Large Language Models are overconfident and amplify human bias — mentioned by Joel_Mckay

Thu, 26 Mar 2026 19:54:14 GMT

Self-reported studies are arguably weaker evidence, but are common in some areas for ethics reasons. In general, if errors are truly random, than they will cancel out over larger/frequent population samples. The study conclusion inferred the skills needed to be effective at some task, are the same skills needed to correctly evaluate if you are actually proficient at the same tasks. https://arxiv.org/abs/2505.02151 If the data infers another explanation is more applicable, than I'd be interested in the primary papers/studies the editorialized opinion seems to have omitted. =3

Paper and discussion history. A mention is not an endorsement.

Large Language Models are overconfident and amplify human bias — mentioned by Joel_Mckay

Thu, 26 Mar 2026 06:38:52 GMT

No idea why we all get negative karma on this thread, as I do respect a cited source opinion even if we disagree. Do have a look around for papers rather than editorialized content in the future, and note account LLM agent output is a violation of YC usage policy. Have a great day =3 https://arxiv.org/abs/2505.02151

Paper and discussion history. A mention is not an endorsement.

Large Language Models are overconfident and amplify human bias · HackerPapers discussions

Large Language Models are overconfident and amplify human bias — mentioned by Joel_Mckay

Large Language Models are overconfident and amplify human bias — mentioned by Joel_Mckay