AI Review Catalogues 257 Papers on Text Generation Tasks

A team led by Jonas Becker, Jan Philip Wahle, Bela Gipp, and Terry Ruas published a systematic literature review of text generation in the Journal of Artificial Intelligence Research (JAIR) on 2026/08/06. The study analyzes 257 papers covering January 2017 through December 2025 and classifies contributions into five core tasks: open‑ended generation, summarization, translation, paraphrasing, and question answering. It also surveys evaluation methods—model‑free, model‑based, and human assessment—and highlights task‑specific hurdles such as missing datasets for multi‑document summarization, lack of coherence in story generation, and challenges with complex reasoning in question answering. Across all sub‑tasks, nine common challenges emerge: bias, reasoning, hallucinations, misuse, privacy, interpretability, transparency, dataset availability, and computational cost.

The authors note that their taxonomy extends earlier work by Zhao et al. (2018), which first highlighted rapid growth in text‑generation publications; they broaden scope further back to 2017 and refine task granularity. They point out that bias concerns echo reinforcement learning from human feedback (RLHF) approaches introduced at OpenAI around 2019, raising questions about training data curation. Future research may focus on mitigating these biases through improved data selection techniques.

This comprehensive survey provides a clear map of how evaluation metrics such as BLEU scores or human preference rankings vary across tasks and often conflict with one another. By cataloguing the state of the field, it offers researchers a useful reference for designing new models and benchmarks while underscoring persistent gaps that still need attention.

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