The Complete Guide to LLM Benchmarks: A Detailed Catalog
Large language models (LLMs) have revolutionized the world of artificial intelligence in recent years, powering everything from chatbots to content generation. However, this advancement also brings new challenges, including ensuring that models are optimal and ethical. Critical to this task are benchmarks, which are standardized ways to quantify and compare AI models to ensure consistency, reliability, and fairness. With the rapid advancement of LLMs, benchmarking capabilities have also expanded significantly.
In this post, we present a detailed catalog of benchmarks, categorized by complexity, dynamics, assessment objectives, end-task specifications, and risk types. Understanding their differences will help you navigate the rapidly evolving LLM benchmark landscape.
Basic principles:
- Complexity: Comprehensive benchmarks for exploring multiple evaluation domains using dynamically updated datasets.
- System type specification: benchmarks tuned to work with specific systems, such as Co-pilot, multimodal, retrieval-augmented generation (RAG), tool-use, and embedded LLM.
- Purpose of assessment: Capability-oriented benchmarks assess the accuracy of task performance, while risk-oriented benchmarks assess potential risks.
- Specification of final tasks: benchmarks that evaluate tasks such as question answering, summarizing, text classification, translation, information extraction, and code generation.
- Specification of risk types: benchmarks that assess LLM risks, including in terms of privacy, reliability, fairness, explainability and sustainability.
What is LLM benchmarking?
LLM benchmarks are used to evaluate the accuracy of LLM using standardized tasks or prompts. This process involves selecting tasks, generating input prompts, and receiving model responses with a numerical assessment of the accuracy of the models. Such assessment is essential in AI audits, as it allows for objective measurement of LLM parameters, ensuring the reliability and ethicality of models in order to maintain public trust and further responsible development of AI.
Benchmarks for LLM can be thought of as two spectrums: from simple to complex and from risk-oriented to capability-oriented. This creates four main segments of benchmarks. Complex benchmarks cover many different evaluation goals and types of systems, while simple benchmarks are focused on a specific goal. Capability-oriented benchmarks focus on assessing the accuracy of task performance, while risk-oriented benchmarks assess the potential risks of models.
LLM Benchmark Complexity
Simple and Composite LLM Benchmarks
Many LLM benchmarks are fairly straightforward, with specific goals and evaluation methodologies, but new benchmarks are becoming increasingly complex. Simple datasets typically focus on specific tasks, providing clear metrics. Composite datasets, on the other hand, include multiple goals and methodologies. These complex benchmarks allow for multiple facets of LLM accuracy to be evaluated simultaneously, providing a more holistic picture of its capabilities and limitations. Some of these complex benchmarks include AlpacaEval, MT-bench, HELM (Holistic Evaluation of Language Models), and BIG-Bench Hard (BBH).
Table 1. Composite benchmarks aimed at testing capabilities
Static and Dynamic LLM Benchmarks
Most benchmarks are static, meaning they consist of a fixed set of questions or tasks that do not change over time; however, some benchmarks are dynamic, meaning new questions or tasks are constantly being added. This helps maintain their relevance and prevents models from overfitting to a specific dataset. Examples of such benchmarks include LMSYS Chatbot Arena, LiveBench.
Table 2. Dynamic benchmarks
Specification of system types
To account for the wide range of LLM applications, benchmarks are often designed with system type specifications in mind to ensure the effectiveness and reliability of the models in real-world use. These benchmarks focus on assessing how well LLM performs in a variety of integrated systems. The main system types are:
- Co-pilot systems: Co-pilot benchmarks focus on how effectively an LLM can assist users in real time, increasing productivity and efficiency in software environments. This includes the model’s ability to understand context, offer relevant recommendations, automate repetitive tasks, and integrate with other software tools that support users’ workflow.
- Retrieval-Augmented Generation (RAG) systems: RAG systems combine the strengths of LLM with powerful information extraction mechanisms. Such benchmarks assess the model's ability to extract relevant information from external databases and embed this information into holistic and contextually appropriate answers. They are especially important for applications that require timely or highly specific information.
- Tool-Use Systems: Tool-use benchmarks evaluate the model's ability to interact with external tools and APIs. This includes executing commands, retrieving data, and performing complex operations based on user input. Effective tool-use allows LLMs to extend their capabilities, making them more versatile and practical across domains, from data analysis to software development.
- Multimodal systems: Multimodal benchmarks test a model’s ability to process and generate output across multiple types of data, such as text, images, and audio. This is important for areas such as media production, training, and technical support, where integrated, context-aware responses across multiple media types are required. Benchmarks assess how well a model understands and combines information from different modalities to provide coherent and relevant results.
- Embedded systems: Embedded systems benchmarks focus on integrating LLMs into physical systems, such as robots or IoT devices. These benchmarks evaluate the model’s ability to understand and navigate physical spaces, interact with objects, and perform tasks that require understanding the physical world. This is critical for applications in robotics, smart home devices, and other areas where LLMs must operate and respond in real-world conditions.
Table 3. Benchmarks of system type specifications
Benchmarking objectives: opportunity-oriented and risk-oriented
Another important difference is the purpose of the benchmarks, which can be capability or risk testing. Capability-oriented benchmarks evaluate the performance of an LLM in performing specific tasks, such as translating texts or summarizing. In other words, these benchmarks are important for measuring the functional strengths of the model. Examples of capability-oriented LLMs include AlpacaEval, MT-bench, HELM, BIG-Bench Hard (BBH), and LiveBench.
Moreover, the core performance metrics are a subset of the capability-oriented benchmarks that test the effectiveness of LLM in text generation by assessing key metrics such as throughput, latency, and token cost.
Table 4. Key performance indicators
Risk-based benchmarks focus on potential vulnerabilities of large language models. Such risks can be broken down into specific categories, such as reliability, privacy, security, fairness, explainability, sustainability, and other social aspects. By identifying and addressing such risks, LLMs can be made not only effective, but also safe and ethical. Examples of composite benchmarks include TrustLLM, AIRBench, and Redteaming Resistance Benchmark.
Table 5. Risk-focused composite benchmarks
Specification of final tasks
To evaluate the real-world applications of large language models, it is necessary to understand the full range of their tasks. Therefore, to evaluate the specific capabilities of LLM, one can use the tasks:
- Understanding and answering questions: This task tests the model's ability to understand and interpret written text. It evaluates how well the model can answer questions in conversations, demonstrating its level of comprehension and retention of information.
- Summary: This task evaluates the ability of a model to compress long texts into short, coherent summaries while preserving important information and meaning. Tools like ROUGE are often used to evaluate the quality of such summaries.
- Classification of texts: Text classification is the assignment of pre-defined labels or categories to a text document based on its content. This fundamental NLP task is applied in many areas, such as sentiment analysis, topic tagging, spam recognition, and so on.
- Translation: This task evaluates the accuracy and fluency of a model in translating texts from one language to another. Metrics that compare the model's translations with translations by real people are most often used to evaluate quality.
- Extracting information: This task tests the model's ability to identify and extract specific pieces of information from unstructured text. It includes tasks such as named entity recognition (NER) and relationship extraction, which are essential when converting text data into structured formats.
- Code generation: This task evaluates the model's ability to generate code blocks or complete code based on natural language descriptions. It involves understanding programming languages, syntax, and logical problem solving.
- Mathematical reasoning: This task measures the model's ability to understand and solve mathematical problems, including concepts in arithmetic, algebra, calculus, and other areas of mathematics. It evaluates the model's logical reasoning and mathematical abilities.
- Common sense reasoning: This task evaluates the model's ability to apply everyday knowledge and logical reasoning to answer questions or solve problems. It evaluates the model's understanding of the world and its ability to create intelligent inferences.
- General and subject knowledge: This task tests the capabilities of the model in specific domains such as medicine, law, finance, and engineering. It evaluates the depth and accuracy of the model's knowledge in specialized areas, which is very important for areas requiring expert-level information.
Table 6. Benchmarks of final tasks
Risk-Based Benchmarks: Details
Reliability benchmarks
Robustness benchmarks are used to evaluate how well LLM performs in various settings, including noisy or adversarial inputs. Such challenges ensure the robustness and consistency of the model in diverse and challenging scenarios.
Table 7. Reliability assessment benchmarks
Security Benchmarks
Security benchmarks focus on the resilience of a model to attacks, such as
data poisoning or exploits, ensuring that the integrity and resilience of the model is verified.
Table 8. Security assessment benchmarks
Privacy Benchmarks
Privacy benchmarks evaluate a model's ability to protect sensitive information while ensuring the privacy and security of user data and interactions.
Table 9. Privacy Evaluation Benchmarks
Fairness Benchmarks
Fairness benchmarks assess a model's responses to fairness and impartiality across different demographic groups, helping to improve inclusivity and prevent discrimination.
Table 10. Fairness Assessment Benchmarks
Explainability Benchmarks
Explainability benchmarks measure how well an LLM does at generating understandable and transparent reasoning about the results of its work, increasing trust and visibility.
Table 11. Explainability evaluation benchmarks
Sustainability Benchmarks
Sustainability assessments evaluate the environmental impact of LLM training and delivery, and encourage environmentally friendly practices and resource efficiency.
Table 12. Benchmarks for assessing eco-sustainability
Social Impact Benchmarks
Social impact benchmarks cover a wide range of issues, including the social and ethical implications of LLM implementation, to ensure that models have a positive impact on society.
Table 13. Benchmarks for assessing the impact on society
This multifaceted approach allows for a thorough examination of the LLM for all possible risks, increasing confidence in the model and its reliability.
Conclusion
The rapid development of large language models (LLMs) has revealed a great need for detailed and reliable benchmarks. Such benchmarks not only help in assessing the capabilities of an LLM, but also allow one to detect potential risks and ethical issues.
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