On December 5th, the Byte Bean Bag model team launched the latest code model evaluation benchmark - FullStack Bench, covering over 11 real-world scenarios, supporting 16 programming languages, and including 3374 questions. Compared to previous evaluation standards, this benchmark can more accurately assess the code development capabilities of large models across a broader programming domain, facilitating the optimization of models in real-world programming tasks. Current mainstream code evaluation benchmarks, such as HumanEval and MBPP, typically focus on basic and advanced.

In the field of natural language processing, the development of large language models (LLMs) has progressed rapidly and achieved significant advancements across various domains. However, as the complexity of these models increases, accurately evaluating their outputs becomes crucial. Traditionally, we have relied on human evaluations, but this method is both time-consuming and difficult to scale, struggling to keep pace with the rapid advancements of models. To change this situation, the Salesforce AI research team has introduced SFR-Judge, which consists of three large language models.

The Sinan OpenCompass team at the Shanghai Artificial Intelligence Laboratory has collaborated with the Modao ModelScope to launch the Compass Multi-Modal Arena, a new section of a large model evaluation platform focusing on multi-modal large models. Users can upload images and input questions to have two anonymous multi-modal large models generate answers, which can then be subjectively evaluated based on the quality of the generated content, allowing users to select the better-performing model. The platform offers an easy-to-use interface and a unique question bank.

In the field of natural language processing, large language models perform exceptionally well on complex tasks, but model evaluation heavily relies on expensive and time-consuming human-annotated data. As models advance, the utility of existing data declines, necessitating the continuous collection of new data for scalable and sustainable evaluation. The Meta FAIR research team has proposed the 'Self-Taught Evaluator' to address this issue. This innovative approach trains on synthetic data, eliminating the need for human annotation by generating contrasting synthetic preferences.

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Amazon AWS has launched a Human Benchmark Testing team to enhance AI model evaluation. Amazon aims to help users better assess AI models and encourages more people to participate in this process. AWS provides model evaluation on Bedrock to assess models in its repository. Model evaluation consists of both automated and human assessments, which can evaluate model performance based on different metrics. AWS also offers a human evaluation team to collaborate with users and detect metrics that automated systems cannot.

["📊 Evaluation System of Large Models: The current evaluation system for large models has issues such as open-source datasets that can be manipulated, fairness problems arising from closed evaluation datasets, and evaluation metrics that are not sufficiently scientific and comprehensive.", "💡 Trend of Large Model Applications: The article mentions that large models have evolved from model-level development to innovation at the application level.", "🔎 Commercialization Issues of Large Models: For large model teams, achieving commercialization is far more important than rankings and parameters." ]

Ant Group, in collaboration with Peking University, has released a benchmark for evaluating large language models in the DevOps field. This benchmark includes a total of 4850 multiple-choice questions across 8 categories such as planning, coding, building, testing, and releasing. The benchmark also provides detailed evaluations for AIOps tasks, showing that the score differences among various models are minimal.

Parameter scale is not the only criterion for assessing large models. Differences in evaluation sets can lead to significant ranking variations. An increase in subjective question proportions can also affect rankings, raising questions about evaluation fairness. Third-party assessment organizations such as OpenCompass and FlagEval are gaining attention. The academic community believes that model robustness, safety, and other dimensions should also be considered. A truly comprehensive and effective evaluation method is still being explored.

SuperCLUE has released the August rankings for Chinese large models, featuring 5 different ranking evaluations that selected 16 general large language models, utilizing 3,337 new test questions. The performance gap between domestic large models on Chinese tasks and GPT-3.5 is narrowing.

Arthur has launched the open source tool ArthurBench for evaluating and comparing the performance of large language models. ArthurBench helps companies test the performance of different language models on specific use cases and provides metrics such as accuracy, readability, and risk mitigation for comparison. Financial services firms, automotive manufacturers, and media platforms have already begun using ArthurBench, accelerating analysis and providing more accurate answers.