most citedUnder the Surface: Tracking the Artifactuality of LLM-Generated Data

2 citations · 3 across the 3 of their papers we have counts for

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cs.CL2024★ 2 cited

Under the Surface: Tracking the Artifactuality of LLM-Generated Data

Debarati Das, Karin De Langis, Anna Martin-Boyle +14

This work delves into the expanding role of large language models (LLMs) in generating artificial data. LLMs are increasingly employed to create a variety of outputs, including ann…

cs.CL2024★ 1 cited

SelectLLM: Can LLMs Select Important Instructions to Annotate?

Ritik Sachin Parkar, Jaehyung Kim, Jong Inn Park +1

Instruction tuning benefits from large and diverse datasets; however, creating such datasets involves a high cost of human labeling. While synthetic datasets generated by large lan…