LLMs can label data as well as human annotators, but 20 times faster

Re:LLMs can label data as well as human annotators, but 20 t... by nihit-desai on Sunday June 18, 2023 @04:48PM (#36384301)

Hi, one of the authors here. Good question! For this benchmarking, we evaluated performance on popular open source text datasets across a few different NLP tasks (details in the report).

For each of these datasets, we specify task guidelines/prompts for the LLM and human annotators, and compare each of their performance against ground truth labels.

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Re:LLMs can label data as well as human annotators, but 20 t... by natch on Sunday June 18, 2023 @05:38PM (#36384778)

* * *

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Re:LLMs can label data as well as human annotators, but 20 t... by nihit-desai on Sunday June 18, 2023 @05:52PM (#36384902)

I mean, sure. For ground truth, we are using the labels that are part of the original dataset: * https://huggingface.co/datasets/banking77 * https://huggingface.co/datasets/lex_glue/viewer/ledgar/train * https://huggingface.co/datasets/squad_v2 ... (exhaustive set of links at the end of the report).

Is there some noise in these labels? Sure! But the relative performance with respect to these is still a valid evaluation

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Re:LLMs can label data as well as human annotators, but 20 t... by natch on Sunday June 18, 2023 @10:53PM (#36386826)

Agreed, thanks for highlighting these links!

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Re:LLMs can label data as well as human annotators, but 20 t... by morelisp on Sunday June 18, 2023 @05:49PM (#36384879)

You didn't answer the question at all, although to be fair the answer is both obvious and completely undermines your claim so I can see why you wouldn't.

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