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      考研英语水平的进步，不仅要记单词，还需要阅读外语文献等资料。接下来，小编为2024考研者们，整理出——2024考研英语同源外刊8月:ChatGPT，供考生参考。

2024考研英语同源外刊8月:ChatGPT

When OpenAI released its latest text-generating artificial intelligence, the large language model GPT-4. in March, it was very good at identifying prime numbers. When the AI was given a series of 500 such numbers and asked whether they were primes, it correctly labeled them 97.6 percent of the time.

今年3月，OpenAI发布了较新的文本生成人工智能模型——大型语言模型GPT-4.它非常擅长识别质数。当给人工智能提供500个这样的数字，并询问它们是否为质数时，它正确把数字列为质数的几率为97.6%。

But a few months later, in June, the same test yielded very different results. GPT-4 only correctly labeled 2.4 percent of the prime numbers AI researchers prompted it with—a complete reversal in apparent accuracy. The finding underscores the complexity of large artificial intelligence models: instead of AI uniformly improving at every task on a straight trajectory, the reality is much more like a winding road full of speed bumps and detours.

但几个月后，在今年6月，同样的测试却产生了截然不同的结果。GPT-4在人工智能研究人员提供的质数中，正确标记率只有2.4%，这明显错得离谱。这个发现强调了大型人工智能模型的复杂性：人工智能并不是在一条笔直的轨道上对每项任务都有一致的进步，现实情况更像是在一条充满减速带和绕路的曲折道路。

What is already clear is that GPT-4’s behavior is different now than it was when it was first released. Even OpenAI has acknowledged that, when it comes to GPT-4. “while the majority of metrics have improved, there may be some tasks where the performance gets worse,” as employees of the company wrote in a July 20 update to a post on OpenAi’s blog. Past studies of other models have also shown this sort of behavioral shift, or “model drift,” over time. That alone could be a big problem for developers and researchers who’ve come to rely on this AI in their own work.

现在已经很清楚的是，GPT-4的行为与起初发布时不同。过去对其他模型的研究也表明，其他模型随着时间的推移也出现了这种行为变化，或称“模型漂移”。对于已经在工作中依赖这种人工智能的开发人员和研究人员来说，这可能是一个大问题。

So what is causing the AI to change over time? Without human intervention, these models are static. Companies such as OpenAI are constantly seeking to make programs the best they can be (by certain metrics)—but attempted improvements can have unintended consequences.

那么，是什么导致人工智能随着时间的推移而改变呢?没有人为干预，这些模型是静态的。像OpenAI这样的公司一直在努力让这个程序精益求精(在某些指标的表现上)，但尝试改进可能会产生意想不到的后果。

There are two main factors that determine an AI’s capability and behavior: the many parameters that define a model and the training data that go into refining it. A large language model such as GPT-4 might contain hundreds of billions of parameters meant to guide it. Unlike in a traditional computer program, where each line of code serves a clear purpose, developers of generative AI models often cannot draw an exact one-to-one relationship between a single parameter and a single corresponding trait. This means that modifying the parameters can have unexpected impacts on the AI’s behavior.

决定人工智能能力和行为的因素主要有两个：定义模型的许多参数和用于细化模型输入的训练数据。像GPT-4这样的大型语言模型可能包含数千亿个用来指导它的参数。在传统的计算机程序中，每一行代码都有一个明确的目的，与之不同的是，生成式人工智能模型的开发者通常无法在一个参数和一个对应特征之间提取出准确的一对一关系。这意味着修改参数可能会对人工智能的行为产生意想不到的影响。

 

单词：

1.yield

/jiːld/

v. 产生(收益、效益等)，产生(结果); 出产(天然产品，农产品，工业产品); 屈服，让步; 放弃，让出; 给(大路上的车辆)让路; (受压)活动，弯曲，折断; 被……替代; 请(某人)讲话; 停止争论

n. 产量; 收益，利润，红利(或股息)率

2.reversal

/rɪˈvɜːsəl/

n.反转，颠倒;(运气)逆转;挫折，失败;(角色的)交换，互换;(对下级法院或权力机构裁决、宣判或法令的)撤销，废弃;(摄)反转

3.underscore

/ˌʌndəˈskɔː/

v. 强调; 在……的下面划线

n. (尤指为强调)下划线; (打字机或电脑键盘上的字符)下划线

4.trajectory

/trəˈdʒɛktərɪ/

n. (物体射向或抛向空中形成的)轨道，轨迹; (事业等的)发展轨迹，起落; (几何)常角轨道，轨线

5. detour

/ˈdiːtʊə/

n. 绕行，迂回

v. (使)绕道，绕行; 绕过，绕……走

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