mlsanityData to 7 Oct 2026

Attribute Controlled Dialogue Prompting

arXiv 2307.05228 ↗First posted 11 Jul 2023cs.CL cs.LGcited by 3

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Abstract (arXiv)

Prompt-tuning has become an increasingly popular parameter-efficient method for adapting large pretrained language models to downstream tasks. However, both discrete prompting and continuous prompting assume fixed prompts for all data samples within a task, neglecting the fact that inputs vary greatly in some tasks such as open-domain dialogue generation. In this paper, we present a novel, instance-specific prompt-tuning algorithm for dialogue generation. Specifically, we generate prompts based on instance-level control code, rather than the conversation history, to explore their impact on controlled dialogue generation. Experiments on popular open-domain dialogue datasets, evaluated on both automated metrics and human evaluation, demonstrate that our method is superior to prompting baselines and comparable to fine-tuning with only 5%-6% of total parameters.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 27 of its 42 references that are in the map. First posted on arXiv 11 Jul 2023.

Builds on

27 of 42 references.

References · 27

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,878
  4. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,769
  5. 2021Calibrate Before Use: Improving Few-Shot Performance of Language Modelscited by 1,371
  6. 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,255
  7. 2015A Diversity-Promoting Objective Function for Neural Conversation Modelscited by 1,068
  8. 2019How Can We Know What Language Models Know?cited by 953
  9. 2020Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inferencecited by 934
  10. 2019DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generationcited by 842
  11. 2021GPT Understands, Toocited by 715
  12. 2020It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learnerscited by 668
15 more
  1. 2017DailyDialog: A Manually Labelled Multi-turn Dialogue Datasetcited by 649
  2. 2020Recipes for building an open-domain chatbotcited by 593
  3. 2021Learning How to Ask: Querying LMs with Mixtures of Soft Promptscited by 376
  4. 2020Parameter-Efficient Transfer Learning with Diff Pruningcited by 351
  5. 2018Generating Informative and Diverse Conversational Responses via Adversarial Information Maximizationcited by 114
  6. 2019ACUTE-EVAL: Improved Dialogue Evaluation with Optimized Questions and Multi-turn Comparisonscited by 83
  7. 2022Controllable Natural Language Generation with Contrastive Prefixescited by 75
  8. 2022HyperPrompt: Prompt-based Task-Conditioning of Transformerscited by 67
  9. 2019Investigating Evaluation of Open-Domain Dialogue Systems With Human Generated Multiple Referencescited by 48
  10. 2022IDPG: An Instance-Dependent Prompt Generation Methodcited by 43
  11. 2021Call for Customized Conversation: Customized Conversation Grounding Persona and Knowledgecited by 30
  12. 2022Tailor: A Prompt-Based Approach to Attribute-Based Controlled Text Generationcited by 22
  13. 2022Instance-aware Prompt Learning for Language Understanding and Generationcited by 18
  14. 2021Response Generation with Context-Aware Prompt Learningcited by 12
  15. 2021SideControl: Controlled Open-domain Dialogue Generation via Additive Side Networkscited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2024A Critical Look At Tokenwise Reward-Guided Text Generationcited by 12
  2. 2025Towards Cost-Effective Reward Guided Text Generationcited by 5
  3. 2026Controlled Attribute-Specific Summarization of Interrogative Dialoguescited by 0