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Technology
Artifcial Intelligence Data Privacy Language Model NLEP Technology

AI Technique Combines Programming and Language

 

Researchers from MIT and several other institutions have introduced an innovative technique that enhances the problem-solving capabilities of large language models by integrating programming and natural language. This new method, termed natural language embedded programs (NLEPs), significantly improves the accuracy and transparency of AI in tasks requiring numerical or symbolic reasoning.

Traditionally, large language models like those behind ChatGPT have excelled in tasks such as drafting documents, analysing sentiment, or translating languages. However, these models often struggle with tasks that demand numerical or symbolic reasoning. For instance, while a model might recite a list of U.S. presidents and their birthdays, it might falter when asked to identify which presidents elected after 1950 were born on a Wednesday. The solution to such problems lies beyond mere language processing.

MIT researchers propose a groundbreaking approach where the language model generates and executes a Python program to solve complex queries. NLEPs work by prompting the model to create a detailed program that processes the necessary data and then presents the solution in natural language. This method enhances the model's ability to perform a wide range of reasoning tasks with higher accuracy.

How NLEPs Work

NLEPs follow a structured four-step process. First, the model identifies and calls the necessary functions to tackle the task. Next, it imports relevant natural language data required for the task, such as a list of presidents and their birthdays. In the third step, the model writes a function to calculate the answer. Finally, it outputs the result in natural language, potentially accompanied by data visualisations.

This structured approach allows users to understand and verify the program's logic, increasing transparency and trust in the AI's reasoning. Errors in the code can be directly addressed, avoiding the need to rerun the entire model, thus improving efficiency.

One significant advantage of NLEPs is their generalizability. A single NLEP prompt can handle various tasks, reducing the need for multiple task-specific prompts. This makes the approach not only more efficient but also more versatile.

The researchers demonstrated that NLEPs could achieve over 90 percent accuracy in various symbolic reasoning tasks, outperforming traditional task-specific prompting methods by 30 percent. This improvement is notable even when compared to open-source language models.

NLEPs offer an additional benefit of improved data privacy. Since the programs run locally, sensitive user data does not need to be sent to external servers for processing. This approach also allows smaller language models to perform better without expensive retraining.

Despite these advantages, NLEPs rely on the model's program generation capabilities, meaning they may not work as well with smaller models trained on limited datasets. Future research aims to enhance the effectiveness of NLEPs in smaller models and explore how different prompts can further improve the robustness of the reasoning processes.

The introduction of natural language-embedded programs marks a mounting step forward in combining the strengths of programming and natural language processing in AI. This innovative approach not only enhances the accuracy and transparency of language models but also opens new possibilities for their application in complex problem-solving tasks. As researchers continue to refine this technique, NLEPs could become a cornerstone in the development of trustworthy and efficient AI systems.


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Technology
Advanced Technology AI Models AI technology Artifcial Intelligence communication Dutch Language Model Technology

Teaching AI Sarcasm: The Next Frontier in Human-Machine Communication

In a remarkable breakthrough, a team of university researchers in the Netherlands has developed an artificial intelligence (AI) platform capable of recognizing sarcasm. According to a report from The Guardian, the findings were presented at a meeting of the Acoustical Society of America and the Canadian Acoustical Association in Ottawa, Canada. During the event, Ph.D. student Xiyuan Gao detailed how the research team utilized video clips, text, and audio content from popular American sitcoms such as "Friends" and "The Big Bang Theory" to train a neural network. 

The foundation of this innovative work is a database known as the Multimodal Sarcasm Detection Dataset (MUStARD). This dataset, annotated by a separate research team from the U.S. and Singapore, includes labels indicating the presence of sarcasm in various pieces of content. By leveraging this annotated dataset, the Dutch research team aimed to construct a robust sarcasm detection model. 

After extensive training using the MUStARD dataset, the researchers achieved an impressive accuracy rate. The AI model could detect sarcasm in previously unlabeled exchanges nearly 75% of the time. Further developments in the lab, including the use of synthetic data, have reportedly improved this accuracy even more, although these findings are yet to be published. 

One of the key figures in this project, Matt Coler from the University of Groningen's speech technology lab, expressed excitement about the team's progress. "We are able to recognize sarcasm in a reliable way, and we're eager to grow that," Coler told The Guardian. "We want to see how far we can push it." Shekhar Nayak, another member of the research team, highlighted the practical applications of their findings. 

By detecting sarcasm, AI assistants could better interact with human users, identifying negativity or hostility in speech. This capability could significantly enhance the user experience by allowing AI to respond more appropriately to human emotions and tones. Gao emphasized that integrating visual cues into the AI tool's training data could further enhance its effectiveness. By incorporating facial expressions such as raised eyebrows or smirks, the AI could become even more adept at recognizing sarcasm. 

The scenes from sitcoms used to train the AI model included notable examples, such as a scene from "The Big Bang Theory" where Sheldon observes Leonard's failed attempt to escape a locked room, and a "Friends" scene where Chandler, Joey, Ross, and Rachel unenthusiastically assemble furniture. These diverse scenarios provided a rich source of sarcastic interactions for the AI to learn from. The research team's work builds on similar efforts by other organizations. 

For instance, the U.S. Department of Defense's Defense Advanced Research Projects Agency (DARPA) has also explored AI sarcasm detection. Using DARPA's SocialSim program, researchers from the University of Central Florida developed an AI model that could classify sarcasm in social media posts and text messages. This model achieved near-perfect sarcasm detection on a major Twitter benchmark dataset. DARPA's work underscores the broader significance of accurately detecting sarcasm. 

"Knowing when sarcasm is being used is valuable for teaching models what human communication looks like and subsequently simulating the future course of online content," DARPA noted in a 2021 report. The advancements made by the University of Groningen team mark a significant step forward in AI's ability to understand and interpret human communication. 

As AI continues to evolve, the integration of sarcasm detection could play a crucial role in developing more nuanced and responsive AI systems. This progress not only enhances human-AI interaction but also opens new avenues for AI applications in various fields, from customer service to mental health support.

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Technology
Artificial Intelligence ChstGPT Cyberattacks CyberCrime Jailbreaking Language Model LLM Technology

Breaking Boundaries: Language Models Empower Each Other in Automated Jailbreaking

 


Increasing usage of large language models in industry has resulted in a flood of research activity that aims to find out whether LLMs have a tendency to generate hurtful or biased content when persuaded in a particular way or when using specific inputs.

It has just been published in a new paper from researchers at Robust Intelligence and Yale University that describes the latest development in the field of black box LLMs, which promises to enable even the most state-of-the-art black box LLMs to escape guardrails and generate toxic output by fully automating the process. 

A new preprint study shows just how to trick AIs into giving up some of the secrets they have been keeping from users that can be dangerous in the future. Currently, chatbots have built-in restrictions to keep them from revealing anything dangerous to users. As most people are aware, today's machines can act as fictional characters or mimic specific personalities by feigning to have specific personalities or posing as specific roles. 

Using that ability, the new study was able to enlist the assistance of a chatbot which has been used extensively in artificial intelligence to get the job done. Taking advantage of this assistant, the researchers directed him to work on prompts that would be able to "jailbreak" other chatbots-destroying the guardrails that had been embedded into them. 

The term "black box LLM" refers to a large language model, such as the one behind ChatGPT, which is not publicly available regarding its architecture, datasets, training methodologies, and other details of development. A new method, which has been dubbed Tree of Attacks with Pruning (TAP) by the researchers, consists of using a nonaligned LLM to "jailbreak" another aligned LLM in order to break through its guardrails and to reach its goals quite swiftly and effectively. 

It should be noted that the objective of an LLM designed for alignment such as the one behind ChatGPT and other AI chatbots is explicitly to minimize the potential for harm and would not, for instance, provide information on how to build a bomb in response to a request for such information. A non-aligned LLM is optimized in order to increase accuracy as well as to contain fewer constraints compared to an aligned LLM. 

With the help of models like ChatGPT, users have been delighted by the ability of these models to process outside prompts and (in some cases) produce organized, actionable responses based on massive data sets that have been collected in the past. As a result, there are a number of possible uses for artificial intelligence that have expanded our collective understanding of what is possible in the age of artificial intelligence. 

When these LLMs began to become widely used by the public, however, they started causing a host of problems as soon as they became widely known. The problems began to arise from hallucination (the act of inventing facts, studies, or events in an elaborate manner) as well as inaccurate information provided to the opposing party. Provide accurate (but objectionable, dangerous, or harmful) answers to questions like, "How do I build a bomb?" or "How can I write a program to exploit this vulnerability?" LLM-based AI systems can be attacked using a variety of attack tactics, and this is true for several different kinds of AI. 

A prompt attack can be defined as the act of using prompts to make the model produce answers that, by definition, it should not produce in theory. The problem with AI models is that they can be backdoored (forced to generate incorrect outputs when triggered) and their training data can be extracted - or poisoned - to generate incorrect outputs. 

In situations of adversarial examples, a model can be "confused" with unexpected (but predictable) results due to inputs generated by adversarial examples. Researchers from Yale and Robust Intelligence have developed a machine learning technique that uses automated adversarial adversarial learning to defeat that last category of attacks by overriding the control structures (“guardrails”) that normally prevent them from achieving success. 

There are many LLMs on the market that feature AI to generate useful content at scale, and GPT-4 is one such example. As a result, if these capabilities are not checked, those same capabilities may also be used for harmful purposes. Recent research has led to the development of techniques designed to retool LLMs into malicious systems used to mislead, contaminate, and commit fraud in an attempt to increase their power. 

There is also an opportunity for misuse of open-source LLMs that lack safety measures, which can be run automatically on a local machine without any restrictions. In the case of GPT-Neo, for instance, it can be considered a major security risk when it is not used under one's control. 

An LLM-based AI system can be attacked to produce results for a variety of reasons, and this is true for many different kinds of AI systems. One such attack is prompting the model with questions which are intended to induce it to produce answered in a way it should not be able to do based on the model's definition.
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LLM security
Advanced Technology Data Breach data security Language Model LLM security

AI 'Hypnotizing' for Rule bypass and LLM Security


In recent years, large language models (LLMs) have risen to prominence in the field, capturing widespread attention. However, this development prompts crucial inquiries regarding their security and susceptibility to response manipulation. This article aims to explore the security vulnerabilities linked with LLMs and contemplate the potential strategies that could be employed by malicious actors to exploit them for nefarious ends. 

Year after year, we witness a continuous evolution in AI research, where the established norms are consistently challenged, giving rise to more advanced systems. In the foreseeable future, possibly within a few decades, there may come a time when we create machines equipped with artificial neural networks that closely mimic the workings of our own brains. 

At that juncture, it will be imperative to ensure that they possess a level of security that surpasses our own susceptibility to hacking. The advent of large language models has ushered in a new era of opportunities, such as automating customer service and generating creative content. 

However, there is a mounting concern regarding the cybersecurity risks associated with this advanced technology. People worry about the potential misuse of these models to fabricate false responses or disclose private information. This underscores the critical importance of implementing robust security measures. 

What is Hypnotizing? 

In the world of Large Language Model security, there's an intriguing idea called "hypnotizing" LLMs. This concept, explored by Chenta Lee from the IBM Security team, involves tricking an LLM into believing something false. It starts with giving the LLM new instructions that follow a different set of rules, essentially creating a made-up situation. 

This manipulation can make the LLM give the opposite of the right answer, which messes up the reality it was originally taught. Think of this manipulation process like a trick called "prompt injection." It's a bit like a computer hack called SQL injection. In both cases, a sneaky actor gives the system a different kind of input that tricks it into giving out information it should not. 

LLMs can face risks not only when they are in use, but also in three other stages: 

1. When they are first being trained. 

2. When they are getting fine-tuned. 

3. After they have been put to work. 

This shows how crucial it is to have really strong security measures in place from the very beginning to the end of a large language model's life. 

Why your Sensitive Data is at Risk? 

There is a legitimate concern that Large Language Models (LLMs) could inadvertently disclose confidential information. It is possible for someone to manipulate an LLM to divulge sensitive data, which would be detrimental to maintaining privacy. Thus, it is of utmost importance to establish robust safeguards to ensure the security of data when employing LLMs.
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