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LLMs make impressive graders-of-convenience, but their judgments swing wildly with prompt phrasing and option order. Treat them like noisy crowd-raters: randomi
by PrimordialEdg71 1y ago
LLMs make impressive graders-of-convenience, but their judgments swing wildly with prompt phrasing and option order. Treat them like noisy crowd-raters: randomize inputs, ensemble outputs, and keep a human in the loop whenever single-digit accuracy points matter.
- einrealist 1y ago"and keep a human in the loop whenever single-digit accuracy points matter" So we are supposed to give up on accuracy now? At least with humans (assuming good actors) I can assume an effort for accuracy and correctness. And I can build trust based on some resume and on former interaction. With LLMs, this is more like a coin-flip with each prompt. And since the models are updated constantly, its hard to build some sort of resume. In the meantime, people - especially casual users - might just trust outputs, because its convenient. A single digit error is harder to find. The costs of validating outputs increases with increased accuracy of LLMs. And casual users tend to skip validation because "its a computer and computers are correct". I fear an overall decrease in quality wherever LLMs are included. And any productivity gains are eaten by that.