|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
该研究的作者之一 Lexin Zhou 认为,由于人工智能模型经过优化,总是能够提供可信的答案,因此看似正确的答案
Artificially intelligent chatbots are making more mistakes over time, a recent research study titled "Larger and more instructable language models become less reliable" in the Nature Scientific Journal has found.
《自然科学杂志》最近发表的一项题为“更大、更易指导的语言模型变得不太可靠”的研究发现,随着时间的推移,人工智能聊天机器人会犯更多的错误。
The study, which was conducted by a team of researchers from the University of California, Berkeley, the University of Washington, and DeepMind, evaluated the performance of several different chatbot models on a range of natural language processing tasks. They found that the newer, larger models performed worse on many of the tasks than the older, smaller models.
这项研究由来自加州大学伯克利分校、华盛顿大学和 DeepMind 的研究人员团队进行,评估了几种不同的聊天机器人模型在一系列自然语言处理任务上的性能。他们发现,较新、较大的模型在许多任务上的表现比较旧、较小的模型更差。
One of the study's authors, Lexin Zhou, theorized that this decline in performance is due to the way that AI models are optimized. He explained that because these models are designed to always provide believable answers, they tend to prioritize and push the seemingly correct responses to the end user, regardless of whether or not they are actually accurate.
该研究的作者之一 Lexin Zhou 认为,这种性能下降是由于人工智能模型的优化方式造成的。他解释说,由于这些模型旨在始终提供可信的答案,因此它们倾向于优先考虑并将看似正确的响应推送给最终用户,无论它们实际上是否准确。
"The models are getting better at generating hallucinated text that sounds plausible and consistent with the context, but they are not necessarily getting better at generating true and factual text," Zhou said in a statement.
“这些模型在生成听起来可信且与上下文一致的幻觉文本方面做得越来越好,但它们不一定在生成真实和事实文本方面做得更好,”周在一份声明中说。
These AI hallucinations are self-reinforcing and tend to compound over time, a phenomenon that is further exacerbated by the common practice of using older large language models to train newer large language models, a process known as "model collapse."
这些人工智能幻觉是自我强化的,并且往往会随着时间的推移而复合,使用旧的大型语言模型来训练新的大型语言模型的常见做法进一步加剧了这种现象,这一过程被称为“模型崩溃”。
"The worrying part is that these hallucinations are often difficult to detect, even for humans," Zhou added. "This could lead to people relying on and trusting the output of these models too much, which could have dangerous consequences."
“令人担忧的是,这些幻觉往往很难被发现,即使对于人类来说也是如此,”周补充道。 “这可能会导致人们过度依赖和信任这些模型的输出,这可能会产生危险的后果。”
Mathieu Roy, an editor and writer who covers artificial intelligence for Interesting Engineering, cautioned users not to rely too heavily on these tools and to always check AI-generated search results for inconsistencies, especially if the information being presented seems surprising or too good to be true.
马蒂厄·罗伊 (Mathieu Roy) 是《有趣的工程》杂志上人工智能领域的编辑兼作家,他提醒用户不要过度依赖这些工具,并始终检查人工智能生成的搜索结果是否存在不一致之处,特别是当所呈现的信息看起来令人惊讶或好得令人难以置信时。真的。
"To make matters worse, there’s often no way to check the information except by asking the chatbot itself," Roy asserted in an article about the study's findings.
“更糟糕的是,除了询问聊天机器人本身之外,通常没有办法检查信息,”罗伊在一篇关于该研究结果的文章中断言。
Related: OpenAI raises an additional $6.6B at a 157B valuation
相关:OpenAI 以 157B 估值额外筹集 $6.6B
The stubborn problem of AI hallucinations
人工智能幻觉的顽固问题
The issue of AI hallucinations has been a persistent problem in the development of large language models, despite efforts by researchers and industry leaders to mitigate this tendency.
尽管研究人员和行业领导者努力减轻这种趋势,但人工智能幻觉问题一直是大型语言模型开发中长期存在的问题。
In February 2024, Google's artificial intelligence platform drew ridicule after the AI started producing historically inaccurate images. Among other things, the AI was seen portraying people of color as Nazi officers and creating wildly inaccurate images of well-known historical figures.
2024 年 2 月,谷歌的人工智能平台因人工智能开始生成历史上不准确的图像而受到嘲笑。除此之外,人工智能还将有色人种描绘成纳粹军官,并创建了极其不准确的著名历史人物图像。
Unfortunately, incidents like this are far too common with the current iteration of artificial intelligence and large language models. Several industry executives, including Nvidia CEO Jensen Huang, have proposed possible solutions to this problem, such as forcing AI models to conduct research and provide sources for every single answer that is given to a user.
不幸的是,在人工智能和大型语言模型的当前迭代中,此类事件太常见了。包括英伟达首席执行官黄仁勋在内的几位行业高管提出了解决这一问题的可能方案,例如强制人工智能模型进行研究并为用户提供的每个答案提供来源。
However, these measures are already featured in the most popular AI and large language models, yet the problem of AI hallucinations still persists.
然而,这些措施已经在最流行的人工智能和大型语言模型中得到体现,但人工智能幻觉的问题仍然存在。
More recently, in September, HyperWrite AI CEO Matt Shumer announced that the company's new 70B model uses a method called “Reflection-Tuning” — which purportedly gives the AI bot a way of learning by analyzing its own mistakes and adjusting its responses over time.
最近,HyperWrite AI 首席执行官 Matt Shumer 在 9 月份宣布,该公司的新 70B 模型使用了一种名为“Reflection-Tuning”的方法,据称该方法为 AI 机器人提供了一种通过分析自身错误并随着时间的推移调整其响应来学习的方法。
免责声明:info@kdj.com
所提供的信息并非交易建议。根据本文提供的信息进行的任何投资,kdj.com不承担任何责任。加密货币具有高波动性,强烈建议您深入研究后,谨慎投资!
如您认为本网站上使用的内容侵犯了您的版权,请立即联系我们(info@kdj.com),我们将及时删除。
-
- 以太坊面临压力:仔细观察价格走势
- 2024-10-05 14:30:01
- 以太坊作为市值第二大的加密货币,近期出现了明显的下行压力,反映了加密货币市场的整体情绪。
-
- 停止解释比特币并开始向人们展示它解决的问题
- 2024-10-05 14:30:01
- 比特币不会因为求知欲或理论上是有史以来最好的货币形式而被更广泛地采用。
-
- 了解比特币期权到期
- 2024-10-05 14:30:01
- 期权合约赋予交易者在特定日期之前以预定价格买卖资产的权利,但没有义务。
-
- XRP 值得预测:2000 美元的资金需要多高才能发展到 100 万美元?
- 2024-10-05 14:30:01
- 在过去的几周里,山寨币经历了巨大的波动。与市场其他产品相比,XRP 的价值下跌了近 6%。