▲ 151 ▼ Today's Large Language Models are Essentially BS Machines (quandyfactory.com) submitted 3 years ago by Veraticus@lib.lgbt to c/technology@beehaw.org 134 comments fedilink hide all child comments
[–] Zaktor@sopuli.xyz 0 points 3 years ago (1 child) That likely and correct are frequently the same shouldn’t blind us to the fact that correctness is a coincidence. That's an absurd statement. Do you have any experience with machine learning? permalink fedilink source parent hideshow 2 child comments replies: [–] Veraticus@lib.lgbt [S] 1 point 3 years ago (1 child) It isn't; I do; do you? permalink fedilink source parent hideshow 2 child comments replies: [–] Zaktor@sopuli.xyz 1 point 3 years ago (1 child) Yes, it's been my career for the last two decades and before that was the focus of my education. The idea that "correctness is a coincidence" is absurd and either fails to understand how training works or rejects the entire premise of large data revealing functional relationships in the underlying processes. permalink fedilink source parent hideshow 2 child comments replies: [–] Veraticus@lib.lgbt [S] 1 point 3 years ago Or you've simply misunderstood what I've said despite your two decades of experience and education. If you train a model on a bad dataset, will it give you correct data? If you ask a question a model it doesn't have enough data to be confident about an answer, will it still confidently give you a correct answer? And, more importantly, is it trained to offer CORRECT data, or is it trained to return words regardless of whether or not that data is correct? I mean, it's like you haven't even thought about this. permalink fedilink source parent
[–] Veraticus@lib.lgbt [S] 1 point 3 years ago (1 child) It isn't; I do; do you? permalink fedilink source parent hideshow 2 child comments replies: [–] Zaktor@sopuli.xyz 1 point 3 years ago (1 child) Yes, it's been my career for the last two decades and before that was the focus of my education. The idea that "correctness is a coincidence" is absurd and either fails to understand how training works or rejects the entire premise of large data revealing functional relationships in the underlying processes. permalink fedilink source parent hideshow 2 child comments replies: [–] Veraticus@lib.lgbt [S] 1 point 3 years ago Or you've simply misunderstood what I've said despite your two decades of experience and education. If you train a model on a bad dataset, will it give you correct data? If you ask a question a model it doesn't have enough data to be confident about an answer, will it still confidently give you a correct answer? And, more importantly, is it trained to offer CORRECT data, or is it trained to return words regardless of whether or not that data is correct? I mean, it's like you haven't even thought about this. permalink fedilink source parent
[–] Zaktor@sopuli.xyz 1 point 3 years ago (1 child) Yes, it's been my career for the last two decades and before that was the focus of my education. The idea that "correctness is a coincidence" is absurd and either fails to understand how training works or rejects the entire premise of large data revealing functional relationships in the underlying processes. permalink fedilink source parent hideshow 2 child comments replies: [–] Veraticus@lib.lgbt [S] 1 point 3 years ago Or you've simply misunderstood what I've said despite your two decades of experience and education. If you train a model on a bad dataset, will it give you correct data? If you ask a question a model it doesn't have enough data to be confident about an answer, will it still confidently give you a correct answer? And, more importantly, is it trained to offer CORRECT data, or is it trained to return words regardless of whether or not that data is correct? I mean, it's like you haven't even thought about this. permalink fedilink source parent
[–] Veraticus@lib.lgbt [S] 1 point 3 years ago Or you've simply misunderstood what I've said despite your two decades of experience and education. If you train a model on a bad dataset, will it give you correct data? If you ask a question a model it doesn't have enough data to be confident about an answer, will it still confidently give you a correct answer? And, more importantly, is it trained to offer CORRECT data, or is it trained to return words regardless of whether or not that data is correct? I mean, it's like you haven't even thought about this. permalink fedilink source parent