【南加州大学研究揭示ChatGPT参数规模新发现】
据量子位报道，南加州大学的研究团队近期取得一项重要突破，他们推测ChatGPT的参数规模可能仅为70亿。这一发现与人们普遍对大型语言模型的预期有所出入。研究团队的三位学者通过对未公开的gpt-3.5-turbo嵌入向量维度的分析，确定其维度为4096或4608。有趣的是，已知的开源大模型，如Llama和Mistral，在相同维度下通常拥有约70亿的参数规模。
研究人员指出，模型的参数规模与性能之间存在微妙的平衡。如果比例失调，过宽或过窄的网络结构都会对模型的性能产生负面影响。基于这一理论，他们认为，除非gpt-3.5-turbo采用了特殊的混合专家架构（MoE），否则其参数规模很可能保持在70亿左右，与4096维嵌入向量的常规模式相符。
这一研究结果为理解大型语言模型的内部机制提供了新的视角，也为未来的模型设计提供了参考。ChatGPT作为人工智能领域的热门话题，其参数规模的精确度一直是业界关注的焦点。南加州大学的这项研究无疑为这个话题增添了新的讨论点，有望推动相关领域的进一步探索和优化。
研究人员强调，虽然目前的推测基于一定的假设，但这一发现对于验证和优化大模型的架构设计具有重要意义。未来，他们将继续深入研究，以期揭示更多关于ChatGPT以及类似模型的内在秘密。
英语如下：
**News Title:** “USC Study Unveils ChatGPT Parameter Size Mystery: Potentially Just 7 Billion, Challenging Norms for Large Models”
**Keywords:** USC study, GPT parameters, model performance
**News Content:**
**New Insights from USC Research on ChatGPT’s Parameter Scale**
According to QbitAI, a research team from the University of Southern California (USC) has recently made a significant breakthrough, suggesting that ChatGPT’s parameter count might be as low as 7 billion. This revelation contrasts with common expectations for large language models. The team’s trio of scholars deduced a dimensionality of 4096 or 4608 for the unpublicized gpt-3.5-turbo embedding vectors, which is consistent with the parameter scale of around 7 billion seen in open-source large models like Llama and Mistral at the same dimension.
Researchers emphasize the delicate balance between a model’s parameter scale and its performance. An imbalance, with networks being either too wide or too narrow, can negatively affect the model’s effectiveness. Based on this principle, they speculate that unless gpt-3.5-turbo employs a unique Mixed-Expert (MoE) architecture, its parameter count is likely to remain around 7 billion, aligning with the standard pattern for a 4096-dimensional embedding vector.
This study offers a fresh perspective on the inner workings of large language models and provides a reference for future model designs. As a hot topic in the AI domain, the accuracy of ChatGPT’s parameter size has been a focal point of industry interest. USC’s research injects new points of discussion into the topic and is poised to stimulate further exploration and optimization in the field.
The researchers stress that while current speculations are based on assumptions, this discovery holds significant implications for validating and optimizing the architecture design of large models. They intend to continue their in-depth research to uncover more of ChatGPT’s and similar models’ hidden intricacies.
【来源】https://mp.weixin.qq.com/s/y0RQ0aOrHGLzLJKxbyGxMw
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