/BBQ_(Bias_Benchmark_for_Question_Answering) "BBQ (Bias Benchmark for Question Answering)")** (Bias Benchmark for Question Answering) and **[BOLD](https://systems-analysis.info/eng/BOLD_(Bias_in_Open-Ended_Language_Generation_Dataset) "BOLD (Bias in Open-Ended Language Generation Dataset)")** (Bias in Open-ended Language generation Dataset) are used to assess social and demographic biases.
- **Toxicity**: Benchmarks such as **[RealToxicityPrompts](https://systems-analysis.info/eng/RealToxicityPrompts "RealToxicityPrompts")** provide prompts that provoke the generation of toxic content to evaluate a model's resilience.
- **Robustness**: Assessed using adversarial attacks. The **[PromptRobust](https://systems-analysis.info/eng/PromptRobust_(benchmark) "PromptRobust (benchmark)")** framework provides a comprehensive set of prompts to test model stability at the character, word, and sentence levels.

### Modern Standards and Frameworks

- **HELM** (Holistic Evaluation of Language Models): An initiative by Stanford University that proposes a "holistic" methodology. HELM evaluates models across multiple dimensions: accuracy, robustness, fairness, bias, toxicity, and efficiency<sup>[\[10\]](https://systems-analysis.info/eng/LLM_evaluation#cite_note-bommasani2022-helm-10)</sup>.
- **ISO/IEC 42001:2023**: The first international standard for AI management systems, establishing requirements for AI governance throughout its entire lifecycle.
- **Regulation (EU) 2024/1689 (EU AI Act)**: The first comprehensive AI regulation, requiring standardized evaluations for general-purpose models with systemic risks.
- **NIST AI Risk Management Framework 1.0**: A voluntary framework for developing and deploying trustworthy AI, developed by the U.S. National Institute of Standards and Technology.

## Challenges and Limitations of Existing Methods

- **Benchmark Saturation**: Many models achieve near-perfect scores on popular benchmarks, leading to the phenomenon of "benchmark chasing," where models are optimized for specific tests rather than general capabilities.
- **Data Contamination**: A critical issue where benchmark test data is accidentally included in the training set, leading to inflated and dishonest evaluation results.
- **Low Correlation with Human Judgment**: Automatic metrics like BLEU and ROUGE often correlate poorly with human quality assessments, especially in creative and open-ended tasks.

## Current Research and Trends

- **The LLM-as-a-Judge paradigm**: Using powerful LLMs (e.g., GPT-4) as "judges" to evaluate the responses of other models. This approach provides a scalable alternative to costly human evaluation.
- **Dynamic and Adaptive Evaluation**: Platforms like **[LMArena](https://systems-analysis.info/eng/LMArena_(Chatbot_Arena) "LMArena (Chatbot Arena)")** introduce a crowdsourced system with Elo ratings for real-world model evaluation through live user interactions.
- **Hybrid Approaches**: Combining automated metrics with human judgment and LLM-based evaluation to obtain a more complete and reliable picture of model performance.

The landscape of LLM evaluation continues to evolve, moving toward the creation of multidimensional, standardized, and reproducible frameworks that consider not only accuracy but also the social and ethical aspects of AI technology application<sup>[\[1\]](https://systems-analysis.info/eng/LLM_evaluation#cite_note-chang2023-1)</sup>.

## External links

- <a href="https://crfm.stanford.edu/helm/" class="external text" rel="nofollow">Stanford HELM</a> — Official website of the Holistic Evaluation of Language Models project.
- <a href="https://lmsys.org/blog/2023-05-03-arena/" class="external text" rel="nofollow">Chatbot Arena</a> — A platform for the comparative evaluation of chatbots based on human preferences.
- <a href="https://en.wikipedia.org/wiki/Language_model_benchmark" class="external text" rel="nofollow">Language model benchmark — Wikipedia</a>

## See also

- [FLORES-200](https://systems-analysis.info/eng/FLORES-200 "FLORES-200")

## Literature

- Wang, A. et al. (2018). *GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding*. <a href="https://arxiv.org/abs/1804.07461" class="external text" rel="nofollow">arXiv:1804.07461</a>.
- Zhang, T. et al. (2019). *BERTScore: Evaluating Text Generation with BERT*. <a href="https://arxiv.org/abs/1904.09675" class="external text" rel="nofollow">arXiv:1904.09675</a>.
- Wang, A. et al. (2019). *SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems*. <a href="https://arxiv.org/abs/1905.00537" class="external text" rel="nofollow">arXiv:1905.00537</a>.
- Zellers, R. et al. (2019). *HellaSwag: Can a Machine Really Finish Your Sentence?*. <a href="https://arxiv.org/abs/1905.07830" class="external text" rel="nofollow">arXiv:1905.07830</a>.
- Hendrycks, D. et al. (2020). *Measuring Massive Multitask Language Understanding*. <a href="https://arxiv.org/abs/2009.03300" class="external text" rel="nofollow">arXiv:2009.03300</a>.
- Gehman, S. et al. (2020). *RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models*. <a href="https://arxiv.org/abs/2009.11462" class="external text" rel="nofollow">arXiv:2009.11462</a>.
- Chen, M. et al. (2021). *Evaluating Large Language Models Trained on Code*. <a href="https://arxiv.org/abs/2107.03374" class="external text" rel="nofollow">arXiv:2107.03374</a>.
- Parrish, A. et al. (2021). *BBQ: A Hand-Built Bias Benchmark for Question Answering*. <a href="https://arxiv.org/abs/2110.08193" class="external text" rel="nofollow">arXiv:2110.08193</a>.
- Dhamala, J. et al. (2021). *BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation*. <a href="https://arxiv.org/abs/2101.11718" class="external text" rel="nofollow">arXiv:2101.11718</a>.
- Srivastava, A. et al. (2022). *Beyond the Imitation Game: Quantifying and Extrapolating the Capabilities of Language Models*. <a href="https://arxiv.org/abs/2206.04615" class="external text" rel="nofollow">arXiv:2206.04615</a>.
- Bommasani, R. et al. (2022). *Holistic Evaluation of Language Models*. <a href="https://arxiv.org/abs/2211.09110" class="external text" rel="nofollow">arXiv:2211.09110</a>.
- Chang, Y. et al. (2023). *A Survey on Evaluation of Large Language Models*. <a href="https://arxiv.org/abs/2307.03109" class="external text" rel="nofollow">arXiv:2307.03109</a>.
- Zhuang, Y. et al. (2023). *Through the Lens of Core Competency: Survey on Evaluation of Large Language Models*. <a href="https://aclanthology.org/2023.ccl-2.8/" class="external text" rel="nofollow">ACL Anthology:2023.ccl-2.8</a>.
- Zhu, K. et al. (2023). *PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts*. <a href="https://arxiv.org/abs/2306.04528" class="external text" rel="nofollow">arXiv:2306.04528</a>.

## References

1.  <span id="cite_note-chang2023-1">↑ <sup>[1.0](https://systems-analysis.info/eng/LLM_evaluation#cite_ref-chang2023_1-0)</sup> <sup>[1.1](https://systems-analysis.info/eng/LLM_evaluation#cite_ref-chang2023_1-1)</sup> <sup>[1.2](https://systems-analysis.info/eng/LLM_evaluation#cite_ref-chang2023_1-2)</sup> Chang, Y., et al. (2023). "A Survey on Evaluation of Large Language Models". *arXiv*. <a href="https://arxiv.org/abs/2307.03109" class="external autonumber" rel="nofollow">[1]</a></span>
2.  <span id="cite_note-ccl-survey-2">↑ <sup>[2.0](https://systems-analysis.info/eng/LLM_evaluation#cite_ref-ccl-survey_2-0)</sup> <sup>[2.1](https://systems-analysis.info/eng/LLM_evaluation#cite_ref-ccl-survey_2-1)</sup> Zhuang, Y., et al. (2023). "Through the Lens of Core Competency: Survey on Evaluation of Large Language Models". *ACL Anthology*. <a href="https://aclanthology.org/2023.ccl-2.8/" class="external autonumber" rel="nofollow">[2]</a></span>
3.  <span id="cite_note-wang2018-3">[↑](https://systems-analysis.info/eng/LLM_evaluation#cite_ref-wang2018_3-0) Wang, A., et al. (2018). "GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding". *arXiv*.<a href="https://arxiv.org/abs/1804.07461" class="external autonumber" rel="nofollow">[3]</a></span>
4.  <span id="cite_note-understanding-benchmarks-4">[↑](https://systems-analysis.info/eng/LLM_evaluation#cite_ref-understanding-benchmarks_4-0) Kumar, Pradosh. "Understanding Benchmarking in NLP: GLUE, SuperGLUE, HELM, MMLU, and BIG-Bench". *Medium*.</span>
5.  <span id="cite_note-zhang2019-bertscore-5">[↑](https://systems-analysis.info/eng/LLM_evaluation#cite_ref-zhang2019-bertscore_5-0) Zhang, T., et al. (2019). "BERTScore: Evaluating Text Generation with BERT". *arXiv*.</span>
6.  <span id="cite_note-chen2021-humaneval-6">[↑](https://systems-analysis.info/eng/LLM_evaluation#cite_ref-chen2021-humaneval_6-0) Chen, M., et al. (2021). "Evaluating Large Language Models Trained on Code". *arXiv*.</span>
7.  <span id="cite_note-zellers2019-hellaswag-7">[↑](https://systems-analysis.info/eng/LLM_evaluation#cite_ref-zellers2019-hellaswag_7-0) Zellers, R., et al. (2019). "HellaSwag: Can a Machine Really Finish Your Sentence?". *arXiv*.</span>
8.  <span id="cite_note-hendrycks2020-mmlu-8">[↑](https://systems-analysis.info/eng/LLM_evaluation#cite_ref-hendrycks2020-mmlu_8-0) Hendrycks, D., et al. (2020). "Measuring Massive Multitask Language Understanding". *arXiv*.</span>
9.  <span id="cite_note-srivastava2022-bigbench-9">[↑](https://systems-analysis.info/eng/LLM_evaluation#cite_ref-srivastava2022-bigbench_9-0) Srivastava, A., et al. (2022). "Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models". *arXiv*.</span>
10. <span id="cite_note-bommasani2022-helm-10">[↑](https://systems-analysis.info/eng/LLM_evaluation#cite_ref-bommasani2022-helm_10-0) Bommasani, R., et al. (2022). "Holistic Evaluation of Language Models". *arXiv*. <a href="https://arxiv.org/abs/2211.09110" class="external autonumber" rel="nofollow">[4]</a></span>