Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants

By Max Bartolo, Tristan Thrush, Sebastian Riedel, Pontus Stenetorp, Robin Jia, Douwe Kiela. 2021

39 citations recorded by the source corpus.

In Dynamic Adversarial Data Collection (DADC), human annotators are tasked with finding examples that models struggle to predict correctly. Models trained on DADC-collected training data have been shown to be more robust in adversarial and out-of-domain settings, and are considerably harder for humans to fool. However, DADC is more time-consuming than traditional data collection and thus more costly per annotated example. In this work, we examine whether we can maintain the advantages of DADC, without incurring the additional cost. To that end, we introduce Generative Annotation Assistants (GAAs), generator-in-the-loop models that provide real-time suggestions that annotators can either approve, modify, or reject entirely. We collect training datasets in twenty experimental settings and perform a detailed analysis of this approach for the task of extractive question answering (QA) for both standard and adversarial data collection. We demonstrate that GAAs provide significant efficiency benefits with over a 30% annotation speed-up, while leading to over a 5x improvement in model fooling rates. In addition, we find that using GAA-assisted training data leads to higher downstream model performance on a variety of question answering tasks over adversarial data collection.

Subfields: Building and Construction, Computer Science Applications, Transportation, Natural Language Processing

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Citations in the corpus

Red Teaming Language Models with Language Models (2022, background): “Bartolo et al. (2021a) learn to generate test cases but using ∼50k manuallywritten examples.”
Adversarial Training for High-Stakes Reliability (2022, neutral): “[42] augmented their human adversaries for an adversarial QA task using a generative model to suggest questions.”
WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation (2022, methodology): “Dataset generation Another recent approach leverages language models toward fully automatic dataset creation (Schick and Schütze, 2021; Wu et al., 2022; West et al., 2021; Bartolo et al., 2021a, i.a.).”
Jailbreaking Black Box Large Language Models in Twenty Queries (2023, unknown): “While there have been efforts to automate the generation of prompt-level jailbreaks, these methods require prompt engineering [23], manually-generated test cases [3], or retraining large generative models on objectionable text [4], all of which hinders the widespread deployment of these techniques.”