mlsanityData to 14 Oct 2026

TegTok: Augmenting Text Generation via Task-specific and Open-world Knowledge

arXiv 2203.08517 ↗First posted 16 Mar 2022cs.CL cs.AIcited by 9

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Abstract (arXiv)

Generating natural and informative texts has been a long-standing problem in NLP. Much effort has been dedicated into incorporating pre-trained language models (PLMs) with various open-world knowledge, such as knowledge graphs or wiki pages. However, their ability to access and manipulate the task-specific knowledge is still limited on downstream tasks, as this type of knowledge is usually not well covered in PLMs and is hard to acquire. To address the problem, we propose augmenting TExt Generation via Task-specific and Open-world Knowledge (TegTok) in a unified framework. Our model selects knowledge entries from two types of knowledge sources through dense retrieval and then injects them into the input encoding and output decoding stages respectively on the basis of PLMs. With the help of these two types of knowledge, our model can learn what and how to generate. Experiments on two text generation tasks of dialogue generation and question generation, and on two datasets show that our method achieves better performance than various baseline models.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 42 of its 57 references that are in the map. First posted on arXiv 16 Mar 2022.

Builds on

42 of 57 references.

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30 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 3; 2025: 2; 2026: 1.

9 papers.

  1. 2024Corrective Retrieval Augmented Generationcited by 197
  2. 2023Retrieving Multimodal Information for Augmented Generation: A Surveycited by 85
  3. 2025RbFT: Robust Fine-tuning for Retrieval-Augmented Generation against Retrieval Defectscited by 27
  4. 2025MoC: Mixtures of Text Chunking Learners for Retrieval-Augmented Generation Systemcited by 19
  5. 2024Meta-Chunking: Learning Text Segmentation and Semantic Completion via Logical Perceptioncited by 17
  6. 2023Augmenting Black-box LLMs with Medical Textbooks for Biomedical Question Answeringcited by 11
  7. 2024Do You Know What You Are Talking About? Characterizing Query-Knowledge Relevance For Reliable Retrieval Augmented Generationcited by 2
  8. 2023DiffuSIA: A Spiral Interaction Architecture for Encoder-Decoder Text Diffusioncited by 1
  9. 2026QChunker: Learning Question-Aware Text Chunking for Domain RAG via Multi-Agent Debatecited by 0