Testing is particularly crucial for large language models. Since LLMs can generate free-form text, it's hard to anticipate their exact responses. Flaws in the training data or model architecture can lead to Hallucinations, biases, and errors that only surface during real-world usage. Rigorous t...
Large Language Models (LLMs) have shown remarkable promise in communicating with humans. Their potential use as artificial partners with humans in sociological experiments involving conversation is an exciting prospect. But how viable is it? Here, we rigorously test the limits of agents that debate ...
Testing Large Language Models with Natural Language Requirements 主讲人(Speaker):Xueqing Liu 时间(Date & Time):2023.12.19;1-2pm 地点(Location):理科一号楼1621(燕园校区)Room 1621, Science Building #1 (Yanyuan) 邀请人(Host):谢涛 主办...
The recent rise of large language models (LLMs), such as generative pre-trained transformer (GPT) models, has shown some promise that artificial theory of mind may not be too distant an idea. Generative LLMs exhibit performance that is characteristic of sophisticated decision-making and reasoning...
Large language models (LLMs) are advanced deep learning algorithms that can process written or spoken prompts and generate texts in response to these prompts. These models have recently become increasingly popular and are now helping many users to create summaries of long documents, gain inspiration...
Now, let’s shift gears and look at how Large Language Models (LLMs) are stepping in as a game-changer. Testing has always been complex, and while LLMs aren’t a magic fix, they’re definitely a tool you can’t ignore. Imagine having a senior QA architect who instantly applies years...
As artificial intelligence (AI) technologies, particularly large language models (LLMs) like OpenAI's GPT-4, continue to evolve and integrate into various sectors, ensuring the protection of intellectual property (IP) becomes paramount. This report presents a comprehensive and systematic framework desig...
Abstract 本文: Task: Review on the use of LLMs in software testing Method: 1. analyzes 52 relevant studies 1. Intro 2. Background 2.1 Large Language Model 2.2 Software Testing 3. Paper Selection and Review Schema 3.1 Survey Scope 3.2 Paper Collection Methodology ...
Combining different forms of prompts with pre-trained large language models has yielded remarkable results on reasoning tasks (e.g. Chain-of-Thought prompting). However, along with testing on more complex reasoning, these methods also expose problems such as invalid reasoning and fictional reasoning ...
The integration of machine learning (ML) into cyber-physical systems (CPS) offers significant benefits, including enhanced efficiency, predictive capabilities, real-time responsiveness, and the enabling of autonomous operations. This convergence has accelerated the development and deployment of a range of...