mlsanityData to 13 Oct 2026

Natural Language Generation

arXiv 2503.16728 ↗First posted 20 Mar 2025cs.CLcited by 0

Not on the map. Why?

Abstract (arXiv)

This article provides a brief overview of the field of Natural Language Generation. The term Natural Language Generation (NLG), in its broadest definition, refers to the study of systems that verbalize some form of information through natural language. That information could be stored in a large database or knowledge graph (in data-to-text applications), but NLG researchers may also study summarisation (text-to-text) or image captioning (image-to-text), for example. As a subfield of Natural Language Processing, NLG is closely related to other sub-disciplines such as Machine Translation (MT) and Dialog Systems. Some NLG researchers exclude MT from their definition of the field, since there is no content selection involved where the system has to determine what to say. Conversely, dialog systems do not typically fall under the header of Natural Language Generation since NLG is just one component of dialog systems (the others being Natural Language Understanding and Dialog Management). However, with the rise of Large Language Models (LLMs), different subfields of Natural Language Processing have converged on similar methodologies for the production of natural language and the evaluation of automatically generated text.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 33 of its 80 references that are in the map. First posted on arXiv 20 Mar 2025.

Builds on

33 of 80 references.

References · 33

  1. 2017Attention Is All You Needcited by 60,106
  2. 2022Training language models to follow instructions with human feedbackcited by 15,856
  3. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,492
  4. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,765
  5. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,229
  6. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,594
  7. 2024The AI Scientist: Towards Fully Automated Open-Ended Scientific Discoverycited by 1,041
  8. 2023Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formattingcited by 797
  9. 2023Automatic Prompt Optimization with "Gradient Descent" and Beam Searchcited by 637
  10. 2020Evaluation of Text Generation: A Surveycited by 275
  11. 2024Is DPO Superior to PPO for LLM Alignment? A Comprehensive Studycited by 262
  12. 2017The E2E Dataset: New Challenges For End-to-End Generationcited by 253
21 more
  1. 2022Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Textcited by 154
  2. 2024LongWriter: Unleashing 10,000+ Word Generation from Long Context LLMscited by 142
  3. 2015Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systemscited by 134
  4. 2023Beyond One-Preference-Fits-All Alignment: Multi-Objective Direct Preference Optimizationcited by 123
  5. 2024The Prompt Report: A Systematic Survey of Prompt Engineering Techniquescited by 120
  6. 2023Evaluating the Social Impact of Generative AI Systems in Systems and Societycited by 91
  7. 2019Neural data-to-text generation: A comparison between pipeline and end-to-end architecturescited by 51
  8. 2024MetaAligner: Towards Generalizable Multi-Objective Alignment of Language Modelscited by 44
  9. 2024Take Caution in Using LLMs as Human Surrogates: Scylla Ex Machinacited by 29
  10. 2024Automatic Metrics in Natural Language Generation: A Survey of Current Evaluation Practicescited by 20
  11. 2021The Human Evaluation Datasheet 1.0: A Template for Recording Details of Human Evaluation Experiments in NLPcited by 19
  12. 2021Underreporting of errors in NLG output, and what to do about itcited by 19
  13. 2015A Survey and Classification of Controlled Natural Languagescited by 18
  14. 2015Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Rerankingcited by 17
  15. 2023The Iron(ic) Melting Pot: Reviewing Human Evaluation in Humour, Irony and Sarcasm Generationcited by 11
  16. 2024SLIDE: A Framework Integrating Small and Large Language Models for Open-Domain Dialogues Evaluationcited by 11
  17. 2022Innovations in Neural Data-to-text Generation: A Surveycited by 10
  18. 2024MMTE: Corpus and Metrics for Evaluating Machine Translation Quality of Metaphorical Languagecited by 10
  19. 2023Evaluating Open-Domain Dialogues in Latent Space with Next Sentence Prediction and Mutual Informationcited by 6
  20. 2025HPSS: Heuristic Prompting Strategy Search for LLM Evaluatorscited by 5
  21. 2025Dual use issues in the field of Natural Language Generationcited by 1

Built on by

Not cited yet.