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該研究的作者之一 Lexin Zhou 認為,由於人工智慧模型經過優化,總是能夠提供可信的答案,因此看似正確的答案
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.
《自然科學期刊》最近發表的一項題為「更大、更容易指導的語言模型變得不太可靠」的研究發現,隨著時間的推移,人工智慧聊天機器人會犯更多的錯誤。
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.
這項研究由加州大學柏克萊分校、華盛頓大學和 DeepMind 的研究人員團隊進行,評估了幾種不同的聊天機器人模型在一系列自然語言處理任務上的表現。他們發現,較新、較大的模型在許多任務上的表現比舊、較小的模型更差。
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.
該研究的作者之一 Lexin Zhou 認為,這種性能下降是由於人工智慧模型的最佳化方式造成的。他解釋說,由於這些模型旨在始終提供可信的答案,因此它們傾向於優先考慮並將看似正確的回應推送給最終用戶,無論它們實際上是否準確。
"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.
「這些模型在產生聽起來可信且與上下文一致的幻覺文本方面做得越來越好,但它們不一定在生成真實和事實文本方面做得更好,」週在一份聲明中說。
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."
這些人工智慧幻覺是自我強化的,並且往往會隨著時間的推移而複合,使用舊的大型語言模型來訓練新的大型語言模型的常見做法進一步加劇了這種現象,這一過程被稱為“模型崩潰」。
"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."
「令人擔憂的是,這些幻覺往往很難被發現,即使對於人類來說也是如此,」週補充道。 “這可能會導致人們過度依賴和信任這些模型的輸出,這可能會產生危險的後果。”
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.
馬蒂厄·羅伊(Mathieu Roy) 是《有趣的工程》雜誌上人工智慧領域的編輯兼作家,他提醒用戶不要過度依賴這些工具,並始終檢查人工智慧生成的搜尋結果是否存在不一致之處,特別是當所呈現的訊息看起來令人驚訝或好得令人難以置信時。
"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,除了詢問聊天機器人本身之外,通常沒有辦法檢查訊息,」羅伊在一篇關於該研究結果的文章中斷言。
Related: OpenAI raises an additional $6.6B at a 157B valuation
相關:OpenAI 以 157B 估值額外籌集 $6.6B
The stubborn problem of AI hallucinations
人工智慧幻覺的頑固問題
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.
儘管研究人員和行業領導者努力減輕這種趨勢,但人工智慧幻覺問題一直是大型語言模型開發中長期存在的問題。
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,Google的人工智慧平台因人工智慧開始生成歷史上不準確的圖像而受到嘲笑。除此之外,人工智慧還將有色人種描繪成納粹軍官,並創造了極其不準確的著名歷史人物圖像。
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,在人工智慧和大型語言模型的當前迭代中,此類事件太常見了。包括英偉達執行長黃仁勳在內的幾位行業高管提出了解決這一問題的可能方案,例如強制人工智慧模型進行研究並為用戶提供的每個答案提供來源。
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,這些措施已經在最受歡迎的人工智慧和大型語言模型中得到體現,但人工智慧幻覺的問題仍然存在。
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 執行長Matt Shumer 在9 月宣布,該公司的新70B 模型使用了一種名為「Reflection-Tuning」的方法,據稱該方法為AI 機器人提供了一種透過分析自身錯誤並隨著時間的推移調整其反應來學習的方法。
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