C o A nnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation (2023)

Abstract: Annotated data plays a critical role in Natural Language Processing (NLP) in training models and evaluating their performance. Given recent developments in Large Language Models (LLMs), models such as ChatGPT demonstrate zero-shot capability on many text-annotation tasks, comparable with or even exceeding human annotators. Such LLMs can serve as alternatives for manual annotation, due to lower costs and higher scalability. However, limited work has leveraged LLMs as complementary annotators, nor explored how annotation work is best allocated among humans and LLMs to achieve both quality and cost objectives. We propose CoAnnotating, a novel paradigm for Human-LLM co-annotation of unstructured texts at scale. Under this framework, we utilize uncertainty to estimate LLMs’ annotation capability. Our empirical study shows CoAnnotating to be an effective means to allocate work from results on different datasets, with up to 21% performance improvement over random baseline. For code implementation, see https://github.com/SALT-NLP/CoAnnotating.

## introduction labeled data plays a critical role in establishing benchmarks and developing models for natural language processing (nlp). although large language models (llms) like chatgpt have demonstrated their strong zero-shot performance in various tasks such as question answering, reasoning, natural language inference, sentiment analysis, and named entity recognition, results obtained by finetuned language models still outperform llms on most of these tasks @xcite @xcite . therefore, collecting labeled data for model training and fine-tuning is still valuable. instead of deploying llms directly for downstream uses, it is worthwhile to investigate how researchers can leverage llms' zero-shot capability in labeling text data to construct highquality datasets and improve the performance of fine-tuned models. typically, researchers recruit human annotators such as experts or crowd workers to perform data annotation @xcite . some challenges in manual annotation includes high costs of recruiting and training annotators, annotation inconsistency and human subjectivity @xcite . recent work explored how llms perform relative to crowd workers @xcite and results showed that it is possible for llms like chatgpt to replace large-scale manual annotation @xcite . in some cases, llms' annotation quality even outperforms human annotators on certain tasks @xcite . given the much lower annotation cost than crowd workers, llms are considered to have great poten-tial to increase the cost efficiency of the data annotation process. however, some studies also show that, relative to human performance, llms' zero-shot performance falls short on more difficult and pragmatic tasks @xcite . they suggest that practitioners should use caution when using llms to annotate data @xcite . such prior works view humans and llms as competitors, measuring the accuracy of llm labels as a replacement for human annotation, rather than considering how humans and llms might collaborate in an efficient manner. it is human-llm collaboration that motivates this work. we propose the coannotating framework, which aims to balance the complementary profiles of humans and llms in terms of their respective annotation quality and cost. our work tackles the problem of human-llm co-annotation from a resource allocation perspective. following @xcite , @xcite and @xcite , we consider model confidence as a reliable signal for the model's expected performance. as we consider allocating a given datapoint for an llm to annotate, we can use the inverse of the model's uncertainty to estimate our confidence in that allocation. under coannotating, we quantify llms' annotating expertise on the instance-level (estimating how well llms can annotate the given data point) beyond task-level (evaluating how llms performs on overall for each dataset). as such, a more informed allocation decision can be made with this fine-grained and contextualized instance-level perspective, rather than broad and coarse dataset-level expertise. we show that our proposed method using the uncertainty of responses can achieve a more efficient and more accurate work allocation than the random allocation baseline. our results also show that confidence scores generated by llms are generally well-calibrated but not always reliable. it is possible to outsource some annotation work to achieve human-level performance for more straightforward tasks like topic understanding. on the other hand, a tradeoff between annotation quality and annotation cost is inevitable for more nuanced tasks. our framework establishes a guide to effectively allocate ai and human efforts in collaborative annotation, and in doing so, it provides key insights into the capacities of llms, as well as the nature of the tasks and data that remain outside these capacities. ## coannotating framework our coannotating framework sets up a guide for annotating text data collaboratively (figure 2 ). for a given unlabeled train dataset d @xmath0, t 2 , ...t m } where t i is the i-th instance in the dataset, our framework automatically decides whether each data instance should be annotated by human or by the llms (section 3.3) by computing the uncertainty level of the llms's annotations for each instance (section 3.2), with the goal of achieving a higher annotation quality and a lower annotation cost for a given dataset (section 3.4). @xmath1: {sentence1} sentence2: {sentence2} prompt type please label if the following two sentences are paraphrases of each other. please give your answer as "paraphrase" or "not paraphrase". {text} instruction {text} please label if the two sentences above are paraphrases of each other. please give your answer as "paraphrase" or "not paraphrase". sequence swapping given the following two sentences, please classify the relationship of the following two sentences as "paraphrase" or "not paraphrase". {text} paraphrase is it true that the following two sentences are/are not paraphrases of each other? give your answer as "true" or "false". {text} true/false what relationship do the following two sentences have? is it "paraphrase" or "not paraphrase"? {text} question answering please choose one option that best describes the relationship between the following two sentences. {text} (a) paraphrase (b) not paraphrase multiple choice question i think the following two sentences are/are not paraphrases of each other. do you agree? {text} question with confirmation bias