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

TL;DR

This paper investigates the quality and implications of various types of LLM-generated artificial data, revealing hidden disparities compared to human data, especially in complex tasks, and emphasizing ethical considerations in data creation.

Contribution

First comprehensive analysis aggregating diverse LLM-generated text data and evaluating its quality against human data across multiple benchmarks.

Findings

LLM-generated data can match human performance in some tasks

Significant disparities exist in complex tasks involving nuanced understanding

Highlights ethical concerns and biases in LLM-generated content

Abstract

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 annotations, preferences, instruction prompts, simulated dialogues, and free text. As these forms of LLM-generated data often intersect in their application, they exert mutual influence on each other and raise significant concerns about the quality and diversity of the artificial data incorporated into training cycles, leading to an artificial data ecosystem. To the best of our knowledge, this is the first study to aggregate various types of LLM-generated text data, from more tightly constrained data like "task labels" to more lightly constrained "free-form text". We then stress test the quality and implications of LLM-generated artificial data, comparing it with human data across various existing benchmarks. Despite artificial data's capability to match human performance, this paper reveals significant hidden disparities, especially in complex tasks where LLMs often miss the nuanced understanding of intrinsic human-generated content. This study critically examines diverse LLM-generated data and emphasizes the need for ethical practices in data creation and when using LLMs. It highlights the LLMs' shortcomings in replicating human traits and behaviors, underscoring the importance of addressing biases and artifacts produced in LLM-generated content for future research and development. All data and code are available on our project page.