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Long Text Generation Challenge

arXiv 2306.02334 ↗First posted 4 Jun 2023cs.CLcited by 0

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Abstract (arXiv)

We propose a shared task of human-like long text generation, LTG Challenge, that asks models to output a consistent human-like long text (a Harry Potter generic audience fanfic in English), given a prompt of about 1000 tokens. We suggest a novel statistical metric of the text structuredness, GloVe Autocorrelations Power/ Exponential Law Mean Absolute Percentage Error Ratio (GAPELMAPER) and a human evaluation protocol. We hope that LTG can open new avenues for researchers to investigate sampling approaches, prompting strategies, autoregressive and non-autoregressive text generation architectures and break the barrier to generate consistent long (40K+ token) texts.

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References · 20

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  3. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,724
  4. 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,465
  5. 2021Multitask Prompted Training Enables Zero-Shot Task Generalizationcited by 1,470
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  10. 2021Efficient Attentions for Long Document Summarizationcited by 345
  11. 2021The GEM Benchmark: Natural Language Generation, its Evaluation and Metricscited by 239
  12. 2021QuALITY: Question Answering with Long Input Texts, Yes!cited by 208
8 more
  1. 2019Neural Text Summarization: A Critical Evaluationcited by 189
  2. 2022SCROLLS: Standardized CompaRison Over Long Language Sequencescited by 170
  3. 2021ContractNLI: A Dataset for Document-level Natural Language Inference for Contractscited by 148
  4. 2021SummScreen: A Dataset for Abstractive Screenplay Summarizationcited by 79
  5. 2023LongForm: Effective Instruction Tuning with Reverse Instructionscited by 47
  6. 2016Criticality in Formal Languages and Statistical Physicscited by 35
  7. 2021LOT: A Story-Centric Benchmark for Evaluating Chinese Long Text Understanding and Generationcited by 24
  8. 2023Autocorrelations Decay in Texts and Applicability Limits of Language Modelscited by 9

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