What are LLM Leaderboards

Large Language Model (LLM) leaderboards have taken a prominent position among LLM developers, for model ranking, to compare the capabilities of LLMs. They provide standardized methods for evaluating and comparing the performance of various language models across different tasks. These tasks include text generation, translation, summarization, question answering, and more. By leveraging detailed methods and large databases, LLM leaderboards help identify the strengths and weaknesses of models, guiding researchers and practitioners.

LLM Leaderboards

LMSYS Chatbot Arena Leaderboard

The LMSYS Chatbot Arena Leaderboard is renowned among AI professionals for its comprehensive evaluation system. It combines human preference votes with the Elo ranking method to assess language models. This platform incorporates benchmarks like MT-Bench and MMLU, allowing users to rank models through interactions with custom prompts. While praised for its openness and fairness, the reliance on human judgment can introduce biases, potentially skewing results towards models that provide agreeable rather than accurate responses.

Trustbit LLM Benchmark

The Trustbit LLM Benchmark is a simple to read, valuable resource for those involved in digital product development. It offers detailed monthly evaluations of LLMs based on real benchmark data from software products. Trustbit assesses models in categories such as document processing, CRM integration, marketing support, cost, speed and code generation. This benchmark is particularly useful for high-level comparison of the best known models.

EQ-Bench: Emotional Intelligence

EQ-Bench, developed by Samuel J. Paech, evaluates the emotional intelligence of language models. It focuses on the ability of models to understand complex emotions and social interactions through 171 English-language questions. The latest version, EQ-Bench v2, features an improved scoring system to better differentiate model performances. While it excels in initial emotional ingelligence assessments, it may not be suited for all evaluations.

Berkeley Function-Calling Leaderboard

The Berkeley Function-Calling Leaderboard focuses on the function-calling capabilities of LLMs. It evaluates models on their ability to process function calls, analyze syntax trees, and execute functions accurately across various scenarios.

ScaleAI Leaderboad

The leaderboards from ScaleAI feature proprietary, private datasets and expert-led evaluations, aiming for unbiased and uncontaminated results in a dynamic, contest-like environment.

OpenCompass: CompassRank

OpenCompass 2.0 is a versatile benchmarking platform, including leaderboards. They evaluate LLMs across multiple domains using both open-source and proprietary benchmarks. The platform's core components include CompassKit for evaluation tools, CompassHub for benchmark repositories, and CompassRank for leaderboard rankings.

HuggingFace Open LLM Leaderboard

The Hugging Face Open LLM Leaderboard is a well-regarded platform that evaluates and ranks open large language models and chatbots. Using the Eleuther AI LM Evaluation Harness, it assesses models on knowledge, reasoning, and problem-solving capabilities. Although the leaderboard faced issues with merged models manipulating the rankings, recent updates have improved fairness by filtering out such models by default.

CanAiCode Leaderboard

The CanAiCode leaderboard is part of the CanAiCode test suite, designed to test small text-to-code LLMs. It assesses how well models can turn text inputs into code, providing visibility and comparison for various coding models. While it helps identify top performers, its relevance for real-world coding applications can sometimes be limited.

Open Multilingual LLM Evaluation Leaderboard

This leaderboard evaluates LLMs in 29 languages, expanding the benefits of LLMs globally. It assesses models across benchmarks like AI2 Reasoning Challenge and TruthfulQA, ensuring comprehensive language inclusivity. This platform supports both multilingual and single-language LLMs, making it a critical resource for global LLM development.

Massive Text Embedding Benchmark (MTEB) Leaderboard

The MTEB Leaderboard benchmarks text embedding models across 56 datasets and 8 tasks, supporting up to 112 languages. It evaluates models based on classification, accuracy, F1 scores, and other metrics. This leaderboard is essential for selecting effective embedding models for various tasks, highlighting the need for task-specific evaluations.

AlpacaEval Leaderboard

The AlpacaEval Leaderboard provides a quick evaluation of instruction-following language models. It ranks models based on their win rates against GPT-4-based reference responses. While useful for initial assessments, users are advised to supplement results with additional metrics and tests tailored to specific real-world tasks.

Uncensored General Intelligence Leaderboard (UGI)

The UGI Leaderboard ranks models based on their handling of sensitive or uncensored topics. It maintains evaluation question confidentiality to prevent bias, providing an honest comparison of models. This leaderboard is crucial for those seeking models adept at managing controversial content.

Conclusion

LLM leaderboards are invaluable for measuring and comparing the effectiveness of language models. They foster competition, aid in model development, and set standards for evaluating diverse tasks. However, challenges such as biases, data contamination, and evaluation method inadequacies persist. As the field evolves, we will surely see continuous updates and refinements to these leaderboards.

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