Summary of Ceval: a Benchmark For Evaluating Counterfactual Text Generation, by Van Bach Nguyen et al.
CEval: A Benchmark for Evaluating Counterfactual Text Generation
by Van Bach Nguyen, Jörg Schlötterer, Christin Seifert
First submitted to arxiv on: 26 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Counterfactual text generation aims to modify a text to change its classification. The field’s advancement is hampered by inconsistent use of datasets and metrics across related work. To address this, 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 single perfect method for generating counterfactual text. Methods excelling at counterfactual metrics often produce lower-quality text, while LLMs with simple prompts generate high-quality text but struggle with counterfactual criteria. CEval is an open-source Python library, encouraging the community to contribute more methods and maintain consistent evaluation in future work. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers are trying to make computers better at changing texts so they get classified differently. The problem is that different groups use different ways to measure how well these changes work. To fix this, we created a tool called CEval that helps compare different methods for making these text changes. CEval uses the same way of measuring quality and includes examples of common texts with human annotations. We tested some existing methods and found that there isn’t one perfect way to make counterfactual texts. Some methods are good at changing the classification but bad at producing high-quality texts, while others are good at making high-quality texts but struggle to change the classification. CEval is open-source, so we hope it will help other researchers contribute their own methods and make progress in this area. |
Keywords
» Artificial intelligence » Classification » Language model » Llama » Text generation
