# Performance of diverse evaluation metrics in NLP-based assessment and text generation of consumer complaints

arXiv: https://arxiv.org/abs/2506.21623 · HTML: https://mlsanity.com/paper/2506.21623/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 23 Jun 2025.

## Builds on (most cited first, 8 of 8)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Can large language models replace humans in the systematic review process? Evaluating GPT-4's efficacy in screening and extracting data from peer-reviewed and grey literature in multiple languages](https://mlsanity.com/paper/2310.17526/) (2023)
- [GAN Computers Generate Arts? A Survey on Visual Arts, Music, and Literary Text Generation using Generative Adversarial Network](https://mlsanity.com/paper/2108.03857/) (2021)
- [Market-GAN: Adding Control to Financial Market Data Generation with Semantic Context](https://mlsanity.com/paper/2309.07708/) (2023)
- [Generating Long Financial Report using Conditional Variational Autoencoders with Knowledge Distillation](https://mlsanity.com/paper/2010.12188/) (2020)
- [NLP-based detection of systematic anomalies among the narratives of consumer complaints](https://mlsanity.com/paper/2308.11138/) (2023)
- [Feature-aware conditional GAN for category text generation](https://mlsanity.com/paper/2308.00939/) (2023)
- [Functional Correlations in the Pursuit of Performance Assessment of Classifiers](https://mlsanity.com/paper/1905.04667/) (2019)

## Built on by (most cited first, 2 of 2)

- [From Complaint Narratives to Monetary Relief: A Hybrid Machine Learning Framework for CFPB Consumer Complaints](https://mlsanity.com/paper/2606.22664/) (2026)
- [Sentiment Analysis Of Shopee Product Reviews Using Distilbert](https://mlsanity.com/paper/2511.22313/) (2025)

## Abstract (arXiv)

Machine learning (ML) has significantly advanced text classification by enabling automated understanding and categorization of complex, unstructured textual data. However, accurately capturing nuanced linguistic patterns and contextual variations inherent in natural language, particularly within consumer complaints, remains a challenge. This study addresses these issues by incorporating human-experience-trained algorithms that effectively recognize subtle semantic differences crucial for assessing consumer relief eligibility. Furthermore, we propose integrating synthetic data generation methods that utilize expert evaluations of generative adversarial networks and are refined through expert annotations. By combining expert-trained classifiers with high-quality synthetic data, our research seeks to significantly enhance machine learning classifier performance, reduce dataset acquisition costs, and improve overall evaluation metrics and robustness in text classification tasks.