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Re3val: Reinforced and Reranked Generative Retrieval (2401.16979v3)

Published 30 Jan 2024 in cs.IR
Re3val: Reinforced and Reranked Generative Retrieval

Abstract: Generative retrieval models encode pointers to information in a corpus as an index within the model's parameters. These models serve as part of a larger pipeline, where retrieved information conditions generation for knowledge-intensive NLP tasks. However, we identify two limitations: the generative retrieval does not account for contextual information. Secondly, the retrieval can't be tuned for the downstream readers as decoding the page title is a non-differentiable operation. This paper introduces Re3val, trained with generative reranking and reinforcement learning using limited data. Re3val leverages context acquired via Dense Passage Retrieval to rerank the retrieved page titles and utilizes REINFORCE to maximize rewards generated by constrained decoding. Additionally, we generate questions from our pre-training dataset to mitigate epistemic uncertainty and bridge the domain gap between the pre-training and fine-tuning datasets. Subsequently, we extract and rerank contexts from the KILT database using the rerank page titles. Upon grounding the top five reranked contexts, Re3val demonstrates the Top 1 KILT scores compared to all other generative retrieval models across five KILT datasets.

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An Examination of Re3val: Reinforced and Reranked Generative Retrieval

The paper entitled "Re3val: Reinforced and Reranked Generative Retrieval" introduces Re3val, a generative retrieval model aimed at enhancing the retrieval process for knowledge-intensive NLP tasks. The researchers focus on addressing limitations in generative retrieval, particularly around the use of contextual information and adaptability to downstream readers. They offer a methodology that integrates generative reranking and reinforcement learning, ambitious yet grounded in improving retrieval performance through systematic and theoretically sound means.

Generative retrieval models encode information pointers within their parameters, impacting how content is retrieved to support knowledge-intensive tasks. However, they typically do not account for contextual nuances or dynamically adjust to the needs of downstream readers, a gap Re3val intends to bridge. The method integrates contextual depth acquired via Dense Passage Retrieval (DPR) and employs REINFORCE for enhancing relevance through reward signals, ultimately seeking to minimize entropy in retrieved page titles and optimize retrieval outputs.

Innovative Contributions and Numerical Outcomes

The authors highlight the following key contributions of Re3val:

  • Entropy Minimization and Generative Reranking: By leveraging DPR-derived context, Re3val is able to rerank page titles more effectively. In doing so, it surpassed previous generative models like GENRE and CorpusBrain by an average of 1.9% in R-Precision over five tasks.
  • Integration of REINFORCE: The application of the REINFORCE algorithm for injecting reward signals into the decoding process enhances the model's retrieval accuracy. The model demonstrated an 8% improvement in R-Precision on average against zero-shot retrieval from CorpusBrain.
  • A New "Retrieve and Read" Framework: Re3val extracts contexts based on reranked page titles, achieving the best KILT scores among current generative models, with an average improvement of 2.1%.

These findings exemplify Re3val's practical capability to leverage question generation, reinforce learning during decoding, and facilitate rerank-informed document selection, all of which significantly enhance retrieval precision and relevance.

Implications and Future Directions

Re3val showcases potential theoretical and practical advances. Theoretically, its approach to integrating contextual understanding in generative models is noteworthy, challenging traditional retrieval paradigms by introducing reliable reranking mechanisms. Practically, the gains in retrieval efficacy highlighted in their results signal a meaningful step toward reducing data labeling efforts and computational overhead associated with NLP tasks.

Speculating on the future of AI developments, models like Re3val could see a synergy with expanding datasets and more sophisticated contextual understanding frameworks, aligning with increasingly data-hungry and context-aware applications. Further exploration into combinations of generative models with advanced reinforcement learning methodologies could extend the promise shown by Re3val, engendering a generation of retrieval systems deeply attuned to the nuances of data and task-specific demands. As retrieval becomes increasingly automated and pervasive in diverse applications, successes such as those demonstrated by Re3val mark significant steps forward in AI's continuous evolution.

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References (40)
  1. Autoregressive search engines: Generating substrings as document identifiers. In Advances in Neural Information Processing Systems, volume 35, pages 31668–31683. Curran Associates, Inc.
  2. Autoregressive entity retrieval. In International Conference on Learning Representations.
  3. Yllias Chali and Sadid A. Hasan. 2015. Towards topic-to-question generation. Computational Linguistics, 41(1):1–20.
  4. Reading Wikipedia to answer open-domain questions. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1870–1879, Vancouver, Canada. Association for Computational Linguistics.
  5. CorpusBrain: Pre-train a generative retrieval model for knowledge-intensive language tasks. In Proceedings of the 31st ACM International Conference on Information &\&& Knowledge Management. ACM.
  6. Scaling instruction-finetuned language models.
  7. Wizard of wikipedia: Knowledge-powered conversational agents. CoRR, abs/1811.01241.
  8. Question generation for question answering. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 866–874, Copenhagen, Denmark. Association for Computational Linguistics.
  9. Re2G: Retrieve, rerank, generate. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2701–2715, Seattle, United States. Association for Computational Linguistics.
  10. Retrieval augmented language model pre-training. In International conference on machine learning, pages 3929–3938. PMLR.
  11. Fid-light: Efficient and effective retrieval-augmented text generation. https://arxiv.org/pdf/2209.14290.pdf.
  12. Unsupervised dense information retrieval with contrastive learning. Transactions on Machine Learning Research.
  13. Gautier Izacard and Edouard Grave. 2021. 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, pages 874–880, Online. Association for Computational Linguistics.
  14. Atlas: Few-shot learning with retrieval augmented language models. arXiv preprint arXiv, 2208.
  15. Karen Johns. 1972. A statistical interpretation of term specificity and its application in retrieval.
  16. TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1601–1611, Vancouver, Canada. Association for Computational Linguistics.
  17. Dense passage retrieval for open-domain question answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6769–6781, Online. Association for Computational Linguistics.
  18. Alex Kendall and Yarin Gal. 2017. What uncertainties do we need in bayesian deep learning for computer vision? In 31st Conference on Neural Information Processing Systems.
  19. Natural questions: A benchmark for question answering research. Transactions of the Association for Computational Linguistics, 7:452–466.
  20. Deep questions without deep understanding. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 889–898, Beijing, China. Association for Computational Linguistics.
  21. FiD-ex: Improving sequence-to-sequence models for extractive rationale generation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 3712–3727, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
  22. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems, 33:9459–9474.
  23. Generation-augmented retrieval for open-domain question answering. 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), pages 4089–4100, Online. Association for Computational Linguistics.
  24. Asynchronous methods for deep reinforcement learning.
  25. Rodrigo Nogueira and Kyunghyun Cho. 2020. Passage re-ranking with bert.
  26. A deep reinforced model for abstractive summarization. In International Conference on Learning Representations.
  27. KILT: a benchmark for knowledge intensive language tasks. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2523–2544, Online. Association for Computational Linguistics.
  28. The probabilistic relevance framework: Bm25 and beyond. Foundations and Trends® in Information Retrieval, 3(4):333–389.
  29. In defense of cross-encoders for zero-shot retrieval.
  30. Improving passage retrieval with zero-shot question generation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 3781–3797, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
  31. Proximal policy optimization algorithms.
  32. Generating factoid questions with recurrent neural networks: The 30M factoid question-answer corpus. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 588–598, Berlin, Germany. Association for Computational Linguistics.
  33. C. E. Shannon. 1948. A mathematical theory of communication. In The Bell System Technical Journal.
  34. Transformer memory as a differentiable search index. In 36th Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, LA, USA.
  35. James Thorne. 2022. Data-efficient auto-regressive document retrieval for fact verification. In Proceedings of The Third Workshop on Simple and Efficient Natural Language Processing (SustaiNLP), pages 44–51, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
  36. FEVER: a large-scale dataset for fact extraction and VERification. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 809–819, New Orleans, Louisiana. Association for Computational Linguistics.
  37. A neural corpus indexer for document retrieval. In 36th Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, LA, USA.
  38. Ronald J. Williams. 1992. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach. Learn., 8(3–4):229–256.
  39. Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 38–45, Online. Association for Computational Linguistics.
  40. HotpotQA: A dataset for diverse, explainable multi-hop question answering. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2369–2380, Brussels, Belgium. Association for Computational Linguistics.
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Authors (5)
  1. EuiYul Song (3 papers)
  2. Sangryul Kim (8 papers)
  3. Haeju Lee (6 papers)
  4. Joonkee Kim (13 papers)
  5. James Thorne (48 papers)
Citations (7)
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