Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
166 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

RAGGED: Towards Informed Design of Retrieval Augmented Generation Systems (2403.09040v2)

Published 14 Mar 2024 in cs.CL

Abstract: Retrieval-augmented generation (RAG) can significantly improve the performance of LLMs (LMs) by providing additional context for tasks such as document-based question answering (DBQA). However, the effectiveness of RAG is highly dependent on its configuration. To systematically find the optimal configuration, we introduce RAGGED, a framework for analyzing RAG configurations across various DBQA tasks. Using the framework, we discover distinct LM behaviors in response to varying context quantities, context qualities, and retrievers. For instance, while some models are robust to noisy contexts, monotonically performing better with more contexts, others are more noise-sensitive and can effectively use only a few contexts before declining in performance. This framework also provides a deeper analysis of these differences by evaluating the LMs' sensitivity to signal and noise under specific context quality conditions. Using RAGGED, researchers and practitioners can derive actionable insights about how to optimally configure their RAG systems for their specific question-answering tasks.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (32)
  1. Evidentiality-guided generation for knowledge-intensive nlp tasks.
  2. Longformer: The long-document transformer.
  3. Optimizing retrieval-augmented reader models via token elimination.
  4. Unlimiformer: Long-range transformers with unlimited length input. In Thirty-seventh Conference on Neural Information Processing Systems.
  5. 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.
  6. Reading wikipedia to answer open-domain questions. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).
  7. Benchmarking large language models in retrieval-augmented generation.
  8. Scaling instruction-finetuned language models. arXiv preprint arXiv:2210.11416.
  9. An empirical analysis of compute-optimal large language model training. In Advances in Neural Information Processing Systems.
  10. Gautier Izacard and Edouard Grave. 2021. Leveraging passage retrieval with generative models for open domain question answering.
  11. Dense passage retrieval for open-domain question answering.
  12. Omar Khattab and Matei Zaharia. 2020. Colbert: Efficient and effective passage search via contextualized late interaction over BERT. CoRR.
  13. Bioasq-qa: A manually curated corpus for biomedical question answering. Scientific Data, 10:170.
  14. Natural questions: A benchmark for question answering research. Transactions of the Association for Computational Linguistics.
  15. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems, 33:9459–9474.
  16. Lost in the middle: How language models use long contexts.
  17. When not to trust language models: Investigating effectiveness of parametric and non-parametric memories.
  18. National Library of Medicine. 2023. Pubmed baseline 2023 repository.
  19. Kilt: a benchmark for knowledge intensive language tasks.
  20. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683.
  21. Mctest: A challenge dataset for the open-domain machine comprehension of text. In Proceedings of the 2013 conference on empirical methods in natural language processing, pages 193–203.
  22. The probabilistic relevance framework: Bm25 and beyond. Foundations and Trends® in Information Retrieval, 3(4):333–389.
  23. Roformer: Enhanced transformer with rotary position embedding. Neurocomputing, 568:127063.
  24. Ul2: Unifying language learning paradigms. arXiv preprint arXiv:2205.05131.
  25. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971.
  26. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288.
  27. Learning to filter context for retrieval-augmented generation. arXiv preprint arXiv:2311.08377.
  28. Recomp: Improving retrieval-augmented lms with compression and selective augmentation.
  29. Retrieval meets long context large language models.
  30. Hotpotqa: A dataset for diverse, explainable multi-hop question answering.
  31. Making retrieval-augmented language models robust to irrelevant context.
  32. Chain-of-note: Enhancing robustness in retrieval-augmented language models.
Citations (3)
View on Semantic Scholar

Summary

  • The paper introduces the RAGGED framework that systematically optimizes retriever and reader configurations for DBQA tasks.
  • It demonstrates that encoder-decoder models benefit from up to 30 context documents while decoder-only models peak with fewer than 5.
  • The study reveals that dense retrievers excel in open-domain tasks, though sparse methods perform comparably in specialized domains with reduced costs.

Insights from RAGGED: Optimizing Retrieval-Augmented Generation Systems

Introduction to RAGGED Framework

The paper introduces a comprehensive framework, RAGGED, designed for the analysis and optimization of Retrieval-Augmented Generation (RAG) systems. The motivation behind RAGGED stems from the observation that the performance of RAG systems heavily depends on the configuration of its components, notably the retriever and the reader models, as well as the quantity and quality of context documents provided. Through systematic experimentation across a diverse set of document-based question answering (DBQA) tasks, the authors investigate two main types of retrievers (sparse and dense) and evaluate four high-performing reader models from both encoder-decoder and decoder-only architectures. The paper reveals significant insights into optimal RAG setup, the effects of context quantity and quality, and the interaction between reader models and context information.

Key Findings

Optimal Number of Contexts

One of the paper's significant findings is the variation in the optimal number of documents that different reader models can effectively use. Encoder-decoder models exhibit a continuous improvement in performance with the inclusion of up to 30 documents within their token limit. In contrast, decoder-only models' performance peaks with fewer than 5 documents, despite possessing a larger context window. This discrepancy highlights the importance of tailoring the number of context documents to the specific reader model in use.

Model Dependence on Context

The paper dives deep into reader models' reliance on provided contexts versus their pre-trained knowledge. It finds that decoder-only models, characterized by a larger memorization capacity during training, show less dependence on additional contexts provided at test-time. On the other hand, encoder-decoder models demonstrate a stronger reliance on contexts, implying that they are more sensitive to the quality of the retrieval.

Impact of Retrieval Quality

Another critical aspect explored is the effect of retriever quality on RAG systems. Notably, dense retrievers like ColBERT outperform sparse retrievers (BM25) in open-domain tasks. However, this advantage diminishes in specialized domains (e.g., biomedical), where lexical retrievers offer comparable accuracy with significantly less computational expense. The paper interestingly notes that the substantial retrieval performance gaps do not always translate to equivalent disparities in downstream performance, especially in multi-hop questions and specialized domains.

Implications and Future Directions

The insights from the RAGGED framework have far-reaching implications for the design and development of RAG systems:

  • Customization of RAG components: The findings underscore the importance of tailoring the number of retrieved documents and the choice of retriever and reader models based on the specific task and domain requirements.
  • Model Selection: The paper provides critical guidance on selecting reader models based on their contextualization behavior and dependence on pre-trained knowledge.
  • Focus on Specialized Domains: The nuanced performance of retrievers in specialized domains invites further investigation into domain-specific retrieval strategies.

Looking ahead, the RAGGED framework lays the groundwork for future explorations into the intricate dynamics of retrieval-augmented generation systems. It opens avenues for research into novel retriever and reader architectures, multi-domain RAG systems, and fine-grained analyses of context utilization behaviors.

Conclusion

Through the RAGGED framework, this paper contributes significantly to the understanding of retrieval-augmented generation systems. By meticulously analyzing various configurations and their impact on performance across several DBQA tasks, the authors provide a valuable resource for researchers and practitioners aiming to optimize RAG systems for diverse applications.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown