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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 task
by nihit-desai 3y ago
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.
- natch 3y ago>compare each of their performance against supposed ground truth labels. Fixed it for you.
- nihit-desai 3y agoI 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/banking77 * https://huggingface.co/datasets/lex_glue/viewer/ledgar/train https://huggingface.co/datasets/lex_glue/viewer/ledgar/train * https://huggingface.co/datasets/squad_v2 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
- natch 3y agoAgreed, thanks for highlighting these links!
- morelisp 3y agoYou 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.