mlsanityData to 7 Oct 2026

The E2E Dataset: New Challenges For End-to-End Generation

arXiv 1706.09254 ↗First posted 28 Jun 2017cs.CLcited by 252

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Abstract (arXiv)

This paper describes the E2E data, a new dataset for training end-to-end, data-driven natural language generation systems in the restaurant domain, which is ten times bigger than existing, frequently used datasets in this area. The E2E dataset poses new challenges: (1) its human reference texts show more lexical richness and syntactic variation, including discourse phenomena; (2) generating from this set requires content selection. As such, learning from this dataset promises more natural, varied and less template-like system utterances. We also establish a baseline on this dataset, which illustrates some of the difficulties associated with this data.

Key facts. Cited by 252 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Jun 2017.

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Citing papers per year: 2021: 41; 2022: 46; 2023: 41; 2024: 60; 2025: 41; 2026: 23.

252 papers.

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  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  2. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  3. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,878
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  10. 2024LoRA Learns Less and Forgets Lesscited by 313
  11. 2023VeRA: Vector-based Random Matrix Adaptationcited by 294
  12. 2022Generating Sequences by Learning to Self-Correctcited by 271
38 more
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  4. 2022SSD-LM: Semi-autoregressive Simplex-based Diffusion Language Model for Text Generation and Modular Controlcited by 191
  5. 2022Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Textcited by 154
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  7. 2021Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Surveycited by 130
  8. 2024Mobile Edge Intelligence for Large Language Models: A Contemporary Surveycited by 125
  9. 2023Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learningcited by 115
  10. 2023FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Modelscited by 112
  11. 2022Latent Diffusion for Language Generationcited by 110
  12. 2021Competency Problems: On Finding and Removing Artifacts in Language Datacited by 91
  13. 2023SkipDecode: Autoregressive Skip Decoding with Batching and Caching for Efficient LLM Inferencecited by 89
  14. 2023Do Androids Know They're Only Dreaming of Electric Sheep?cited by 67
  15. 2024Parameter-Efficient Fine-Tuning with Discrete Fourier Transformcited by 67
  16. 2025A Systematic Survey of Automatic Prompt Optimization Techniquescited by 61
  17. 2024SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Modelscited by 60
  18. 2021HTLM: Hyper-Text Pre-Training and Prompting of Language Modelscited by 58
  19. 2022AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Modelcited by 57
  20. 2022Follow the Wisdom of the Crowd: Effective Text Generation via Minimum Bayes Risk Decodingcited by 54
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  23. 2022Exploring the Limits of Differentially Private Deep Learning with Group-wise Clippingcited by 52
  24. 2024VB-LoRA: Extreme Parameter Efficient Fine-Tuning with Vector Bankscited by 52
  25. 2024Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Modelscited by 49
  26. 2022When Does Differentially Private Learning Not Suffer in High Dimensions?cited by 48
  27. 2021Text-to-Table: A New Way of Information Extractioncited by 47
  28. 2024Recent Advances of Foundation Language Models-based Continual Learning: A Surveycited by 46
  29. 2023Decoder-Only or Encoder-Decoder? Interpreting Language Model as a Regularized Encoder-Decodercited by 44
  30. 2025Parameter-Efficient Fine-Tuning for Foundation Modelscited by 43
  31. 2021Controlling Hallucinations at Word Level in Data-to-Text Generationcited by 41
  32. 2023LayoutDiffusion: Improving Graphic Layout Generation by Discrete Diffusion Probabilistic Modelscited by 40
  33. 2023Empirical Analysis of the Strengths and Weaknesses of PEFT Techniques for LLMscited by 38
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  35. 2022GEMv2: Multilingual NLG Benchmarking in a Single Line of Codecited by 36
  36. 2023Why Is Public Pretraining Necessary for Private Model Training?cited by 36
  37. 2024ALoRA: Allocating Low-Rank Adaptation for Fine-tuning Large Language Modelscited by 36
  38. 2021Efficient (Soft) Q-Learning for Text Generation with Limited Good Datacited by 35

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