Skip to main content
SpringerLink
Log in

Prompt and Instruction-Based Tuning for Response Generation in Conversational Question Answering

  • Conference paper
  • First Online:
Natural Language Processing and Information Systems (NLDB 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13913))

Abstract

In recent years, prompt-based tuning and instruction-based tuning have emerged as popular approaches for natural language processing. In this paper, we investigate the application of prompt and instruction-based tuning approaches for response generation in conversational question answering. We approach this task from both extractive and generative angles, where we adopt prompt-based tuning for the extractive angle and instruction-based tuning for the generative angle. Additionally, we utilize multi-task learning to integrate these two angles. To evaluate the performance of our proposed approaches, we conduct experiments on the GPT-2 model. The results show that the approaches improve performance by \(18\%\) on F1 score over the baseline. We share our codes and data for reproducibility. (https://github.com/yujie-xing/Multi-Turn_QA_Prompt).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://huggingface.co/.

  2. 2.

    https://huggingface.co/gpt2.

References

  1. Bragg, J., Cohan, A., Lo, K., Beltagy, I.: FLEX: unifying evaluation for few-shot NLP. Adv. Neural. Inf. Process. Syst. 34, 15787–15800 (2021)

    Google Scholar 

  2. Chada, R., Natarajan, P.: FewshotQA: a simple framework for few-shot learning of question answering tasks using pre-trained text-to-text models. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 6081–6090 (2021)

    Google Scholar 

  3. Chen, X., Zhang, Y., Deng, J., Jiang, J.Y., Wang, W.: Gotta: generative few-shot question answering by prompt-based cloze data augmentation. In: Proceedings of the 2023 SIAM International Conference on Data Mining (SDM) (2023)

    Google Scholar 

  4. Deng, Y., et al.: Multi-task learning with multi-view attention for answer selection and knowledge base question answering. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 6318–6325 (2019)

    Google Scholar 

  5. Gekhman, Z., Oved, N., Keller, O., Szpektor, I., Reichart, R.: On the robustness of dialogue history representation in conversational question answering: a comprehensive study and a new prompt-based method. arXiv preprint arXiv:2206.14796 (2022)

  6. Gupta, P., Jiao, C., Yeh, Y.T., Mehri, S., Eskenazi, M., Bigham, J.: InstructDial: improving zero and few-shot generalization in dialogue through instruction tuning. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, UAE, pp. 505–525. Association for Computational Linguistics (2022). https://aclanthology.org/2022.emnlp-main.33

  7. Izacard, G., Grave, É.: Leveraging passage retrieval with generative models for open domain question answering. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp. 874–880 (2021)

    Google Scholar 

  8. Joshi, M., Chen, D., Liu, Y., Weld, D.S., Zettlemoyer, L., Levy, O.: SpanBERT: improving pre-training by representing and predicting spans. Trans. Assoc. Comput. Linguist. 8, 64–77 (2020)

    Article  Google Scholar 

  9. Khashabi, D., Baral, C., Choi, Y., Hajishirzi, H.: Reframing instructional prompts to GPTk’s language. In: Findings of the Association for Computational Linguistics: ACL 2022, pp. 589–612 (2022)

    Google Scholar 

  10. Khashabi, D., et al.: UnifiedQA: crossing format boundaries with a single QA system. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 1896–1907 (2020)

    Google Scholar 

  11. Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., Soricut, R.: ALBERT: a lite BERT for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942 (2019)

  12. Lewis, P., et al.: Retrieval-augmented generation for knowledge-intensive NLP tasks. Adv. Neural. Inf. Process. Syst. 33, 9459–9474 (2020)

    Google Scholar 

  13. Min, S., Lewis, M., Zettlemoyer, L., Hajishirzi, H.: MetaICL: learning to learn in context. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 2791–2809 (2022)

    Google Scholar 

  14. Min, S., et al.: Rethinking the role of demonstrations: what makes in-context learning work? arXiv preprint arXiv:2202.12837 (2022)

  15. Ouyang, L., et al.: Training language models to follow instructions with human feedback. Adv. Neural. Inf. Process. Syst. 35, 27730–27744 (2022)

    Google Scholar 

  16. Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. Advances Neural Inf. Process. Syst. 32 (2019)

    Google Scholar 

  17. Prasad, A., Hase, P., Zhou, X., Bansal, M.: GrIPS: gradient-free, edit-based instruction search for prompting large language models. arXiv preprint arXiv:2203.07281 (2022)

  18. Puri, R.S., Mishra, S., Parmar, M., Baral, C.: How many data samples is an additional instruction worth? arXiv preprint arXiv:2203.09161 (2022)

  19. Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21(1), 5485–5551 (2020)

    MathSciNet  MATH  Google Scholar 

  20. Ram, O., Kirstain, Y., Berant, J., Globerson, A., Levy, O.: Few-shot question answering by pretraining span selection. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 3066–3079 (2021)

    Google Scholar 

  21. Reddy, S., Chen, D., Manning, C.D.: CoQA: a conversational question answering challenge. Trans. Assoc. Comput. Linguist. 7, 249–266 (2019). https://aclanthology.org/Q19-1016

  22. Shen, T., et al.: Multi-task learning for conversational question answering over a large-scale knowledge base. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 2442–2451 (2019)

    Google Scholar 

  23. Själander, M., Jahre, M., Tufte, G., Reissmann, N.: EPIC: an energy-efficient, high-performance GPGPU computing research infrastructure (2019)

    Google Scholar 

  24. Tafjord, O., Clark, P.: General-purpose question-answering with macaw. arXiv preprint arXiv:2109.02593 (2021)

  25. Wei, J., et al.: Finetuned language models are zero-shot learners. In: International Conference on Learning Representations (2022)

    Google Scholar 

  26. Weller, O., Lourie, N., Gardner, M., Peters, M.E.: Learning from task descriptions. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1361–1375 (2020)

    Google Scholar 

  27. Xiong, W., et al.: Answering complex open-domain questions with multi-hop dense retrieval. In: International Conference on Learning Representations (2021)

    Google Scholar 

  28. Xu, H., et al.: ZeroPrompt: scaling prompt-based pretraining to 1,000 tasks improves zero-shot generalization. arXiv preprint arXiv:2201.06910 (2022)

  29. Zhong, W., et al.: ProQA: structural prompt-based pre-training for unified question answering. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Seattle, USA, pp. 4230–4243. Association for Computational Linguistics (2022). https://aclanthology.org/2022.naacl-main.313

  30. Zhu, C., Zeng, M., Huang, X.: SDNet: contextualized attention-based deep network for conversational question answering. arXiv preprint arXiv:1812.03593 (2018)

Download references

Acknowledgements

This paper is funded by the collaborative project of DNB ASA and Norwegian University of Science and Technology (NTNU). We also received assistance on computing resources from the IDUN cluster of NTNU [23]. We would like to thank Jon Atle Gulla for his helpful comments.

Author information

Authors and Affiliations

  1. Norwegian University of Science and Technology, Trondheim, Norway

    Yujie Xing & Peng Liu

Authors
  1. Yujie Xing
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yujie Xing .

Editor information

Editors and Affiliations

  1. Conservatoire National des Arts et Métiers, Paris, France

    Elisabeth Métais

  2. University of Derby, Derby, UK

    Farid Meziane

  3. Oakland University, Rochester, NY, USA

    Vijayan Sugumaran

  4. University of Derby, Derby, UK

    Warren Manning

  5. University of Derby, Derby, UK

    Stephan Reiff-Marganiec

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Xing, Y., Liu, P. (2023). Prompt and Instruction-Based Tuning for Response Generation in Conversational Question Answering. In: Métais, E., Meziane, F., Sugumaran, V., Manning, W., Reiff-Marganiec, S. (eds) Natural Language Processing and Information Systems. NLDB 2023. Lecture Notes in Computer Science, vol 13913. Springer, Cham. https://doi.org/10.1007/978-3-031-35320-8_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-35320-8_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-35319-2

  • Online ISBN: 978-3-031-35320-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation