# Benchmark Testing

Humanity's Last Exam is a multimodal benchmark test collaboratively developed by global experts to evaluate the performance of large language models in academic settings. It includes 3,000 questions contributed by nearly 1,000 experts from over 500 institutions across 50 countries, covering more than 100 disciplines. This test aims to serve as the ultimate closed-form academic benchmark, pushing the limits of models to advance AI technology. Its main advantage is its high difficulty, effectively assessing model performance on complex academic questions.

FACTS Grounding is a comprehensive benchmark test launched by Google DeepMind, designed to evaluate whether the responses generated by large language models (LLMs) are factually accurate not only concerning the given input but also sufficiently detailed to provide satisfactory answers for users. This benchmark is crucial for enhancing the trustworthiness and accuracy of LLMs in real-world applications, facilitating industry-wide advancements in factual reliability and foundational integrity.

FrontierMath is a mathematical benchmarking platform designed to test the limits of artificial intelligence in solving complex mathematical problems. Created by over 60 mathematicians, it spans the full spectrum of modern mathematics, from algebraic geometry to Zermelo-Fraenkel set theory. Each problem on FrontierMath requires expert mathematicians to invest hours of work, and even state-of-the-art AI systems like GPT-4 and Gemini can solve less than 2% of the problems. This platform provides a genuine assessment environment, with all problems being novel and unpublished, eliminating the common data contamination issues found in existing benchmarks.

MLE-bench is a benchmark test launched by OpenAI to measure the performance of AI agents in the domain of machine learning engineering. It compiles 75 diverse challenges from Kaggle-related machine learning engineering competitions, testing real-world skills such as model training, dataset preparation, and experiment execution. Using publicly available leaderboard data from Kaggle, human benchmarks for each competition are established. Various cutting-edge language models are evaluated against this benchmark using open-source agent frameworks, revealing that the best-performing setup—OpenAI's o1-preview paired with the AIDE framework—achieved at least Kaggle bronze medal levels in 16.9% of the competitions. Moreover, the study examines various resource extension forms of AI agents and the effects of pre-training contamination. The benchmark code for MLE-bench has been open-sourced to facilitate future understanding of AI agents' capabilities in machine learning engineering.

LLaVA-Video is a large multimodal model (LMM) focused on video instruction tuning, addressing the challenge of acquiring high-quality raw data from the internet by creating a high-quality synthetic dataset, LLaVA-Video-178K. This dataset includes detailed video descriptions, open-ended questions, and multiple-choice questions, aimed at enhancing the understanding and reasoning capabilities of video language models. The LLaVA-Video model has demonstrated outstanding performance across various video benchmarks, validating the effectiveness of its dataset.

TAG-Bench is a benchmark for evaluating and researching the performance of natural language processing models in answering database queries. It is built on the BIRD Text2SQL benchmark, enhancing query complexity by incorporating semantic reasoning that leans on world knowledge or goes beyond the explicit information in the database. TAG-Bench aims to foster the integration of AI and database technologies by simulating realistic database query scenarios, providing researchers with a platform to challenge existing models.

The LMSYS Chatbot Arena is an online platform aimed at conducting benchmark tests on large language models (LLMs) through interactions with anonymous chatbot models. The platform has accumulated over 700,000 human votes, calculating LLMs' Elo rankings to determine the champions in the chatbot field. The platform offers a research preview with limited security measures, which may generate inappropriate content, thus requiring users to adhere to specific terms of use.

The LMSys Chatbot Arena Leaderboard is a crowdsourced open platform for evaluating the performance of Large Language Models (LLMs). It utilizes the Elo ranking system to rank LLMs based on the results of over 300,000 user votes. Users can interact with different LLMs on the website and vote on their conversational quality. This leaderboard can be used to track the development trends of different LLMs and provide benchmark testing tools for researchers and developers.