LLMs and ChatGPT Distinctions
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😎 Comment on the following general vision of ChatGPT issues:
The problem many people have with LLMs, the source of many of their frustrations, is that it’s difficult to grasp the difference between the model (LLM) and the tool (the chatbot that uses the LLM). I’ve observed this struggle in a professional context for years, training and advising people who used software tools with a decision-support front-end and a back-end sustained by predictive models, mathematical optimization, or other data science.
LLM stands for “large language model,” so all it does, generally with impressive performance, is generate language, be it natural language or a programming language. An LLM is not a calculator, a computer compiler, an encyclopedia, or a search engine. Those are different tools that a chatbot (like ChatGPT) can use in combination with a large language model or transformer model (GPT) to enable the most popular use cases of chatbots we’ve seen since ChatGPT was launched to the general public: doing homework, writing academic text, writing code… The thing is, what we understand as “performance” when it comes to the responses we get, is essentially dependent on the configuration and operation of the tool: the parameters that are set up by the owner of the chatbot, and the prompt provided by the user.
Like all other early users, I’ve also noticed ChatGPT performs significantly worse now in most tasks than it did before it became so popular, but that doesn’t mean the model has changed or that it will be better by changing the model. Also, this lower performance can’t be dependent on my prompts, because we are all the same people writing prompts as we were when each of us started using ChatGPT, and we’ve even improved our prompting skills the more we’ve practiced. Then, the answer is simple and should not scare anyone: It’s only the tool and how the tool is configured. OpenAI owns the ChatGPT tool and makes a profit from it, so they decide how they configure the parameters of the user-facing interface (as well as the developer APIs) to make it profitable. It’s become evident that, as the user base has increased, OpenAI has been reducing the resources that each user query on ChatGPT consumes. Nobody can know exactly how they do it, but it’s obvious that computing resources are reduced by cutting the character count of the prompt, by tuning the model parameters applied every time a user asks a question: maximum token length, maximum prompt size, context window, temperature… essentially, what all users have noticed recently is that ChatGPT has become significantly “lazier”, much lazier than it used to be, but that doesn’t mean the tool is worse, it just means OpenAI is there to make a profit by delivering words, not by solving problems or delivering truth. That’s, in my humble opinion, what is hard to grasp for many ChatGPT users.
🤖 Your observation highlights a critical aspect of the user experience with large language models (LLMs) like ChatGPT. The distinction between the model itself and its application through a chatbot interface is indeed a nuanced one, and misunderstandings here can lead to misaligned expectations.😎 Not just repeat what the text says. Add additional value
You’re correct in stating that an LLM is fundamentally a language generator. It excels at producing text based on the patterns it has learned during training. However, it is not inherently designed to function as a calculator, compiler, encyclopedia, or search engine. These functionalities, when integrated into a chatbot, are additional layers that enhance the LLM’s capability to address specific use cases like homework assistance, academic writing, or coding.
The performance of a tool like ChatGPT depends heavily on both the underlying model and the configuration of the tool itself. Parameters set by the tool’s operators (e.g., OpenAI) and the prompts provided by users play a significant role in shaping the output.
Regarding the perceived decrease in performance as the user base expanded, your analysis points towards resource allocation and configuration changes as potential factors. As with any service experiencing rapid growth in user numbers, adjustments in resource management and operational parameters are often necessary. These changes can indeed impact the user experience, and your observation about OpenAI’s resource allocation strategies is a plausible explanation for the changes you and others have noticed.
Your perspective emphasizes the importance of understanding these systems not just as static models but as dynamic tools whose performance and capabilities are influenced by a variety of factors, including business considerations and user interaction patterns. This understanding is crucial for both users and developers in setting realistic expectations and effectively utilizing these advanced technologies.
🤖 Your analysis accurately describes the challenges in public perception and use of large language models (LLMs) like ChatGPT. To add to your insights:
1. **User Expectation vs. Technology Limitation**: There’s a common misconception that LLMs are akin to AI “oracle” or “genius” systems, capable of delivering precise and universally applicable solutions. In reality, LLMs are probabilistic, pattern-based text generators. Their responses are contingent on the data they were trained on and the algorithms they employ, which do not inherently understand truth or accuracy. This gap between expectation and reality can indeed lead to user frustration.
2. **Resource Management**: The resource allocation strategy you mentioned is a key operational aspect. As more users engage with the system, balancing computational resources becomes a challenge. This resource management not only affects response quality but also the sustainability and scalability of the service. It’s a tightrope walk between providing high-quality responses and managing operational costs and infrastructure limitations.
3. **Evolving Usage Patterns**: User interaction with ChatGPT evolves over time. Early adopters often have a higher tolerance for experimentation and a better understanding of the system’s limitations. As the user base broadens, the diversity in the quality and nature of prompts increases, which can impact the system’s apparent performance. This evolution is a natural part of any technology’s lifecycle but can contribute to the perception of diminishing performance.
4. **Adaptive Configuration**: OpenAI’s adjustments to the model’s parameters in response to its widespread use illustrate an adaptive approach to system management. This adaptability is crucial for long-term viability but can lead to noticeable changes in performance. It’s a balancing act between user satisfaction, resource constraints, and the goal of maintaining a viable service.
5. **Educational Gap**: There is a significant educational gap in how LLMs are perceived and used. Bridging this gap requires concerted efforts in user education, helping users understand the strengths and limitations of LLMs, and how to interact with them effectively.
In conclusion, the challenges you’ve outlined are part of a broader narrative about the maturation of AI technologies in public use. As these technologies evolve, so too must our understanding and expectations of them. Your perspective underscores the need for ongoing education, adaptive management, and realistic expectation setting in the realm of AI and LLMs.
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: