Kunlun Wanwei's Self-Developed Large Model Surpasses GPT-3.5 and LLaMA2 in Inference Capability

Kunlun Tech has announced that its self-developed TianGong large model achieved an accuracy rate of 80% on the Benchmark GSM8K test, surpassing both GPT-3.5 and LLaMA2-70B, reaching a global leading level, close to that of GPT-4. The TianGong large model also performed exceptionally well on multiple datasets including MMLU, C-EVAL, and HumanEval, with accuracy rates higher than those of other mainstream large models. The reasoning capability of the TianGong large model exceeds that of GPT-3.5 and LLaMA2-70B, and compared to GPT-3.5, the problem-solving approach of the TianGong large model is simpler and clearer. Currently in the internal testing phase, the TianGong large model will continue to enhance its technical prowess, providing robust intelligent support for users and businesses.

Effortless Tuning! Microsoft Launches Serverless Fine-Tuning for Phi-3 Small Language Model

Microsoft has announced the launch of serverless fine-tuning capabilities for the Phi-3 small language model, enabling developers to easily adjust model performance on the Azure AI platform without managing underlying infrastructure, and it is free for initial use. The Phi-3 model, with 3 billion parameters, is suitable for enterprise developers, offering efficient performance at a lower cost. Although it has fewer parameters compared to OpenAI's GPT-3.5, it performs similarly across various applications. Serverless fine-tuning simplifies operations, reduces barriers, and supports the fine-tuning of small to medium models.

Google Successfully Attacks OpenAI GPT-3.5-turbo Model at a Cost as Low as 150 Yuan

Google successfully attacked the OpenAI GPT-3.5-turbo model at a cost as low as 150 Yuan. The attack method was simple; by making fewer than 2000 queries through the API, the entire projection matrix and hidden dimensions were obtained. This attack reveals that even large language models may face security threats. OpenAI has modified the model API to prevent similar attacks from happening again.

GPT-3.5 Model Output Up to 60% Suspected of Plagiarism

Plagiarism detection company Copyleaks released a report stating that 60% of the output generated by the GPT-3.5 model contains some form of plagiarized content. Copyleaks uses a proprietary scoring method that considers factors such as text similarity, minor alterations, and paraphrasing to calculate a 'similarity score.' The highest similarity scores were found in fields like computer science, physics, and psychology, while the lowest were in drama, humanities, and English. OpenAI has stated that it has taken measures to limit inadvertent memory usage, and its terms also prohibit deliberate reverse-engineering of content. Aside from the New York Times lawsuit, creators are also attempting to assert rights regarding AI training.

Major Shock in the AI Circle! Microsoft Paper Exposes GPT-3.5 Parameter Count at Only 20 Billion

["Microsoft paper reveals that the parameter count of GPT-3.5 is only 20B, a huge gap from the official claims.","CodeFusion's small model performs extraordinarily well with just 75 million parameters.","Amid controversy, deep reflections on model scale and performance.","The competition in the AI field is fierce, with ChatGPT and ClaudeAI vying for supremacy.","GPT-4 reviewer Terence Tao uses GPT-4 to solve mathematical problems."]

Kunlun Wanwei's Self-Developed Large Model Surpasses GPT-3.5 and LLaMA2 in Inference Capability

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Effortless Tuning! Microsoft Launches Serverless Fine-Tuning for Phi-3 Small Language Model

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Report Shows: 60% of GPT-3.5 Outputs Exhibit Plagiarism

GPT-3.5 Model Output Up to 60% Suspected of Plagiarism

Major Shock in the AI Circle! Microsoft Paper Exposes GPT-3.5 Parameter Count at Only 20 Billion