AI model evaluation

AutoArena is an automated generative AI evaluation platform focused on evaluating large language models (LLMs), retrieval-augmented generation (RAG) systems, and generative AI applications. It provides trusted assessments through automated head-to-head judgment, helping users find the best version of their system quickly, accurately, and cost-effectively. The platform supports the use of judgment models from different vendors, such as OpenAI, Anthropic, etc., and can also use locally run open source weight judgment models. AutoArena also provides Elo scores and confidence interval calculations to help users convert multiple head-to-head votes into leaderboard rankings. Additionally, AutoArena supports fine-tuning of custom judgment models for more accurate, domain-specific assessments and can be integrated into continuous integration (CI) processes to automate the assessment of generative AI systems.

Cheating LLM Benchmarks is a research project aimed at exploring cheating in automated language model (LLM) benchmarks by building so-called "null models". The project experimentally found that even simple null models can achieve high winning rates on these benchmarks, challenging the validity and reliability of existing benchmarks. This research is important for understanding the limitations of current language models and improving benchmarking methods.

SWE-bench Verified is a manually verified subset of SWE-bench released by OpenAI, designed to more reliably evaluate the ability of AI models to solve real-world software problems. By providing a code base and a description of the problem, it challenges the AI ​​to generate a patch that solves the described problem. This tool was developed to improve the accuracy of assessments of a model's ability to autonomously complete software engineering tasks and is a key component of the medium risk level in the OpenAI Readiness Framework.

Turtle Benchmark is a new, uncheatable benchmark based on the 'Turtle Soup' game that focuses on evaluating the logical reasoning and context understanding capabilities of large language models (LLMs). It provides objective and unbiased testing results by eliminating the need for background knowledge, with quantifiable results, and by using real user-generated questions so that the model cannot be 'gamified'.

llm-colosseum is an innovative benchmarking tool that uses the Street Fighter 3 game to evaluate the real-time decision-making capabilities of large language models (LLMs). Unlike traditional benchmarks, this tool tests a model's quick reactions, smart strategies, innovative thinking, adaptability, and resilience by simulating actual game scenarios.

Prometheus-Eval is an open source toolset for evaluating the performance of large language models (LLMs) in generation tasks. It provides a simple interface to evaluate command and response pairs using Prometheus models. The Prometheus 2 model supports direct evaluation (absolute scoring) and pairwise ranking (relative scoring), can simulate human judgment and proprietary language model-based evaluation, and solves the issues of fairness, controllability and affordability.

Deepmark AI is a benchmark tool for evaluating large language models (LLMs) on a variety of task-specific metrics on their own data. It comes pre-integrated with leading generative AI APIs such as GPT-4, Anthropic, GPT-3.5 Turbo, Cohere, AI21, and more.

DeepEval provides different aspects of metrics to evaluate LLM's answers to questions to ensure that the answers are relevant, consistent, unbiased, and non-toxic. These integrate well with CI/CD pipelines, allowing machine learning engineers to quickly evaluate and check whether the LLM application is performing well as they improve it. DeepEval provides a Python-friendly offline evaluation method to ensure your pipeline is ready for production. It's like "Pytest for your pipelines", making the process of producing and evaluating your pipelines as simple and straightforward as passing all your tests.