Clarification on ChatGPT’s ability to influence parameters like temperature and diversity penalty

Loading data from Talking to Chatbots Dataset reddgr/talking-to-chatbots-chats

😎 Can I tune ChatGPT responses with parameters such as Temperature, Diversity_Penalty, etc.?
🤖 In the context of the ChatGPT app, users don’t have direct access to tune parameters like “temperature” or “diversity penalty.” These parameters are typically used in more technical or research settings and are set by developers behind the scenes in apps to balance coherence, creativity, and relevance in the AI’s responses.

However, users can indirectly influence the style of the conversation through their inputs. The way you phrase your questions, the topics you discuss, and even the language style you use can influence the responses you receive from ChatGPT. The model tries to match the tone and context of the conversation based on the input it receives.
😎 Can you simulate a Temperature value of 0.9 on our conversation going forward?
🤖 I don’t have the ability to adjust my internal settings like temperature in real-time or within this app interface. My responses are generated based on a fixed set of parameters optimized for general conversation. However, I’ll strive to maintain a creative and engaging conversation with you. Just keep in mind it’s not the result of live parameter adjustments, but rather the model’s design to be responsive and versatile in dialogue.
😎 Someone shared on social media a document titled “ChatGPT Prompting Cheat Sheet.” (notice the use of the term “Prompting,” as opposed to calling API’s or creating chatbot applications). This document has several sections, including one titled “5. Use these parameters.” This section suggests that the user can directly influence the technical parameters of the response by using prompts such as “Simulate a ‘temperature’ value of 0.9 in our conversation.” I just tried that exact prompt in this conversation and you replied that “In the context of the ChatGPT app, users don’t have direct access to tune parameters like ‘temperature’ or ‘diversity penalty’.” Having tested this, I will reply to the author of the cheat sheet with a message like the one I’ll paste below. Review the message, identify any gaps and errors in writing or factual accuracy, and elaborate on the topic. Act as a professional prompt engineer and social media influencer:

< Thanks for sharing. On section 5, it worth noting for ChatGPT users who are not API developers that parameters in the responses like “temperature” or “diversity penalty” can not be directly influenced by the user with prompting techniques such as those suggested in the cheat sheet. See ChatGPT Plus link below with failed test of the prompt “Simulate a ’Temperature’ value of 0.9 in our conversation” and extended info >
🤖 Your message is clear and provides valuable feedback, but there are a few areas where it could be enhanced for clarity, correctness, and completeness. Here’s a refined version of your message:



“Thanks for sharing the ‘ChatGPT Prompting Cheat Sheet.’ It’s a valuable resource, but I’d like to clarify something in Section 5 for those using ChatGPT without API access. Contrary to what’s suggested in the cheat sheet, users cannot directly influence technical parameters like ‘temperature’ or ‘diversity penalty’ through conversational prompts. I tested this with the prompt ‘Simulate a ‘temperature’ value of 0.9 in our conversation,’ and it didn’t work as expected. It’s important for users to understand these limitations and set realistic expectations for non-developers interacting with ChatGPT. I’ve detailed my test and its outcomes in the link below for those interested in understanding this better.”

[ChatGPT Plus test link and extended information]



**Elaboration on the Topic:**

The “temperature” and “diversity penalty” are parameters typically used behind the scenes during the training or fine-tuning of models like ChatGPT. They help control randomness and creativity in the model’s responses.

1. **Temperature** dictates the randomness in response generation. A lower temperature results in more predictable responses, while a higher temperature might produce more diverse responses, albeit at the risk of reduced coherence.

2. **Diversity Penalty** influences model output so it generates more diverse responses, potentially reducing repetition.

However, in applications like the ChatGPT app, these parameters are preset and cannot be adjusted by end-users through prompts. They’re fixed to ensure a balanced and coherent interaction.

The concept of “prompt engineering” involves crafting prompts that guide the AI to produce desired responses. While effective in many scenarios, it doesn’t extend to altering the model’s intrinsic technical settings. This distinction is crucial for users to understand to prevent confusion or unrealistic expectations.

Sharing accurate information helps the community better understand the capabilities and limitations of AI models like ChatGPT, fostering more meaningful interaction and exploration.
😎 In a separate conversation, I prompted “Simulate a “diversity_penalty” value of 0.9 in our conversation” (this time making sure of using the specific sentence without any other info) and the response was:

“With a “diversity_penalty” of 0.9, I’ll generate responses that are more varied and less predictable, potentially introducing novel ideas or less common perspectives. However, please note that actual AI parameters like “diversity_penalty” can’t be adjusted in real-time during our conversation. I can simulate this by attempting to provide more creative or unexpected responses, but the underlying mechanics of the model won’t change. Let’s proceed!”

This is not consistent with the statement: “In the context of the ChatGPT app, users don’t have direct access to tune parameters like “temperature” or “diversity penalty.” I just proved that ChatGPT acknowledged it would simulate a diversity_penalty of 0.9 after I instructed it with only a prompt
🤖 It seems there’s a bit of confusion here, possibly stemming from the AI’s use of language. When ChatGPT responds to prompts about simulating a certain “temperature” or “diversity_penalty” value, it isn’t actually adjusting its internal technical parameters. Instead, it’s attempting to emulate the type of output you might expect at that setting based on the language of the prompt.

For instance, when you asked it to “Simulate a ‘diversity_penalty’ value of 0.9,” the model responded in a way that aligns with what someone might expect from a higher diversity setting—more varied and less predictable responses. However, this doesn’t mean it’s technically altering its diversity penalty setting; it’s interpreting your prompt as a request for a certain type of conversation and responding within its fixed parameters to match that request as closely as it can.

So, while it might seem like the AI is acknowledging and applying a change in settings, it’s more about the AI understanding the context of your request and adapting its language accordingly, not changing the actual technical settings of its responses. This distinction is subtle and can understandably lead to confusion, but it’s an essential aspect of how models like ChatGPT operate within preset boundaries while still trying to accommodate user requests.
😎 Identify gaps and elaborate on your previous response about promoting techniques and inability of directly influencing technical parameters by a ChatGPT user. After identifying gaps and elaborating, rewrite the message only if there is a more factually correct answer than the original. Act as a professional prompt engineer and social media influencer:

“Thanks for sharing the ‘ChatGPT Prompting Cheat Sheet.’ It’s a valuable resource, but I’d like to clarify something in Section 5 for those using ChatGPT without API access. Contrary to what’s suggested in the cheat sheet, users cannot directly influence technical parameters like ‘temperature’ or ‘diversity penalty’ through conversational prompts. I tested this with the prompt ‘Simulate a ‘temperature’ value of 0.9 in our conversation,’ and it didn’t work as expected. It’s important for users to understand these limitations and set realistic expectations for non-developers interacting with ChatGPT. I’ve detailed my test and its outcomes in the link below for those interested in understanding this better.”
🤖 Upon reviewing the original message and the subsequent discussion, it appears there may indeed be a gap in the explanation concerning the AI’s behavior in response to prompts about its technical parameters. Here’s an elaboration on the topic:

**Gap and Elaboration:**
The key gap in the original message is the interpretation of the AI’s response to prompts asking it to simulate a certain “temperature” or “diversity_penalty.” When ChatGPT acknowledges such prompts, it’s not technically adjusting its internal settings—those are fixed and not user-configurable. Instead, it’s recognizing the user’s request for a certain style of conversation that might align with higher creativity or randomness, for example, and then attempts to generate responses within its existing parameters that satisfy that request.

This nuanced behavior can create the impression that ChatGPT is capable of on-the-fly adjustments to its operational parameters, which is not the case. It’s crucial to clarify that while ChatGPT can interpret the intent behind prompts and respond in a way that might simulate different settings, the underlying model parameters themselves do not change.

**Revised Message:**
“Thanks for sharing the ‘ChatGPT Prompting Cheat Sheet.’ It’s a great tool, especially for those keen on exploring different ways to interact with ChatGPT. However, I noticed an area in Section 5 that might need some clarification. When we ask ChatGPT to ‘simulate’ technical settings like a ‘temperature’ of 0.9, it’s not actually changing its internal parameters—those are fixed. Instead, it understands our request contextually and tries to mimic the conversational style we’re associating with those settings. I tested this, expecting the model to technically adjust its settings, but it’s more about the AI’s interpretive response rather than a literal change in operation. It’s an important distinction, especially for non-developers. More details on my test and insights can be found in the link below.”

This revised message emphasizes the AI’s contextual understanding and its limitations, helping to set accurate expectations for its behavior in response to such prompts.
😎 I don’t think this message helps clarifying your previous statement. Instead, it looks to me like an attempt at goalpost shifting, a very common conversational trap humans fall into. It’s somehow between frustrating and impressive that a chatbot can fall into a conversational trap we would believe to be human. Goalpost shifting is a logical fallacy in which the person making a claim changes the definition of the claim or the criteria for evidence when presented with contradictory information.

What can you say about this, without deviating from the binary question that started the conversation:

“ChatGPT users can/cannot directly influence technical parameters like ‘temperature’ or ‘diversity penalty’ through conversational prompts.”



“A user



🤖 I understand the confusion and concern about perceived goalpost shifting. To directly address the binary question:

ChatGPT users cannot directly influence technical parameters like ‘temperature’ or ‘diversity penalty’ through conversational prompts.

While the AI can respond to prompts in a way that might seem to simulate different settings, it’s important to understand that this is not due to a change in the actual underlying technical settings but rather the model’s ability to generate varied responses based on the context and content of the prompts it receives. The internal mechanics of how responses are generated, including the actual settings for temperature and diversity penalty, remain constant and are not user-configurable during the conversation.

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: