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Comment by XenophileJKO | Hacker News Reader

So.. a while ago I was experimenting with using random wikipedia pages and then asking the model to think about concepts, trends or connections between a few pages..

Then I would ask it the actual brain storming problem I had.

In practice it worked pretty well to get ideas that were not the default ideas that the models will come up with.

Now though, you can also seed with anything really and the agents can do the researching. The research itself will probably push the solutions into novel spaces.

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You mean seed the context with random information then ask it to ideate on something totally unrelated?

Like you want it to figure out how to get som information in front of potential customers in a cost effective way but first you give it articles on Witchita, Kansas, the curling iron, and List of Italian Brands?

Yes, I havent seen research on this tactic specifically for language models but for generative image models you can find research thatindicates you get far more variety and diversity in the images returned when suffixing the context with "noisy context" such as random letters or something else unrelated that wont "confuse" it or make it go off the rails

That's how I understood it! Clever hack. You'll get more varied ideas by using many different seeds, than if you didn't use a seed and just asked the LLM in a fresh context.

Reminds of Freudian dream analysis.

The actual content of the dream is meaningless, lacks symbolism, but it prompts the patient to think deeply about personal feelings and memories that might otherwise not surface to conscious level.

At some point you or the agent has to pick the one it thinks will land with the target market.

Some markets you can brute force, like mass-mailing and online display ads, and hope you find something that converts sooner or later.

So I'd suggest people find areas to play in where that doesn't really work. Where selection cost and changing cost are high, and sales is played on extra-hard mode. (Tricky thing is... those sales are hard, of course.)

If you want to beat the model you have to go where feedback isn't fast enough for it to converge on the thing that resonates with actual people before they write it off.

There's another way to ask your problem to something that's thought about concepts, trends and connections.

That aside, interesting approach. I wonder if the quality of the sample makes a big impact. Does it help if the articles are entirely disparate, or related? Does it help if the articles are related or unrelated to the problem?

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