Live data from Hacker News

AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights

arxiv.org

1–10 of 190 posts

Re: AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights

#6
post #3

Another way to phrase this might be that LLMs make better resumes no?

In text generation, LLM language is full of very emphatic phrases. At a surface level it might sound stronger. But as a human reader, it's not necessarily better

Re: AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights

#7
I wonder if this extends to training models on new content as well. Are we creating a cyclical information-consumption and training situation in which models being trained are more likely to pick up on and reference content created by themselves or by other LLMs than by other humans?

Re: AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights

#8
post #3

Another way to phrase this might be that LLMs make better resumes no?

You'd have to define "better".

All this shows is that LLMs generate resumes that fit the heuristics LLMs use to judge resumes. And that makes sense, but isn't necessarily a given.

Re: AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights

#10
post #3

Another way to phrase this might be that LLMs make better resumes no?

Or in other words: LLM it is optimizing function which is generated by same LLM, think you have random variable y, where generator sin(x+r) and your optimizer trying to fit function sin(x+unkown1) + unknown2 ("unknown" function) - it is obvious that will find best fit.
Post reply on HN