mlsanityData to 14 Oct 2026

LAD: Language Models as Data for Zero-Shot Dialog

arXiv 2207.14393 ↗First posted 28 Jul 2022cs.CL cs.AIcited by 18

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Abstract (arXiv)

To facilitate zero-shot generalization in taskoriented dialog, this paper proposes Language Models as Data (LAD). LAD is a paradigm for creating diverse and accurate synthetic data which conveys the necessary structural constraints and can be used to train a downstream neural dialog model. LAD leverages GPT-3 to induce linguistic diversity. LAD achieves significant performance gains in zero-shot settings on intent prediction (+15%), slot filling (+31.4 F-1) and next action prediction (+11 F1). Furthermore, an interactive human evaluation shows that training with LAD is competitive with training on human dialogs. LAD is open-sourced, with the code and data available at https://github.com/Shikib/lad.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 31 of its 54 references that are in the map. First posted on arXiv 28 Jul 2022.

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31 of 54 references.

References · 31

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  4. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  5. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,795
  6. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,780
  7. 2019DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generationcited by 842
  8. 2018MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modellingcited by 766
  9. 2021Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigmcited by 606
  10. 2019Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Datasetcited by 434
  11. 2020Efficient Intent Detection with Dual Sentence Encoderscited by 433
  12. 2021A Survey of Data Augmentation Approaches for NLPcited by 409
19 more
  1. 2019An Evaluation Dataset for Intent Classification and Out-of-Scope Predictioncited by 386
  2. 2021GPT3Mix: Leveraging Large-scale Language Models for Text Augmentationcited by 187
  3. 2021Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Littlecited by 177
  4. 2019Benchmarking Natural Language Understanding Services for building Conversational Agentscited by 166
  5. 2020DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialoguecited by 89
  6. 2021Towards Zero-Label Language Learningcited by 76
  7. 2020SOLOIST: Building Task Bots at Scale with Transfer Learning and Machine Teachingcited by 71
  8. 2021Few-Shot Bot: Prompt-Based Learning for Dialogue Systemscited by 64
  9. 2016A User Simulator for Task-Completion Dialoguescited by 57
  10. 2019How to Build User Simulators to Train RL-based Dialog Systemscited by 46
  11. 2020STAR: A Schema-Guided Dialog Dataset for Transfer Learningcited by 44
  12. 2018Neural User Simulation for Corpus-based Policy Optimisation for Spoken Dialogue Systemscited by 42
  13. 2020Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representationscited by 42
  14. 2020Coach: A Coarse-to-Fine Approach for Cross-domain Slot Fillingcited by 38
  15. 2020Example-Driven Intent Prediction with Observerscited by 26
  16. 2021Domain-independent User Simulation with Transformers for Task-oriented Dialogue Systemscited by 23
  17. 2020ConVEx: Data-Efficient and Few-Shot Slot Labelingcited by 22
  18. 2021Schema-Guided Paradigm for Zero-Shot Dialogcited by 14
  19. 2021GenSF: Simultaneous Adaptation of Generative Pre-trained Models and Slot Fillingcited by 11

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Citing papers per year: 2023: 8; 2024: 7; 2025: 3.

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  1. 2023PLACES: Prompting Language Models for Social Conversation Synthesiscited by 72
  2. 2023Leveraging Large Language Models in Conversational Recommender Systemscited by 63
  3. 2023Leveraging Large Language Models to Power Chatbots for Collecting User Self-Reported Datacited by 48
  4. 2023Faithful Persona-based Conversational Dataset Generation with Large Language Modelscited by 47
  5. 2023SGP-TOD: Building Task Bots Effortlessly via Schema-Guided LLM Promptingcited by 39
  6. 2023Controllable Mixed-Initiative Dialogue Generation through Promptingcited by 32
  7. 2023COKE: A Cognitive Knowledge Graph for Machine Theory of Mindcited by 27
  8. 2024SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Trackingcited by 17
  9. 2024PersonalityChat: Conversation Distillation for Personalized Dialog Modeling with Facts and Traitscited by 6
  10. 2024Efficient Data Generation for Source-grounded Information-seeking Dialogs: A Use Case for Meeting Transcriptscited by 6
  11. 2024Diverse and Effective Synthetic Data Generation for Adaptable Zero-Shot Dialogue State Trackingcited by 6
  12. 2025Mutual Reinforcement of LLM Dialogue Synthesis and Summarization Capabilities for Few-Shot Dialogue Summarizationcited by 5
6 more
  1. 2024Controllable and Diverse Data Augmentation with Large Language Model for Low-Resource Open-Domain Dialogue Generationcited by 4
  2. 2023Data Augmentation for Conversational AIcited by 3
  3. 2024Making Task-Oriented Dialogue Datasets More Natural by Synthetically Generating Indirect User Requestscited by 3
  4. 2025Generative Induction of Dialogue Task Schemas with Streaming Refinement and Simulated Interactionscited by 2
  5. 2024Comparing Data Augmentation Methods for End-to-End Task-Oriented Dialog Systemscited by 0
  6. 2025MonoTODia: Translating Monologue Requests to Task-Oriented Dialoguescited by 0