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arXiv · 2404.17475

CEval: A Benchmark for Evaluating Counterfactual Text Generation

Van Bach Nguyen·Jörg Schlötterer·Christin Seifert
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Abstract

Counterfactual text generation aims to minimally change a text, such that it is classified differently. Judging advancements in method development for counterfactual text generation is hindered by a non-uniform usage of data sets and metrics in related work. We propose CEval, a benchmark for comparing counterfactual text generation methods. CEval unifies counterfactual and text quality metrics, includes common counterfactual datasets with human annotations, standard baselines (MICE, GDBA, CREST) and the open-source language model LLAMA-2. Our experiments found no perfect method for generating counterfactual text. Methods that excel at counterfactual metrics often produce lower-quality text while LLMs with simple prompts generate high-quality text but struggle with counterfactual criteria. By making CEval available as an open-source Python library, we encourage the community to contribute more methods and maintain consistent evaluation in future work.

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Van Bach Nguyen, Jörg Schlötterer, Christin Seifert. 2024-04-26. CEval: A Benchmark for Evaluating Counterfactual Text Generation. https://arxiv.org/abs/2404.17475

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