FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation
Open accessAbstract
Retrieval-augmented generation models offer many benefits over standalone language models: besides a textual answer to a given query they provide provenance items retrieved from an updateable knowledge base. However, they are also more complex systems and need to handle long inputs. In this work, we introduce FiD-Light to strongly increase the efficiency of the state-of-the-art retrieval-augmented FiD model, while maintaining the same level of effectiveness. Our FiD-Light model constrains the information flow from the encoder (which encodes passages separately) to the decoder (using concatenated encoded representations). Furthermore, we adapt FiD-Light with re-ranking capabilities through textual source pointers, to improve the top-ranked provenance precision. Our experiments on a diverse set of seven knowledge intensive tasks (KILT) show FiD-Light consistently improves the Pareto frontier between query latency and effectiveness. FiD-Light with source pointing sets substantial new state-of-the-art results on six KILT tasks for combined text generation and provenance retrieval evaluation, while maintaining high efficiency.
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Cites
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929 · see citing works
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988 · cited 1× · see citing works
“A major component, besides concerns about safety and social biases as summarized by Bender et al. (2021), is the efficiency of the deployed system.”
BackgroundNatural Questions: A benchmark for question answering research
Kwiatkowski, T., Palomaki, J., Redfield, O., Collins, M., Parikh, A., Alberti, C., Epstein, D., Polosukhin, I., Devlin, J., Lee, K., et al. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00276 · Cited by 2,069 · see citing works
Zero-Shot Relation Extraction via Reading Comprehension
Levy, O., Seo, M., Choi, E., & Zettlemoyer, L. (2017) · Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017) · doi:10.18653/v1/k17-1034 · Cited by 482 · cited 2× · see citing works
“…experiments on 7 KILT tasks: HotpotQA (Yang et al., 2018), TriviaQA (Joshi et al., 2017), Natural Questions (NQ) (Kwiatkowski et al., 2019), T-REx (Elsahar et al., 2018), Zero Shot RE (zsRE) (Levy et al., 2017), FEVER (Thorne et al., 2018), and Wizard of Wikipedia (WoW) (Dinan et al., 2018).”
MethodMeasuring Attribution in Natural Language Generation Models
Rashkin, H., Nikolaev, V., Lamm, M., Aroyo, L., Collins, M., Das, D., Petrov, S., Tomar, G. S., Turc, I., & Reitter, D. (2021) · arXiv · doi:10.48550/arxiv.2112.12870 · Cited by 34 · see citing works
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A Comprehensive Overview of Large Language Models
Naveed, H., Khan, A. U., Qiu, S., Saqib, M., Anwar, S., Usman, M., Akhtar, N., Barnes, N., & Mian, A. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3744746 · Cited by 1,257 · see citing works
On the Risk of Misinformation Pollution with Large Language Models
Pan, Y., Pan, L., Chen, W., Nakov, P., Kan, M.-Y., & Wang, W. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.97 · Cited by 70 · see citing works
Shi, J., Yang, Z., & Lo, D. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3708525 · Cited by 43 · see citing works
Zeng, H., Luo, C., & Zamani, H. (2024) · Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3626772.3657746 · Cited by 22 · see citing works
Self-citation
- Authors
- Hofstätter, S., Chen, J., Raman, K., & Zamani, H.
- Affiliations
- Cohere, Vienna, Austria
- Bytedance Inc., Culver City, CA, USA
- Google (United States) (US)
- University of Massachusetts Amherst (US)
- Venue
- Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
- Publisher
- ACM
- Published
- 2023-07-18
- Pages
- 1437-1447
- OpenAlex
- W4384652670
- Cited by
- 51
- References
- 5
- Citations by year
- 2026: 8 · 2025: 18 · 2024: 16 · 2023: 8 · 2022: 1
- FWCI
- 8.88
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- retrieval-augmented generation, re-ranking, query response time, knowledge-intensive tasks, text generation
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