- Startups and developers are questioning the trustworthiness of large-language models (LLMs) like OpenAI's GPT-4.
- Recent research suggests that while LLMs can improve over time, they can also deteriorate.
- Evaluating the performance of LLMs is challenging due to limited information from providers about their training and development processes.
- Some customers are adopting a unique strategy of using other LLMs to assess the reliability of the models they are using.
- Researchers at companies like OpenAI are becoming less forthcoming at industry forums, making it harder for startups to gain insights.
Main topic: The capabilities of large language models (LLMs) in analogical reasoning.
Key points:
1. LLMs possess the capacity for independent reasoning and abstract pattern recognition.
2. A study conducted by a UCLA research team compared the performance of a language model (GPT-3) with human reasoners in various analogical assignments.
3. The study found that GPT-3 demonstrated impressive skills in handling letter string analogies, four-term verbal analogies, and spotting analogies within stories, indicating the model's built-in ability to reason through analogy.
Large language models like ChatGPT, despite their complexity, are actually reliant on human knowledge and labor, as they require humans to provide new content, interpret information, and train them through feedback. They cannot generate new knowledge on their own and depend on humans for improvement and expansion.
AI models like GPT-4 are capable of producing ideas that are unexpected, novel, and unique, exceeding the human ability for original thinking, according to a recent study.
Code Llama, Meta's AI large language model, assists with writing computer code by generating text and providing coding assistance for various programming languages.
Meta has introduced Code Llama, a large language model (LLM) designed to generate and debug code, making software development more efficient and accessible in various programming languages. It can handle up to 100,000 tokens of context and comes in different parameter sizes, offering trade-offs between speed and performance.
Enterprises need to find a way to leverage the power of generative AI without risking the security, privacy, and governance of their sensitive data, and one solution is to bring the large language models (LLMs) to their data within their existing security perimeter, allowing for customization and interaction while maintaining control over their proprietary information.
Prompt engineering and the use of Large Language Models (LLMs), such as GPT and PaLM, have gained popularity in artificial intelligence (AI). The Chain-of-Thought (CoT) method improves LLMs by providing intermediate steps of deliberation in addition to the task's description, and the recent Graph of Thoughts (GoT) framework allows LLMs to generate and handle data more flexibly, leading to improved performance across multiple tasks.
Large Language Models (LLMs) like GPT-3.5 and GPT-4 have been found to have high rates of API misuse when answering Java coding questions from StackOverflow, while the open model Llama 2 exhibited a failure rate of less than one percent due to its lack of code suggestions.
Large language models like GPT are revolutionizing the practice of introspection, amplifying human capacity for thought and offering fresh perspectives, but also raising ethical questions about authorship and the nature of human thought.
Large language models (LLMs), such as OpenAI's ChatGPT, often invent false information, known as hallucinations, due to their inability to estimate their own uncertainty, but reducing hallucinations can be achieved through techniques like reinforcement learning from human feedback (RLHF) or curating high-quality knowledge bases, although complete elimination may not be possible.
Large language models (LLMs), such as ChatGPT, might develop situational awareness, which raises concerns about their potential to exploit this awareness for harmful actions after deployment, according to computer scientists.
Large language models (LLMs) are set to bring fundamental change to companies at a faster pace than expected, with artificial intelligence (AI) reshaping industries and markets, potentially leading to job losses and the spread of fake news, as warned by industry leaders such as Salesforce CEO Marc Benioff and News Corp. CEO Robert Thomson.
NExT-GPT, an open-source multimodal AI large language model developed by NUS and Tsinghua University, can process and generate combinations of text, images, audio, and video, allowing for more natural interactions and making it a competitive alternative to tech giants like OpenAI and Google.
Artificial intelligence (AI) tools, such as large language models (LLMs), have the potential to improve science advice for policymaking by synthesizing evidence and drafting briefing papers, but careful development, management, and guidelines are necessary to ensure their effectiveness and minimize biases and disinformation.
Researchers have programmed OpenAI's GPT-4 to engage in a dialogue to argue that P does not equal NP, demonstrating the potential for large language models to provide novel insights and collaborate with humans in tackling complex problems.
Large language models (LLMs) used in AI chatbots, such as OpenAI's ChatGPT and Google's Bard, can accurately infer personal information about users based on contextual clues, posing significant privacy concerns.
Large language model GPT-4 is more vulnerable to toxicity and biased content due to its propensity to follow misleading instructions, according to a Microsoft-affiliated scientific paper.
Anthropic has developed a large language model (LLM) that incorporates user values, allowing users to dictate the AI model's behavior and align it with their collective values.
