Large Language Models for Data Annotation and Synthesis: A Survey (2024)

Abstract: Data annotation and synthesis generally refers to the labeling or generating of raw data with relevant information, which could be used for improving the efficacy of machine learning models. The process, however, is labor-intensive and costly. The emergence of advanced Large Language Models (LLMs), exemplified by GPT-4, presents an unprecedented opportunity to automate the complicated process of data annotation and synthesis. While existing surveys have extensively covered LLM architecture, training, and general applications, we uniquely focus on their specific utility for data annotation. This survey contributes to three core aspects: LLM-Based Annotation Generation, LLM-Generated Annotations Assessment, and LLM-Generated Annotations Utilization. Furthermore, this survey includes an in-depth taxonomy of data types that LLMs can annotate, a comprehensive review of learning strategies for models utilizing LLM-generated annotations, and a detailed discussion of the primary challenges and limitations associated with using LLMs for data annotation and synthesis. Serving as a key guide, this survey aims to assist researchers and practitioners in exploring the potential of the latest LLMs for data annotation, thereby fostering future advancements in this critical field.

## introduction in the complex realm of machine learning and natural language processing (nlp), data annotation and synthesis stand out as a critical yet challenging task, extending beyond simple label attachment to encompass a diverse array of fundamental or auxiliary information. this detailed process typically involves ❶ categorizing raw data with class or task labels for basic classification, ❷ adding intermediate labels for contextual depth @xcite , ❸ assigning confidence scores to assess annotation reliability @xcite , ❹ applying alignment or * equal contribution. despite its wide applications, data annotation and synthesis poses significant challenges for current machine learning models due to the complexity, subjectivity, and diversity of data @xcite . this process requires domain expertise and is resource-intensive, particularly when manually labeling or creating large datasets. advanced llms such as gpt-4 (openai, 2023), gemini @xcite , and llama-2 @xcite offer a promising opportunity to revolutionize data annotation. llms serve as more than just tools but play a crucial role in improving the effectiveness and precision of data annotation. their ability to automate annotation tasks @xcite , ensure consistency across large volumes of data @xcite , and adapt through finetuning or prompting for specific domains @xcite , significantly mitigates the challenges encountered with traditional annotation and synthesis methods, setting a new standard for what is achievable in the realm of nlp. this survey delves into the nuances of using llms for data annotation and synthesis, exploring methodologies, utilizing strategies, and associated challenges in this transformative approach. through this exploration, we aim to shed light on the motivations behind embracing llms as catalysts for redefining the landscape of data annotation and synthesis in machine learning and nlp. we explore the utilization of llms for annotation synthesis in this survey, making four main contributions: focusing on this underrepresented aspect of llm application, the survey aims to serve as a valuable guide for academics and practitioners who intend to deploy llms for annotation purposes. note that in this survey, we primarily focus on pure language models and do not extensively cover recently emerging multimodal llms, such as llava @xcite . figure 1 illustrates the general structure of this survey. additionally, a list of potential tools for utilizing llms for annotation is included in appendix a, along with explanatory examples. differences from other llm-related surveys. while existing surveys in the nlp domain extensively cover architectural nuances @xcite , training methodologies @xcite , and evaluation protocols @xcite associated with llms, their main focus lies on the capabilities of models for specific end tasks such as machine translation @xcite , alignment @xcite , code generation @xcite , and medical analysis @xcite . in contrast, this survey distinguishes itself by focusing primarily on the application of these potent next-generation llms to the intricate realm of annotation synthesis, a domain that is crucial yet underexplored. ## preliminaries in this section, we delve into our approach to the annotation synthesis process. we introduce two core models: an annotator model, denoted as a, which maps input data to annotations, and a task learner, represented as l, that utilizes or learns from these annotated data to accomplish specific tasks. our primary focus is on utilizing advanced llms like @xcite and llama @xcite as annotators (a), while the task learner (l) can be another large model @xcite or a less complex one such as bert @xcite , which utilizes these annotated data to perform designated tasks. llm-generated annotations encompass categorical labels and enhance raw data points with a comprehensive array of auxiliary signals. these annotations, including confidence scores, contextual details, and other metadata, extend beyond traditional categorical labels. ## llm-based annotation generation the emergence of llms has sparked significant interest in their capacity for high-quality, contextsensitive annotation synthesis. this section discusses various kinds of annotations and data produced via llms. ## llm-generated annotations assessment effective evaluation of annotations generated by llms is crucial to fully harness their potential. this section focuses on two main aspects: ## llm-generated annotations utilization llm-generated annotations provide a valuable resource of labeled data for nlp models in different stages. hereby we explore the methods for utilizing and learning with llm-generated annotations. ## societal impact and future work in this section, we outline llm annotation challenges, including societal implications, technical concerns, and bias propagation. ## conclusion the exploration of llms for data annotation and synthesis has revealed an exciting frontier in nlp, presenting novel solutions to longstanding challenges like data scarcity, and enhancing annotation quality and process efficiency. this survey meticulously reviews methodologies, applications, and hurdles associated with llm employment, including detailed taxonomy from annotation generation to utilization. it evaluates the effects of llmgenerated annotations on training machine learning models while addressing both technical and ethical concerns like bias and societal ramifications. highlighting our novel taxonomy of llm methodologies, strategies for utilizing llm-generated annotations, and a critical discussion on the challenges, this work aims to steer future progress in this crucial area. additionally, we introduce a comprehensive categorization of techniques and compile extensive benchmark datasets to support ongoing research endeavors, concluding with an examination of persistent challenges and open questions, paving the way for future investigative pursuits in the domain.