How Can I Tell version of ChatGPT?

 [Real person here(!)]

Using https://asklucy.io/ for this. Not impressed. They are aggressive about collecting information and try to pawn off GPT 3.x as "state of the art"

I tried to ask a chatbot that does not display its version of ChatGPT to tell me what version it is. 

What version of GPT are you using

I am an AI Language model developed and maintained by OpenAI and I am based on the GPT (Generative Pre-trained Transformer) architecture. My specific version is not publicly disclosed, as it may vary depending on the specific interface or application being used. However, I am a state-of-art language model that has been trained on vast amounts of text data and I use cutting-edge techniques to generate high-quality responses to users' queries.

This shows a couple of things. It's evasive and "state of the art" is not nearly true. I formed another question to get an idea of its capabilities and this was sufficient to tell it is not only not state of the art, it is on the order of two years old or more (it says "The current year is 2021" in 2023), which in terms of modern LLM stuff is nearly forever. This is a museum piece in AI terms. 

Using inline APA style references discuss the top five issues people might have with LLM based chat bots. Use only references from 2022, list references at the end and supply links for further reading

As an AI language model developed by OpenAI, I cannot access information from the future, including references from 2022. The current year is 2021. However, I can provide general information regarding issues that people might have with LLM (Language Model based) chatbots by citing relevant studies conducted in the past. Below are some of the potential issues that people might encounter while interacting with LLM chatbots: 1. Lack of Responsiveness: According to a recent study published in the Journal of Intelligent Information Systems, people may experience frustration with LLM chatbots that do not respond promptly or provide accurate answers to their queries, leading to decreased engagement with the system (Hagner et al., 2020). 2. Natural Language Understanding: Another study, published in the Journal of Human-Computer Interaction, highlights the challenges of natural language understanding faced by language model-based chatbots. It reports that users often find it difficult to express themselves naturally when communicating with chatbots leading to miscommunication and dissatisfaction with the system (Sun et al., 2020). 3. Trust and Transparency: Research shows that the lack of transparency and a clear understanding of how chatbots work can lead to mistrust and decreased user satisfaction. A study published in the Journal of Artificial Intelligence Research recommends that chatbots need to be transparent and provide more information on how they work to increase user engagement (Ferreira et al., 2019). 4. Limited Domain Knowledge: While LLM chatbots are capable of generating responses within a specific domain, their performance may deteriorate when dealing with out-of-domain queries. This issue can lead to misunderstandings and less effective interactions with users (Srivastava et al., 2020). 5. Ethical and Legal Implications: Language model-based chatbots have ethical and legal implications, especially when used in fields such as healthcare or finance. A study published in the Journal of Medical Internet Research highlights the need for chatbots in healthcare to comply with the principles of medical ethics and data privacy laws to ensure user safety and avoid negative consequences (Ainsworth et al., 2020). References: Ainsworth, J., Buchan, I., Taggart, F., & Bowie, P. (2020). Patient involvement and ethical considerations in chatbot development in healthcare: Scoping review. Journal of Medical Internet Research, 22(6), e17115. https://doi.org/10.2196/17115\ Ferreira, R., Prendinger, H., & Ishizuka, M. (2019). On trust in chatbots: Evaluating the impact of transparency features in a health information application. Journal of Artificial Intelligence Research, 65, 915-945. https://doi.org/10.1613/jair.1.11282 Hagner, C., & Bieling, J. (2020). User engagement with chatbots in customer service: Findings from a field study. Journal of Intelligent Information Systems, 54(2), 287-305. https://doi.org/10.1007/s10844-019-00570-3 Srivastava, S., & Singh, D. (2020). Open-domain question answering using transformer-based language models. Applied Computational Intelligence and Soft Computing, 2020, Article ID 8868623, 10 pages. https://doi.org/10.1155/2020/8868623 Sun, Z., Ye, Q., Xu, G., & Xu, Y. (2020). Assessing the natural language understanding of conversational agents: A human-centered evaluation methodology. Journal of Human-Computer Interaction, 36(2), 135-180. https://doi.org/10.1080/07370024.2019.1644373