ATGen: A Framework for Active Text Generation

TL;DR

ATGen introduces a comprehensive framework that integrates active learning with natural language generation, reducing annotation effort and costs while enabling benchmarking of AL strategies for NLG tasks.

Contribution

It presents a unified platform for applying and evaluating active learning strategies in text generation tasks using both human and LLM-based annotations.

Findings

ATGen reduces annotation effort and API call costs.

It supports deployment of LLMs as annotation agents.

The framework facilitates benchmarking of AL strategies in NLG.

Abstract

Active learning (AL) has demonstrated remarkable potential in reducing the annotation effort required for training machine learning models. However, despite the surging popularity of natural language generation (NLG) tasks in recent years, the application of AL to NLG has been limited. In this paper, we introduce Active Text Generation (ATGen) - a comprehensive framework that bridges AL with text generation tasks, enabling the application of state-of-the-art AL strategies to NLG. Our framework simplifies AL-empowered annotation in NLG tasks using both human annotators and automatic annotation agents based on large language models (LLMs). The framework supports LLMs deployed as services, such as ChatGPT and Claude, or operated on-premises. Furthermore, ATGen provides a unified platform for smooth implementation and benchmarking of novel AL strategies tailored to NLG tasks. Finally, we present evaluation results for state-of-the-art AL strategies across diverse settings and multiple text generation tasks. We show that ATGen reduces both the effort of human annotators and costs associated with API calls to LLM-based annotation agents. The code of the framework is available on GitHub under the MIT license. The video presentation is available at http://atgen-video.nlpresearch.group