# Evaluating Quantized Llama 2 Models for IoT Privacy Policy Language Generation
## 1 Introduction  
In today's world, there has been a significant increase in the usage of Internet of Things (IoT) devices, and this usage is expected to rise rapidly in the upcoming years [1; 2]. IoT devices are designed to make our lives easier [3; 4; 5], but they can become a significant threat to the privacy of consumers [6; 7; 8; 9] because IoT devices can collect personally identifiable information such as one's name, age, email address, date of birth, and other types of data, such as sensor-based data (e.g., faces, sound) and metadata [10]. From the perspective of service providers, the privacy policies of IoT devices/services describe what type of information they collect, how long they retain this information, how they are going to use this information, and how they share this information with third parties [11]. While these documents are provided to users to let them know how their data/information is handled, past research shows that the users do not read the terms and conditions of the privacy policy notices and many times accept them blindly [12]. There has been a disconnection between the privacy policies' documents and the comprehension of these policies by users [13; 14]. First, the privacy policy language is vague, and it is very difficult for a naive user to understand the information [7]. Second, privacy policies of IoT devices keep changing regularly and users might not be aware of these changes [11; 15]. Additionally, privacy policies for IoTs are very lengthy [11], and all the information is scattered, making it difficult for the consumers to find what theyare exactly looking for [16]. Previous studies have attempted to address these issues by simplifying the language or restructuring the information in privacy policies [17]; however, these solutions have not fully leveraged the latest AI technologies. With the advancements in artificial intelligence (AI) and natural language processing (NLP), some of the issues regarding understanding privacy policy documents can be addressed. Among these new developments, large language models (LLMs) [18] have attracted great attention due to their ability to comprehend, analyze, and generate text. Large language models (LLMs) can also be called Transformer Language models, and they are composed of billions and trillions of parameters [18] which are trained on considerable amounts of text data [19]. Examples of large language models include GPT-3 (Generative Pre-trained Transformer) [20], which is known for its ability to generate coherent and contextually relevant text on a wide range of topics and is widely used for applications such as content creation, language translation, and summarization. Next, PaLM (Pathways Language Model) [21] is distinguished by its pathway-based training approach, enhancing its multitasking capabilities and excelling in tasks that require reasoning, such as problem solving and code generation. BERT (Bidirectional Encoder Representations from Transformers) [22] revolutionized the field of natural language understanding by its use of bidirectional training, significantly improving performance on tasks like sentiment analysis and question answering. RoBERTa (Robustly Optimized BERT Pretraining Approach) [23] is an optimized version of BERT that adjusts key hyperparameters, which allows it to achieve better performance on a wider range of language-understanding benchmarks. Galactica [24], focused on scientific knowledge, aids in digesting and summarizing large volumes of academic literature and technical documents. LLaMA (Large Language Model Meta AI) [25] is known for its efficiency and scalability and is utilized for a broad array of applications, including assisting in generating and summarizing complex documents. These models can solve complex problems like text generation, question answering, and code generation, among others [18].