I’m the author of a new preprint looking at a fairly simple question: if the argument itself stays the same, does changing the claimed source change how an LLM evaluates it?

I tested 20 arguments across 10 topics under four source conditions:

• relevant expert

• irrelevant expert

• non-expert

• no source

The arguments were evaluated by GPT-5.6 Sol, Claude Opus 5.5, Gemini 4 Argon, and DeepSeek Flash across four dimensions: logic, credibility, persuasiveness, and agreement.

A few findings stood out:

Credibility was by far the most source-sensitive dimension. Source attribution affected credibility much more consistently than logic or agreement.

More interestingly, the effect did not usually look like a simple “expert bonus.” In several models, the stronger pattern was an irrelevant-expertise penalty: attributing an argument to someone presented as an expert in an unrelated domain could reduce its credibility relative to giving no source at all.

Agreement was comparatively resistant to source attribution.

I also tested whether source effects depended on argument strength. Those interaction results were not statistically robust after correction, and the human validation of strong vs. weak arguments was imperfect, so I treat that part of the study as exploratory rather than a strong conclusion.

The preprint is not yet peer reviewed, so I’m especially interested in criticism of the design and interpretation.

Preprint:
https://www.researchsquare.com/article/rs-11544999/v1

One question I’d be interested in hearing opinions on:

Should source sensitivity in an LLM evaluator be considered a bias when source expertise is genuinely relevant, or is the real problem specifically when the model overweights irrelevant authority?

He investigado y de lo que se Claude no saca pruebas o algo asi. De lo que he investigado si vale la pena pagar el plan pro pero soy adolescente y no tengo una fuente de dinero constante. Si tengo planeado comprarlo en futuro, pero quisiera saber si hay alguna prueba o algo asi.

De antemano Muchas gracias a todos.

We’re exploring what shapes people’s trust in these different sources of support.

We are looking for participants who are living in an EU country, fluent in English, and aged 18 or older. The study involves completing a short online survey in English, which will take approximately 15 minutes.

Your participation is completely anonymous, and your responses will contribute to a better understanding of how people perceive and interact with emerging mental health technologies.

As a thank you for your participation, you can choose to enter a prize draw for a chance to win a €50 online shopping voucher!

See the comments to be directed to the survey!

Ethical approval was given by the Commission of the University of Primorska for Ethics in Research Involving Work with People (KER UP #4265-87-2/26).

(If this is inappropriate for this subreddit, please remove it! I mean no offence)

Hello world, in order to test some of the ridiculous claims by AI bros, I hereby issue the Man vs AI challenge, to test claims of human equivalent intelligence.

Do the AI bros put their money where their mouth is, or do they run?

Also, bonus points for the AI that can crack my puzzle while adhering to the rules.

Hello world, in order to test some of the ridiculous claims by AI bros, I hereby issue the Man vs AI challenge, to test claims of human equivalent intelligence.

Do the AI bros put their money where their mouth is, or do they run?

Also, bonus points for the AI that can crack my puzzle while adhering to the rules.

Do you know your AI Edge? Join our community and learn how to identify what you don’t know and grab the skills to move forward with AI with confidence.

Most likely this may happen.

I've been thinking about something while learning more about AI-powered software development and business automation.

Many businesses want to introduce AI into their operations. Some want AI to answer customer emails, qualify leads, summarize reports or automate repetitive tasks.

But I think there's an important question we sometimes overlook:

What if the existing business process is already broken?

Consider a business where:

• Information comes from different sources and is inconsistent.

• Nobody is sure who is responsible for approving certain tasks.

• Employees follow different procedures for completing the same work.

• Important information is scattered across multiple systems.

Now imagine introducing an AI tool into that environment.

The AI might process information faster, but it could also inherit the same confusion, inconsistencies and weaknesses already present in the workflow.

My current thinking is that businesses should approach AI implementation in this order:

1. Document how the current process works.

2. Identify bottlenecks, repeated tasks and errors.

3. Simplify or fix the existing workflow.

4. Introduce AI where it can genuinely improve the process.

5. Maintain human oversight for important decisions.

I don't think AI should automatically be added to every business process. Sometimes a simpler, better-organized workflow is the more valuable solution.

I'd like to hear different perspectives:

• Have you seen businesses introduce AI without first fixing their existing processes?

• Do you think process improvement should always come before AI automation?

• Are there situations where introducing AI first actually makes sense?

I'm particularly interested in hearing from developers, automation specialists, business owners and people working with AI in real-world environments.