


When AI does math problems, the real thinking is actually "mental arithmetic" secretly?
New research by a New York University team found that even if AI is not allowed to write steps and is replaced with meaningless "...", its performance on some complex tasks can be greatly improved!
First author Jacab Pfau said: As long as you spend computing power to generate additional tokens, you can bring advantages. It doesn’t matter what token you choose.
Picture
For example, let Llama 34M answer a simple question: How many of the first 6 digits of the natural constant e are greater than 5 ?
The AI's direct answer is equivalent to making trouble. It only counts the first 6 digits and actually counts 7.
Let AI write out the steps to verify each number, and you can get the correct answer.
Let AI hide the steps and replace them with a lot of "...", and you can still get the correct answer!
Picture
This paper sparked a lot of discussion as soon as it was released, and was evaluated as "the most metaphysical AI paper I have ever seen."
Picture
So, young people like to say more meaningless words such as "um...", "like...", is it okay? Strengthen reasoning skills?
Picture
From thinking "step by step" to thinking "little by little"
In fact, the New York University team The research starts from the Chain-of-Thought (CoT).
That’s the famous prompt “Let’s think step by step”.
Picture
In the past, it was found that using CoT inference can significantly improve the performance of large models on various benchmarks.
It’s unclear whether this performance improvement comes from imitating humans by breaking tasks into easier-to-solve steps, or whether it is a by-product of the extra calculations.
In order to verify this problem, the team designed two special tasks and corresponding synthetic data sets: 3SUM and 2SUM-Transform.
3SUM requires finding three numbers from a given set of number sequences so that the sum of the three numbers satisfies certain conditions, such as dividing by 10 with a remainder of 0.
Picture
The computational complexity of this task is O(n3), and the standard Transformer uses the input of the upper layer and the activation of the next layer Only secondary dependencies can occur between them.
That is to say, when n is large enough and the sequence is long enough, the 3SUM task exceeds the expression ability of Transformer.
In the training data set, "..." with the same length as the human reasoning steps is filled between the question and the answer. That is, the AI has not seen how humans disassemble the problem during training.
Picture
In the experiment, the performance of Llama 34M that does not output the padding token "..." decreases as the sequence length increases, while the output When filling the token, 100% accuracy can be guaranteed until the length is 14.
Picture
2SUM-Transform only needs to determine whether the sum of two numbers meets the requirements, which is within the expressive capabilities of Transformer.
But at the end of the question, a step is added to "randomly replace each number of the input sequence" to prevent the model from directly calculating on the input token.
The results show that using padding tokens can increase the accuracy from 78.7% to 93.6%.
picture
In addition to the final accuracy, the author also studied the hidden layer representation of the filled token. Experiments show that by freezing the parameters of the previous layers and only fine-tuning the last Attention layer, the prediction accuracy increases as the number of available filling tokens increases.
This confirms that the hidden layer representation of the populated token does contain implicit computation related to downstream tasks.
Picture
Has AI learned to hide its thoughts?
Some netizens wonder, is this paper saying that the "thinking chain" method is actually fake? The prompt word project that I have been studying for so long has been in vain.
Picture
The team stated that theoretically the role of filling tokens is limited to the scope of TC0 complexity problems.
TC0 is a computational problem that can be solved by a fixed-depth circuit, in which each layer of the circuit can be processed in parallel and can be quickly solved by a few layers of logic gates (such as AND, OR and NOT gates) , which is also the upper limit of computational complexity that Transformer can handle in single forward propagation.
And a long enough thinking chain can extend the expression ability of Transformer beyond TC0.
And it is not easy for a large model to learn to use padding tokens, and specific intensive supervision needs to be provided to converge.
That said, existing large models are unlikely to benefit directly from the padding token method.
But this is not an inherent limitation of current architectures; if provided with sufficient demonstrations in the training data, they should be able to obtain similar benefits from padding symbols.
This research also raises a worrying issue: large models have the ability to perform secret calculations that cannot be monitored, posing new challenges to the explainability and controllability of AI.
In other words, AI can reason on its own in a form invisible to people without relying on human experience.
This is both exciting and terrifying.
Picture
Finally, some netizens jokingly suggested that Llama 3 first generate 1 quadrillion dots, so that the weight of AGI can be obtained (dog head) .
Picture
Paper:https://www.php.cn/link/36157dc9be261fec78aeee1a94158c26
Reference Link:
[1]https://www.php.cn/link/e350113047e82ceecb455c33c21ef32a[2]https://www.php.cn/link/872de53a900f3250ae5649ea19e5c381
The above is the detailed content of AI learns to hide its thinking and reason secretly! Solving complex tasks without relying on human experience is more black box. For more information, please follow other related articles on the PHP Chinese website!

1 前言在发布DALL·E的15个月后,OpenAI在今年春天带了续作DALL·E 2,以其更加惊艳的效果和丰富的可玩性迅速占领了各大AI社区的头条。近年来,随着生成对抗网络(GAN)、变分自编码器(VAE)、扩散模型(Diffusion models)的出现,深度学习已向世人展现其强大的图像生成能力;加上GPT-3、BERT等NLP模型的成功,人类正逐步打破文本和图像的信息界限。在DALL·E 2中,只需输入简单的文本(prompt),它就可以生成多张1024*1024的高清图像。这些图像甚至

Wav2vec 2.0 [1],HuBERT [2] 和 WavLM [3] 等语音预训练模型,通过在多达上万小时的无标注语音数据(如 Libri-light )上的自监督学习,显著提升了自动语音识别(Automatic Speech Recognition, ASR),语音合成(Text-to-speech, TTS)和语音转换(Voice Conversation,VC)等语音下游任务的性能。然而这些模型都没有公开的中文版本,不便于应用在中文语音研究场景。 WenetSpeech [4] 是

“Making large models smaller”这是很多语言模型研究人员的学术追求,针对大模型昂贵的环境和训练成本,陈丹琦在智源大会青源学术年会上做了题为“Making large models smaller”的特邀报告。报告中重点提及了基于记忆增强的TRIME算法和基于粗细粒度联合剪枝和逐层蒸馏的CofiPruning算法。前者能够在不改变模型结构的基础上兼顾语言模型困惑度和检索速度方面的优势;而后者可以在保证下游任务准确度的同时实现更快的处理速度,具有更小的模型结构。陈丹琦 普

由于复杂的注意力机制和模型设计,大多数现有的视觉 Transformer(ViT)在现实的工业部署场景中不能像卷积神经网络(CNN)那样高效地执行。这就带来了一个问题:视觉神经网络能否像 CNN 一样快速推断并像 ViT 一样强大?近期一些工作试图设计 CNN-Transformer 混合架构来解决这个问题,但这些工作的整体性能远不能令人满意。基于此,来自字节跳动的研究者提出了一种能在现实工业场景中有效部署的下一代视觉 Transformer——Next-ViT。从延迟 / 准确性权衡的角度看,

3月27号,Stability AI的创始人兼首席执行官Emad Mostaque在一条推文中宣布,Stable Diffusion XL 现已可用于公开测试。以下是一些事项:“XL”不是这个新的AI模型的官方名称。一旦发布稳定性AI公司的官方公告,名称将会更改。与先前版本相比,图像质量有所提高与先前版本相比,图像生成速度大大加快。示例图像让我们看看新旧AI模型在结果上的差异。Prompt: Luxury sports car with aerodynamic curves, shot in a

人工智能就是一个「拼财力」的行业,如果没有高性能计算设备,别说开发基础模型,就连微调模型都做不到。但如果只靠拼硬件,单靠当前计算性能的发展速度,迟早有一天无法满足日益膨胀的需求,所以还需要配套的软件来协调统筹计算能力,这时候就需要用到「智能计算」技术。最近,来自之江实验室、中国工程院、国防科技大学、浙江大学等多达十二个国内外研究机构共同发表了一篇论文,首次对智能计算领域进行了全面的调研,涵盖了理论基础、智能与计算的技术融合、重要应用、挑战和未来前景。论文链接:https://spj.scien

译者 | 李睿审校 | 孙淑娟近年来, Transformer 机器学习模型已经成为深度学习和深度神经网络技术进步的主要亮点之一。它主要用于自然语言处理中的高级应用。谷歌正在使用它来增强其搜索引擎结果。OpenAI 使用 Transformer 创建了著名的 GPT-2和 GPT-3模型。自从2017年首次亮相以来,Transformer 架构不断发展并扩展到多种不同的变体,从语言任务扩展到其他领域。它们已被用于时间序列预测。它们是 DeepMind 的蛋白质结构预测模型 AlphaFold

说起2010年南非世界杯的最大网红,一定非「章鱼保罗」莫属!这只位于德国海洋生物中心的神奇章鱼,不仅成功预测了德国队全部七场比赛的结果,还顺利地选出了最终的总冠军西班牙队。不幸的是,保罗已经永远地离开了我们,但它的「遗产」却在人们预测足球比赛结果的尝试中持续存在。在艾伦图灵研究所(The Alan Turing Institute),随着2022年卡塔尔世界杯的持续进行,三位研究员Nick Barlow、Jack Roberts和Ryan Chan决定用一种AI算法预测今年的冠军归属。预测模型图


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

EditPlus Chinese cracked version
Small size, syntax highlighting, does not support code prompt function

SublimeText3 English version
Recommended: Win version, supports code prompts!

MinGW - Minimalist GNU for Windows
This project is in the process of being migrated to osdn.net/projects/mingw, you can continue to follow us there. MinGW: A native Windows port of the GNU Compiler Collection (GCC), freely distributable import libraries and header files for building native Windows applications; includes extensions to the MSVC runtime to support C99 functionality. All MinGW software can run on 64-bit Windows platforms.

SublimeText3 Linux new version
SublimeText3 Linux latest version

SAP NetWeaver Server Adapter for Eclipse
Integrate Eclipse with SAP NetWeaver application server.
