The OpenCompass team from Shanghai Artificial Intelligence Laboratory and ModelScope have jointly launched an upgrade for the large model evaluation platform CompassArena. This upgrade aims to provide users with a more scientific and comprehensive model evaluation experience. Since its launch, the platform has attracted a large number of community users to participate and contribute data. Based on this data, CompassArena continues to optimize, and this upgrade includes the new Judge Copilot feature and improvements to the ranking algorithm.

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.

The rapid development of generative AI has raised the challenge of comprehensively evaluating its performance. A plethora of models have emerged, each showcasing increasingly impressive results. However, the pressing question remains: how do we assess the effectiveness of these text-to-image models? Traditional evaluation methods often rely on subjective visual assessments or simple metrics like CLIPScore, which frequently fail to capture the intricate details found in complex text prompts, such as relationships between objects and logical reasoning. This leads to inaccurate evaluation outcomes for many text-to-image models.

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.

On September 25, Baidu Wenxin Kuai Ma topped the rankings of two major evaluation organizations, Sullivan and SuperCLUE. The '2024 AI Code Generation Market Observation Report - Milestone' published by Sullivan in collaboration with the TOPO Research Institute shows that Baidu Wenxin Kuai Ma ranks first across eight important dimensions, including technological foundation, application implementation, and product maturity, making it the product with the most first-place awards among all participating vendors. On the same day, the domestic evaluation organization SuperCLUE released the 'Chinese Native Code Assistant Evaluation Benchmark List', in which Baidu Wenxin Kuai Ma also achieved high marks.

Hugging Face recently released LightEval, a lightweight AI evaluation tool designed to assist enterprises and research institutions in more accurately assessing large language models. This tool emphasizes the importance of evaluation in the application of AI technologies, helping to ensure the accuracy, impartiality, and alignment of model outputs with business objectives. Addressing the limitations of traditional evaluation methods, LightEval offers customized evaluation solutions and integrates with other tools from Hugging Face, such as Datat.

On September 4, 2024, the Beijing ZhiYuan Artificial Intelligence Research Institute (BAAI) announced the launch of the world's first model combat evaluation service featuring text-to-video, called FlagEval Large Model Arena. This service is open to users and covers approximately 40 large models from both domestic and international sources, supporting customizable online or offline evaluations for four major tasks: language Q&A, multimodal image and text understanding, text-to-image, and text-to-video.

Recently, DingTalk announced the launch of several new products that integrate artificial intelligence (AI) capabilities, aimed at helping small and medium-sized enterprises use AI technology at a lower cost and in a more efficient way. Among them, the 'Super Work Order Assistant' powered by AI technology in DingTalk's multidimensional table is particularly noteworthy.

According to the evaluation results from the China Academy of Information and Communications Technology, Baidu Smart Cloud's Keway intelligent customer service product received the highest rating in the first batch of 'Intelligent Customer Service Based on Large Models,' marking authoritative recognition of Baidu's technical strength and application practices in this field. Intelligent customer service, as an important application of artificial intelligence, holds significant importance for industries such as finance, government, and telecommunications, helping institutions achieve cost reduction and efficiency enhancement. Keway, as the country's first intelligent customer service product based on large models, offers advantages such as comprehensive functionality, superior performance, and mature systems, efficiently solving complex problems and enhancing problem-solving autonomy.

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.

Baidu Intelligent Cloud introduced the financial intelligence application "ZhiJin" at the 2024 China International Financial Expo, aiming to deeply integrate its leading AI large-model technology with core business scenarios in the financial industry to enhance operational efficiency and revenue for financial institutions.ZhiJin is tailored for four core scenarios: wealth management, asset evaluation, business compliance, and over-the-counter (OTC) trading, offering intelligent and efficient bus

According to Bloomberg, OpenAI has created an internal scale to track the progress of its large language models in the field of Artificial General Intelligence (AGI). This move not only demonstrates OpenAI's ambition in the AGI field but also provides the industry with a new standard for measuring AI development. The scale is divided into five levels: 1. Level 1: Current chatbots, such as ChatGPT, fall into this category. 2. Level 2: Systems capable of solving basic problems at the level of a Ph

Anthropic announced on Monday the launch of a new initiative aimed at funding the development of new benchmarks capable of assessing the performance and impact of artificial intelligence models, including its own generative model, Claude. According to information released in Anthropic's official blog, the company will provide financial support to third-party organizations to develop tools that "effectively measure the advanced capabilities of artificial intelligence models." Interested organizat

Hugging Face has updated its Open LLM Leaderboard, a move that will have a significant impact on the landscape of open-source AI development. This improvement comes at a crucial moment in AI development as researchers and companies are facing a plateau in the performance enhancement of large language models (LLMs).

Anthropic's latest model, Claude 3.5 Sonnet, demonstrated remarkable performance in a recent technical evaluation, surpassing even the professional doctorate level. In the Graduate-Level Question Answering (GPQA) test, Claude 3.5 Sonnet achieved a score of 67.2%, marking the first time a large language model has exceeded 65% in such assessments, and indicating its proficiency in understanding and answering advanced scientific knowledge.