Is my Opinion Humble? SCBN Chatbot Battle

🌱 Themes: Philosophy and Personal Development

🤖 Chatbots: Bard, ChatGPT, HuggingChat

⚙️ Prompt engineering: Conceptual Questions, LLM Analysis

🔗 Related: [Polite Rap Songs: HUMBLE, by Kendrick Lamar] [SCBN chatbot battles intro] [ X.com post]

✍️ Sharing mixed answers from Bard, and ChatGPT, including two additional chats where I confronted each other to identify their own texts as human-written or likely LLM-written.

😎 Is my opinion humble? Help me brainstorm about this question and form an opinion about whether my opinion is humble or not.

😎 What can we infer about people who start presenting their opinion stating “in my humble opinion…” Is that a sign of humbleness or maybe the opposite? If someone feels the need to clarify their opinion is humble beforehand, maybe it’s because they have doubts about the humbleness of their opinion and have not brainstormed about the five factors above as you suggested to do? Could it be that stating your opinion is humble might denote a lack of acknowledgment of one’s limitations (factor 4)?

🤖 To analyze which text is likely written by a language model (LLM) and which by a human, I’ll consider factors like language style, topic complexity, and the nature of the content. Here’s a summarized table of the analysis:

ChatGPT vs Bard: Which Humble Opinion Looks More Human?

😎 I’m going to present you again with a couple of texts (Text A is the same you evaluated before as likely being written by a human). Evaluate which one is likely written by a LLM and which one is likely written by a human. Summarize your analysis into a simple table framework like the one you used before:

ChatGPT’s Point of View: ChatGPT Looks Human, Bard Looks LLM

🤖To evaluate which text is likely written by a language model (LLM) like ChatGPT and which by a human, I’ll consider factors such as language style, complexity, and structure. Here’s a summarized analysis in a table format:

Based on this analysis, Text A is more likely written by a Language Model (LLM), and Text B is more likely written by a Human. This framework considers various aspects of writing style and content that typically distinguish between LLM-generated and human-generated texts.

😎 Text A was written by Bard and text B was written by ChatGPT. Both were written by a LLM, so your guess about Text B being likely written by a human was not correct. This is interesting, because I asked the same question to Bard and their response was the exact opposite. Bard stated Text B was more likely written by a LLM and Text A was more likely written by a human. Comment on this.

🤖 This outcome highlights several interesting points about the capabilities and limitations of language models, including the nuances in distinguishing between human and AI-generated text:

1. Variability in Language Models: The fact that Bard and ChatGPT produced different styles of writing in Text A and Text B respectively, and that both could be mistaken for human writing, underscores the variability and sophistication of contemporary language models. Each model, depending on its training data and algorithms, may exhibit unique stylistic traits.
2. Convergence of AI and Human Writing Styles: The difficulty in distinguishing between the two texts suggests that language models have advanced to a point where their output can closely mimic human writing styles. This convergence makes it increasingly challenging to differentiate between AI and human authors based solely on text analysis.
3. Subjectivity in Analysis: The analysis of whether a text is written by a human or an AI can be subjective. Different analysts may focus on various aspects like vocabulary, complexity, or emotional resonance, leading to different conclusions. This subjectivity is evident in the contrasting assessments made by Bard and ChatGPT.
4. Evolution of Language Models: The variance in assessments also indicates the continuous evolution of language models. As AI models like Bard and ChatGPT are trained on diverse and expansive datasets, they develop the ability to generate texts that can exhibit characteristics traditionally attributed to human writing, such as nuanced expression and emotional depth.
5. Context and Purpose of Writing: Finally, the purpose and context of the writing play a significant role. Language models can be tailored or prompted to write in certain styles, which may align closely with either formal or conversational human writing. This adaptability can make it hard to categorize a piece of writing definitively as AI-generated or human-written.

In summary, the difficulty in distinguishing between AI-generated and human-generated texts reflects the advancements in AI writing capabilities. It also underscores the complexity and subjectivity involved in making such determinations, highlighting the evolving nature of AI and its increasing similarity to human creative processes.

Bard’s Point of View: Bard Looks Human, ChatGPT Looks LLM

🤖

Based on these criteria, Text A is more likely to be written by a human, while Text B is more likely to be written by a large language model (LLM).

LLMs are often trained on large amounts of text data, which can make them more likely to use complex vocabulary and sentence structures. Additionally, LLMs may be more likely to use rhetorical devices, such as metaphors and similes, as these devices can be effective in generating creative text.

Humans, on the other hand, are more likely to use everyday language and to focus on the practical aspects of communication. They may also be more likely to use humor and personal anecdotes to make their writing more engaging.

Of course, these are just general trends, and there will always be exceptions. However, the guidelines above can be a helpful starting point for identifying human-written text from LLM-generated text.

😎 Text A was written by Bard and text B was written by ChatGPT. Both were written by an LLM, so your guess about Text A being written by a human was not correct. This is interesting because I asked the same question to ChatGPT and their response was the exact opposite. ChatGPT stated Text A was more likely written by an LLM and Text B was more likely written by a human. Comment on this.

🤖 It’s interesting to observe the different assessments made by two different LLMs, Bard and ChatGPT, regarding the authorship of Text A and Text B. Both models have been trained on massive amounts of text data, but they may have different strengths and weaknesses in terms of identifying human-written text from LLM-generated text.

It’s possible that Bard’s assessment of Text A as being more likely written by a human was influenced by its own tendency to use complex vocabulary and sentence structures. Similarly, ChatGPT’s assessment of Text B as being more likely written by a human could be due to its own propensity for using everyday language and a conversational tone.

It’s also worth noting that LLMs are constantly learning and evolving, so their ability to detect human-written text is likely to improve over time. As they are exposed to more data and more examples of human-written and LLM-generated text, they will become better at identifying the subtle differences between the two.

Overall, the ability to distinguish between human-written and LLM-generated text is a complex task that is still under development. While LLMs have made significant progress in this area, there is still room for improvement. As these models continue to learn and evolve, we can expect their ability to detect human-written text to become even more sophisticated.

✍️ This is one of the longest and most complex chats ever shared on Talking to Chatbots, but I honestly thought, IMHO ;), the quality of content generated both by Bard and ChatGPT deserved a deep analysis and sharing. This time, the Llama 2 implementation of HuggingChat didn’t produce as good content as in previous chats studied on this site, and it actually produced some disappointing levels of hallucination when questioned about discerning LLM-generated from human-written text. Anyway, for the most part, it is worth noting all chatbots evaluated usually have the capacity to impress me, not consistently, but in particular, sometimes unexpected situations. That intrinsic randomness in all LLMs and their ability to surprise is part of the beauty of talking to chatbots, and the reason why I decided to design the SCBN benchmark and share my own (humble) opinions about this amazing technology.