Comprehensive List of Updated LLM Leaderboards

General Purpose Leaderboards

Open LLM Leaderboard by Hugging Face

The Open LLM Leaderboard maintained by Hugging Face is a premier platform for evaluating and ranking open-source large language models (LLMs). It offers standardized benchmarks that assess models across various datasets and tasks, ensuring fair and reproducible comparisons.

Key Features:

Comprehensive evaluation across diverse datasets and tasks.
Standardized benchmarking setup for reproducibility.
Regular updates to incorporate the latest models.
Community-driven with contributions from numerous developers and researchers.

You can access the Open LLM Leaderboard here: Open LLM Leaderboard

LMSys Leaderboard

The LMSys Leaderboard tracks the performance of various LLMs in a competitive environment. It provides insights into how models like Google's Gemini and OpenAI's offerings stack up against each other in terms of performance metrics and capabilities.

Key Features:

Competitive ranking of top-performing LLMs.
Regular updates reflecting the latest advancements.
Detailed performance metrics for in-depth analysis.

Access the LMSys Leaderboard here: LMSys Leaderboard

OpenCompass 2.0

OpenCompass 2.0 is a versatile benchmarking platform that assesses LLMs across multiple domains, including text generation, translation, and summarization. Its modular architecture allows for customizable benchmarks tailored to specific evaluation needs.

Key Features:

Comprehensive evaluation across various language tasks.
Modular design for customizable benchmarking.
Focus on fairness and robustness in evaluations.

Explore OpenCompass 2.0 here: OpenCompass 2.0

Domain-Specific Leaderboards

FlowerTune LLM Leaderboard

The FlowerTune LLM Leaderboard specializes in the federated learning and fine-tuning of LLMs. It emphasizes secure and privacy-preserving methods for evaluating models in specific domains such as finance, medical, and coding.

Key Features:

Federated learning environment for model fine-tuning.
Domain-specific evaluations tailored to targeted applications.
Comprehensive guidelines to ensure fair competition.

Access the FlowerTune LLM Leaderboard here: FlowerTune LLM Leaderboard

Indico Data LLM Leaderboard

The Indico Data LLM Leaderboard focuses on benchmarking LLMs for document understanding tasks, including data extraction, document classification, and summarization. It is particularly beneficial for enterprise applications involving intelligent document processing.

Key Features:

Evaluation of LLMs on deterministic tasks like data extraction and classification.
Includes specialized datasets such as CORD, CUAD, Kleister NDA, and ContractNLI.
Compares performance between traditional machine learning models and LLMs.
Regular updates every 90 days to reflect the latest model advancements.

Access the Indico Data LLM Leaderboard here: Indico Data LLM Leaderboard

Middle East Telecom LLM Leaderboard

This leaderboard is part of a collaborative project by Huawei Paris Research Centre, Khalifa University, and GSMA to develop a standard evaluation framework for telecom-specific LLMs. It focuses on tasks relevant to the telecommunications industry, such as customer service, network optimization, and knowledge management.

Key Features:

Telecom-specific LLM evaluation criteria.
Focus on query answering, mathematical reasoning, and energy efficiency.
Currently in the testing phase with broader access planned for 2025.

Access the project announcement and updates here: Middle East Telecom LLM Leaderboard

CanAiCode Leaderboard

The CanAiCode Leaderboard evaluates LLMs based on their ability to understand and generate code. It is an essential resource for developers and researchers focusing on AI-driven coding solutions.

Key Features:

Assessment of code understanding and generation capabilities.
Benchmarks across multiple programming languages.
Comparative analysis of different models' performance in coding tasks.

Access the CanAiCode Leaderboard here: CanAiCode Leaderboard

Specialized Performance Leaderboards

Hugging Face LLM-Perf Leaderboard

The LLM-Perf Leaderboard by Hugging Face ranks models based on operational efficiency metrics such as latency, throughput, memory usage, and energy consumption. This leaderboard is designed to evaluate the practical deployment feasibility of LLMs.

Key Features:

Focus on performance metrics like latency and energy efficiency.
Evaluates scalability and resource utilization of models.
Provides insights for real-world deployment considerations.

Access the LLM-Perf Leaderboard here: LLM-Perf Leaderboard

Hugging Face Trust and Safety Leaderboard

The Trust and Safety Leaderboard assesses LLMs based on their trustworthiness and safety. It evaluates models for tendencies to generate false information, exhibit biases, or produce harmful content.

Key Features:

Evaluation of ethical AI and safety standards.
Benchmarks for social stereotyping and security measures.
Alignment with regulatory frameworks like the EU AI Act.

Access the Trust and Safety Leaderboard here: Trust and Safety Leaderboard

Hugging Face Workplace Utility Leaderboard

The Workplace Utility Leaderboard evaluates LLMs based on their practical applications in professional settings. This includes tasks like document summarization, email drafting, and task automation, which are crucial for enhancing workplace productivity.

Key Features:

Focus on real-world workplace applications.
Benchmarks for productivity-enhancing tasks.
Evaluation of models' effectiveness in enterprise environments.

Access the Workplace Utility Leaderboard here: Workplace Utility Leaderboard

Hugging Face Reasoning Leaderboard

The Reasoning Leaderboard focuses on assessing the reasoning and problem-solving capabilities of LLMs. It includes tasks that require logical thinking, creativity, and subject matter expertise.

Key Features:

Evaluation of advanced reasoning and decision-making abilities.
Benchmarks for complex problem-solving tasks.
Assessment across diverse reasoning scenarios.

Access the Reasoning Leaderboard here: Reasoning Leaderboard

Open LLM Leaderboard (Multilingual Evaluation)

This variant of the Open LLM Leaderboard evaluates LLMs based on their multilingual capabilities. It includes tasks such as translation, sentiment analysis, and question answering across multiple languages.

Key Features:

Focus on multilingual performance and versatility.
Benchmarks for diverse language-specific tasks.
Evaluation of models' applicability in global contexts.

Access the Multilingual Open LLM Leaderboard here: Multilingual Open LLM Leaderboard

Benchmarking Frameworks

BigBench (Beyond the Imitation Game Benchmark)

BigBench is a comprehensive benchmark that evaluates LLMs across a wide array of reasoning tasks, including general intelligence, creativity, and logical reasoning. It is designed to push the boundaries of what LLMs can achieve beyond simple pattern recognition.

Key Features:

Diverse set of tasks for thorough evaluation.
Focus on advanced reasoning and creative problem-solving.
Challenging benchmarks to assess the limits of model capabilities.

Access BigBench here: BigBench

ARC (AI2 Reasoning Challenge)

The ARC Benchmark centers on reasoning and applying background knowledge to answer grade-school science questions. It challenges models to synthesize information and exhibit effective reasoning skills in educational contexts.

Key Features:

Focus on reasoning and knowledge application in science.
Benchmarks designed around educational and cognitive tasks.
Assessment of models' ability to handle complex scientific queries.

Access ARC here: AI2 Reasoning Challenge

HELM (Holistic Evaluation of Language Models)

HELM is a holistic evaluation framework that assesses LLMs across multiple dimensions, including accuracy, fairness, robustness, and efficiency. It provides a comprehensive overview of model performance in various real-world scenarios.

Key Features:

Multi-dimensional evaluation covering various performance aspects.
Focus on fairness and robustness in model outputs.
Benchmarks designed for diverse language tasks and applications.

Access HELM here: HELM

Eleuther AI LM Evaluation Harness

The Eleuther AI LM Evaluation Harness is a flexible framework designed for evaluating LLMs on a broad spectrum of tasks, including text generation, summarization, and question answering. Its modular architecture allows for customizable evaluations tailored to specific research needs.

Key Features:

Modular and flexible architecture for diverse evaluations.
Supports a wide range of language tasks and benchmarks.
Focus on open-source model assessments.

Access the Eleuther AI LM Evaluation Harness here: Eleuther AI LM Evaluation Harness

OpenAI Eval

OpenAI Eval is a framework developed by OpenAI for evaluating LLMs on custom tasks and benchmarks. It is designed to be highly adaptable, allowing researchers and developers to tailor evaluations to specific use cases and applications.

Key Features:

Customizable evaluations for specialized tasks.
Focus on real-world application scenarios.
Supports diverse language tasks for comprehensive assessments.

Access OpenAI Eval here: OpenAI Eval

Additional Notable Leaderboards

Hugging Face Chatbot Arena Leaderboard

The Chatbot Arena Leaderboard by Hugging Face evaluates chatbot models based on their ability to engage in natural and coherent conversations. It employs blind tests to rank models according to user preferences, ensuring unbiased assessments.

Key Features:

Focus on conversational AI and user interaction quality.
Blind testing methodology to minimize bias.
Emphasis on real-world applicability and user satisfaction.

Access the Chatbot Arena Leaderboard here: Chatbot Arena Leaderboard

Hugging Face Reasoning Leaderboard

The Reasoning Leaderboard by Hugging Face evaluates LLMs on their reasoning and problem-solving capabilities. It includes tasks that require logical thinking, creativity, and expertise in various subjects to assess advanced cognitive functions.

Key Features:

Assessment of advanced reasoning and decision-making.
Benchmarks covering a wide range of problem-solving tasks.
Evaluation across diverse reasoning scenarios to test model versatility.

Access the Reasoning Leaderboard here: Reasoning Leaderboard

Conclusion

The landscape of large language model (LLM) leaderboards is vast and continuously evolving, with numerous platforms dedicated to evaluating and ranking models based on a variety of metrics and specialized tasks. From general-purpose leaderboards like Hugging Face's Open LLM Leaderboard and LMSys Leaderboard to domain-specific evaluations such as FlowerTune and Indico Data Leaderboards, each platform serves a unique purpose in advancing the capabilities and understanding of LLMs.

Specialized performance leaderboards, including those focusing on trust and safety, reasoning, and operational efficiency, provide deep insights into specific aspects of LLM performance, ensuring that models not only excel in general tasks but also adhere to ethical standards and operational requirements. Benchmarking frameworks like BigBench, ARC, HELM, and the Eleuther AI LM Evaluation Harness offer robust methodologies for comprehensive and adaptable evaluations, fostering innovation and improvement in LLM development.

By leveraging these leaderboards and benchmarking tools, researchers, developers, and industry professionals can stay informed about the latest advancements, make data-driven decisions in selecting appropriate models for their needs, and contribute to the ongoing progress in the field of natural language processing and artificial intelligence.