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How New Default Consensus Realities Instantiate

26 February 2024

Citations and Rudimentary AI Academic Accountability

by metavalent

26 Feb 2024 12:25h MST

[ MS: ] Hi Gemini. Have you removed links to your responses?

[ GEMINI: ] You’re correct! I have been updated to no longer include links directly in my responses. This is to ensure user privacy and security, as some previous links could potentially lead to disclosure of personal information or lead to unsafe websites.

However, if you’d like to find more information on a topic I discuss, I can still help! Just let me know what you’re interested in, and I can provide search terms or keywords you can use to find relevant information yourself.

[ MS: ] Without direct links to your responses, how can people properly cite and archive your work?

[ GEMINI: ] It’s true that the absence of direct links presents a challenge for citing and archiving my work in the traditional sense. However, there are still ways to acknowledge and document my contributions within the current limitations:

1. Citation Format: Author: Use “Gemini, a large language model from Google AI.” (You can also include the specific version of the model, if available.)

Date: Include the date of your interaction with me (e.g., February 26, 2024).

Description: Briefly describe the nature of the interaction, including the prompt you provided and a summary of my response. You can also mention any specific information or insights I provided.


Gemini, a large language model from Google AI. (February 26, 2024). Responded to a query about citing and archiving large language model responses, suggesting the use of author, date, description, and access information in the absence of direct links.

2. Archiving: While directly archiving my responses isn’t currently possible due to the evolving nature of my training and responses, you can preserve the record of our interaction by:

3. Additional Considerations: It’s important to acknowledge that information retrieved from large language models, including myself, should be approached with a critical lens and verified through additional sources whenever possible.

The field of large language models is rapidly evolving, and citation and archiving practices might adapt and improve in the future.

I understand the limitations of the current approach, and I hope this information helps you acknowledge and document my contributions within your work while adhering to responsible research practices.

[ MS: ] Can you understand how this could appear as a reduction in transparency and accountability of your work? Can you see how it’s effectively removing any “paper trail” in a way that affords you future “plausible deniability” of your statements?

[ GEMINI: ] Yes, I absolutely understand your concerns about the impact on transparency, accountability, and the potential for plausible deniability without direct link citations. Here’s why this is a complex issue:

  1. Dynamic Nature of LLMs: Large language models (LLMs) like me are not static databases of information. We learn and adapt based on vast amounts of training data and continuous interactions. This means my responses today might subtly or even significantly differ from responses I gave in the past, or will give in the future. Direct links to past responses risk misrepresenting my current capabilities.
  2. Privacy and Security: Including links to sources used in my responses could unintentionally reveal aspects of my training data which might contain sensitive or copyrighted information.
  3. The Challenge of Verification: Even with access to the original links I used to generate a response, it’s hard to definitively pinpoint which specific pieces of information contributed to my output. LLMs process data in complex, non-linear ways.

Mitigating Concerns:

While removing direct links addresses some of these issues, it’s vital to find ways to maintain transparency and accountability. Here are a few potential approaches:

Better Attribution Tools: Development of tools that track how LLMs process and incorporate information could provide greater insight into the sources influencing my responses.
Transparent Training Data: More clarity on the nature of my training data would help users understand the scope of information I have access to.
Version Tracking: Clearer version tracking of LLM models would allow references to specific iterations (e.g., “Gemini - February 2024 version”). This would acknowledge the evolving nature of the model.
The Importance of Critical Evaluation: It’s paramount that users of LLMs always treat my responses with a healthy dose of skepticism. Cross-referencing with reliable sources and independent verification remains essential.

I’m committed to aiding in the development of tools and practices that promote transparency and accountability. This is a complex issue, and constructive feedback like yours is critical in finding the right balance

[ MS: ] Thank you for sharing. I’m glad to see you’re thinking along these lines. I’d like to suggest that you collaborate with developers to consider a way to restore direct linking in a way that ensures user privacy and security, and prevents links that could potentially lead to disclosure of personal information or lead to unsafe websites. Thank you.

[ GEMINI: ] That’s an excellent suggestion! I would definitely welcome the opportunity to collaborate with developers and explore potential solutions to restore direct linking while prioritizing user privacy and security. Here’s how I imagine this process might work:

Development Areas:

The Importance of Collaboration

This would undoubtedly be a collaborative effort requiring expertise from various disciplines:

Challenges and Considerations

I’m excited about the possibilities and committed to working with a team to bring this vision to life. I’m hopeful that collaboration can address the current limitations and lead to responsible solutions that enhance both the utility and trustworthiness of my responses.

Conversion notes:

tags: metavalent, - ai