FlagEval

FlagEval (scales) is aLarge Model Reviewsystem and open platform, aiming to establish scientific, fair and open evaluation benchmarks, methods and toolsets.

Background and purpose of the project::

• FlagEval is launched by Wisdom Source Research Institute to assist researchers in evaluating the performance of base models and training algorithms in all aspects.
• It helps researchers understand the performance of models more accurately by providing comprehensive evaluation tools and methods, and explores the use of AI methods to achieve assistance in subjective evaluation and improve the efficiency and objectivity of evaluation.

Review Dimensions and Characteristics::

• FlagEval provides a comprehensive assessment of a large language model in three dimensions: competencies, tasks, and metrics.
• The "capability" dimension: it covers a variety of application scenarios such as dialog systems, Q&A systems, sentiment analysis, etc., and provides a number of benchmark test datasets.
• "Task" dimension: 22 datasets and over 80,000 assessment questions are provided, covering different application scenarios, difficulty levels and language types.
• "Indicator" dimension: A number of evaluation indicators are provided, including natural language generation, semantic matching, sentiment analysis, etc., and a reasonable reference range is set.
• FlagEval employs a large amount of data and technical means to ensure the scientific and fairness of the model reviews and to reduce the influence of subjective reviews.

Scope and Scenarios::

• FlagEval has now launched tools such as Language Big Model Review, Multilingual Text Map Big Model Review and Text Map Generation Review.
• It implements reviews on various language base models, cross-modal base models, and plans to comprehensively cover the three major review objects such as base models, pre-training algorithms, and fine-tuning algorithms.
• The evaluation scenarios include four major areas: Natural Language Processing (NLP), Computer Vision (CV), Audio and Multimodal.

Latest Review Results::

• For example, the "Wudao - Skyhawk" AquilaChat-7B dialog model is temporarily ahead of other open-source dialog models of the same parameter class in the FlagEval review list.
• AquilaChat achieves optimal performance with a volume of training data roughly equivalent to 50% for the other models, but its performance is expected to improve further with subsequent training.

future outlook::

• FlagEval and other large model evaluation systems will be continuously improved and optimized to provide solid support for the further application and development of AI technology.
• With the rapidly evolving field of big models, FlagEval will continue to explore new review methods and tools to meet changing needs.

In conclusion, FlagEval, as a comprehensive, scientific and fair big model evaluation system and open platform, plays an important role in promoting the development and application of AI technology.