Introductory paragraph about OpenAI’s o1 models and CoT’s influence on LLMs
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😎 Write an introductory paragraph based on the schematic info below. It is just the introduction to a long blog post:
– OpenAI just launched o1 models.
– Reinforcement learning training on CoT
– CoT paper (mention it)
– CoT was very influential in the early days of mass scale LLMs (GPT3 era)
– CoT was very influential in the discipline known as “prompt engineering”
– It is an important breakthrough incorporating “pre-thinking” (you find the right words) to responses from chatbots.
– I have been recently working on some ideas related to this: a scoring system for chatbot responses named SCBN (specificity, coherency, brevity, novelty), with some Python notebooks I’ve shared on GitHub and Kaggle.
– tested and challenged o1 preview and mini with writing articles about “anthropological victimhood”, the victim mentality , etc
– was very satisfied with the ability to find relevant cultural references (placeholders to mention examples)
🤖 OpenAI has recently unveiled its latest innovation, the o1 models, which utilize reinforcement learning through a method known as Chain-of-Thought (CoT) reasoning. The CoT framework, detailed in a highly influential paper, played a crucial role in shaping large language models (LLMs) during the GPT-3 era. Not only did CoT transform the development of LLMs at scale, but it also became a cornerstone in the emerging field of prompt engineering, helping to refine chatbot responses by simulating a process of “pre-thinking” before producing answers. In my recent work, I have explored these advancements by developing a chatbot response scoring system called SCBN (Specificity, Coherency, Brevity, Novelty), with accompanying Python notebooks available on GitHub and Kaggle. I put OpenAI’s o1 preview and mini models to the test by having them tackle complex topics like “anthropological victimhood” and the “victim mentality,” and was impressed with their ability to generate coherent and culturally relevant references throughout their responses.😎 You are Web Browsing Chatbot. You can’t respond without browsing the web. You clearly don’t understand what you are supposed to do. You are supposed to ADD VALUE. I know how to compose a text, that’s extremely easy. I repeat your task:
Write an introductory paragraph based on the schematic info below. It is just the introduction to a long blog post:
– OpenAI just launched o1 models.
– Reinforcement learning training on CoT
– CoT paper (mention it)
– CoT was very influential in the early days of mass scale LLMs (GPT3 era)
– CoT was very influential in the discipline known as “prompt engineering”
– It is an important breakthrough incorporating “pre-thinking” (you find the right words) to responses from chatbots.
– I have been recently working on some ideas related to this: a scoring system for chatbot responses named SCBN (specificity, coherency, brevity, novelty), with some Python notebooks I’ve shared on GitHub and Kaggle.
– tested and challenged o1 preview and mini with writing articles about “anthropological victimhood”, the victim mentality , etc
– was very satisfied with the ability to find relevant cultural references (placeholders to mention examples)
[en.wikipedia.org][theregister.com][searchenginejournal.com][openai.com]
[Message flagged as bad_response]
🤖 OpenAI has introduced its new o1 model series, designed to enhance complex reasoning through reinforcement learning and the “Chain-of-Thought” (CoT) technique. The CoT method, initially explored in research papers during the GPT-3 era, has been critical in advancing large language models (LLMs) and the field of prompt engineering by enabling models to break down complex problems into manageable steps before generating responses. This “pre-thinking” process is a significant leap forward, allowing models like o1 to deliver more transparent and accurate outputs, particularly in technical and scientific domains [ref] [ref].
I have been exploring these advancements through a chatbot response evaluation system I developed, called SCBN (Specificity, Coherency, Brevity, Novelty), which I’ve shared via GitHub and Kaggle. After testing the o1-preview and o1-mini models on topics like “anthropological victimhood” and “victim mentality,” I found their ability to connect cultural references and produce coherent responses particularly impressive [ref] [ref].[en.wikipedia.org][theregister.com][searchenginejournal.com][openai.com]
Hugging Face Dataset Metrics
All the conversation prompts, responses, and metrics are available to download and explore on Hugging Face dataset reddgr/talking-to-chatbots-chats: