Papers
Topics
Authors
Recent
2000 character limit reached

Retrieval-Augmented Generation with Estimation of Source Reliability (2410.22954v3)

Published 30 Oct 2024 in cs.LG

Abstract: Retrieval-augmented generation (RAG) addresses key limitations of LLMs, such as hallucinations and outdated knowledge, by incorporating external databases. These databases typically consult multiple sources to encompass up-to-date and various information. However, standard RAG methods often overlook the heterogeneous source reliability in the multi-source database and retrieve documents solely based on relevance, making them prone to propagating misinformation. To address this, we propose Reliability-Aware RAG (RA-RAG) which estimates the reliability of multiple sources and incorporates this information into both retrieval and aggregation processes. Specifically, it iteratively estimates source reliability and true answers for a set of queries with no labelling. Then, it selectively retrieves relevant documents from a few of reliable sources and aggregates them using weighted majority voting, where the selective retrieval ensures scalability while not compromising the performance. We also introduce a benchmark designed to reflect real-world scenarios with heterogeneous source reliability and demonstrate the effectiveness of RA-RAG compared to a set of baselines.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (48)
  1. Phi-3 technical report: A highly capable language model locally on your phone. arXiv preprint arXiv:2404.14219, 2024.
  2. Self-RAG: Learning to retrieve, generate, and critique through self-reflection. In The Twelfth International Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=hSyW5go0v8.
  3. Factuality challenges in the era of large language models and opportunities for fact-checking. Nature Machine Intelligence, pp.  1–12, 2024.
  4. Crowdsourcing for multiple-choice question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 28, pp.  2946–2953, 2014.
  5. Phantom: General trigger attacks on retrieval augmented language generation. arXiv preprint arXiv:2405.20485, 2024.
  6. Can llm-generated misinformation be detected? arXiv preprint arXiv:2309.13788, 2023.
  7. Benchmarking large language models in retrieval-augmented generation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pp.  17754–17762, 2024.
  8. Toward adaptive reasoning in large language models with thought rollback. In Forty-first International Conference on Machine Learning, 2024.
  9. Cram: Credibility-aware attention modification in llms for combating misinformation in rag. arXiv preprint arXiv:2406.11497, 2024.
  10. The llama 3 herd of models. arXiv preprint arXiv:2407.21783, 2024.
  11. Prospect theory based crowdsourcing for classification in the presence of spammers. IEEE Transactions on Signal Processing, 68:4083–4093, 2020.
  12. Not what you’ve signed up for: Compromising real-world llm-integrated applications with indirect prompt injection. In Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security, pp.  79–90, 2023.
  13. Retrieval augmented language model pre-training. In International conference on machine learning, pp.  3929–3938. PMLR, 2020.
  14. Why so gullible? enhancing the robustness of retrieval-augmented models against counterfactual noise. In Findings of the Association for Computational Linguistics: NAACL 2024, pp.  2474–2495, 2024.
  15. spacy: Industrial-strength natural language processing in python. 2020.
  16. Unsupervised dense information retrieval with contrastive learning. Transactions on Machine Learning Research, 2022. ISSN 2835-8856. URL https://openreview.net/forum?id=jKN1pXi7b0.
  17. Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12):1–38, 2023.
  18. Active retrieval augmented generation. In Houda Bouamor, Juan Pino, and Kalika Bali (eds.), Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp.  7969–7992, Singapore, December 2023. Association for Computational Linguistics. doi: 10.18653/v1/2023.emnlp-main.495. URL https://aclanthology.org/2023.emnlp-main.495.
  19. TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension. In Regina Barzilay and Min-Yen Kan (eds.), Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp.  1601–1611, Vancouver, Canada, July 2017. Association for Computational Linguistics. doi: 10.18653/v1/P17-1147. URL https://aclanthology.org/P17-1147.
  20. Challenges and applications of large language models. arXiv preprint arXiv:2307.10169, 2023.
  21. Budget-optimal crowdsourcing using low-rank matrix approximations. In 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton), pp.  284–291. IEEE, 2011.
  22. Studying large language model behaviors under realistic knowledge conflicts, 2024.
  23. Natural questions: a benchmark for question answering research. Transactions of the Association for Computational Linguistics, 7:453–466, 2019.
  24. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems, 33:9459–9474, 2020.
  25. Error rate bounds and iterative weighted majority voting for crowdsourcing. arXiv preprint arXiv:1411.4086, 2014.
  26. Multi-object classification via crowdsourcing with a reject option. IEEE Transactions on Signal Processing, 65(4):1068–1081, 2016.
  27. Chin-Yew Lin. ROUGE: A package for automatic evaluation of summaries. In Text Summarization Branches Out, pp.  74–81, Barcelona, Spain, July 2004. Association for Computational Linguistics. URL https://aclanthology.org/W04-1013.
  28. Variational inference for crowdsourcing. Advances in neural information processing systems, 25, 2012.
  29. Addressing the harms of ai-generated inauthentic content. Nature Machine Intelligence, 5(7):679–680, 2023.
  30. OpenAI. Gpt-4o-mini: Advancing cost-efficient intelligence. https://openai.com/index/gpt-4o-mini-advancing-cost-efficient-intelligence, 2024.
  31. Not all contexts are equal: Teaching llms credibility-aware generation. arXiv preprint arXiv:2404.06809, 2024.
  32. On the risk of misinformation pollution with large language models. In The 2023 Conference on Empirical Methods in Natural Language Processing, 2023. URL https://openreview.net/forum?id=voBhcwDyPt.
  33. SQuAD: 100,000+ questions for machine comprehension of text. In Jian Su, Kevin Duh, and Xavier Carreras (eds.), Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp.  2383–2392, Austin, Texas, November 2016. Association for Computational Linguistics. doi: 10.18653/v1/D16-1264. URL https://aclanthology.org/D16-1264.
  34. Machine against the rag: Jamming retrieval-augmented generation with blocker documents. arXiv preprint arXiv:2406.05870, 2024.
  35. The instruction hierarchy: Training llms to prioritize privileged instructions. arXiv preprint arXiv:2404.13208, 2024.
  36. Dynamic self-consistency: Leveraging reasoning paths for efficient llm sampling. arXiv preprint arXiv:2408.17017, 2024.
  37. Self-consistency improves chain of thought reasoning in language models. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=1PL1NIMMrw.
  38. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824–24837, 2022.
  39. Defending against disinformation attacks in open-domain question answering. In Yvette Graham and Matthew Purver (eds.), Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers), pp.  402–417, St. Julian’s, Malta, March 2024. Association for Computational Linguistics. URL https://aclanthology.org/2024.eacl-short.35.
  40. Certifiably robust rag against retrieval corruption. arXiv preprint arXiv:2405.15556, 2024.
  41. Adaptive chameleon or stubborn sloth: Revealing the behavior of large language models in knowledge conflicts. In The Twelfth International Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=auKAUJZMO6.
  42. Knowledge conflicts for llms: A survey. arXiv preprint arXiv:2403.08319, 2024.
  43. HotpotQA: A dataset for diverse, explainable multi-hop question answering. In Ellen Riloff, David Chiang, Julia Hockenmaier, and Jun’ichi Tsujii (eds.), Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp.  2369–2380, Brussels, Belgium, October-November 2018. Association for Computational Linguistics. doi: 10.18653/v1/D18-1259. URL https://aclanthology.org/D18-1259.
  44. Tree of thoughts: Deliberate problem solving with large language models. Advances in Neural Information Processing Systems, 36, 2024.
  45. A weighted aggregation rule in crowdsourcing systems for high result accuracy. In 2014 ieee 12th international conference on dependable, autonomic and secure computing, pp.  265–270. IEEE, 2014.
  46. Poisoning retrieval corpora by injecting adversarial passages. In The 2023 Conference on Empirical Methods in Natural Language Processing, 2023. URL https://openreview.net/forum?id=8FgdMHbW27.
  47. Solving challenging math word problems using gpt-4 code interpreter with code-based self-verification. In The Twelfth International Conference on Learning Representations, 2023.
  48. Poisonedrag: Knowledge poisoning attacks to retrieval-augmented generation of large language models. arXiv preprint arXiv:2402.07867, 2024.

Summary

  • The paper introduces RA-RAG, a framework that estimates source reliability to mitigate misinformation propagation during retrieval and generation.
  • It leverages an iterative reliability estimation and a weighted majority voting mechanism to refine information aggregation and reduce misalignment.
  • Experimental results on benchmarks like Natural Questions and TriviaQA demonstrate RA-RAG's superior performance under adversarial conditions.

Reliability-Aware Retrieval-Augmented Generation Framework

The paper addresses a critical drawback in contemporary Retrieval-Augmented Generation (RAG) systems, specifically the propagation of misinformation due to non-discriminative reinforcement of sources solely based on relevance. This oversight is particularly problematic given the significant variability in the reliability of information sources within any sizeable dataset. The proposed Reliability-Aware RAG (RA-RAG) circumvents this through meticulous estimation of source reliability integrated into both retrieval and aggregation, significantly bolstering the system's defenses against misinformation.

Key Contributions

  1. Reliability Estimation and Source Selection: RA-RAG distinguishes itself by implementing an iterative method to estimate the reliability of each data source without requiring pre-labeled data. This estimation iteratively refines the perceived reliability and the truth of presented information, leading to more accurate document retrieval from credible sources.
  2. Weighted Majority Voting (WMV): The paper introduces a WMV mechanism wherein retrieved documents are aggregated not merely by their frequency but weighted by each source's reliability score. This mechanism inherently endorses information from more reliable sources, reducing the likelihood of misinformation propagation.
  3. Misalignment Filtering: RA-RAG addresses RAG's inherent issues of response misalignment by employing a filtering mechanism. The system uses a precision-based method to identify and exclude document-based hallucinations, fortifying the estimation against internal model biases.
  4. Benchmark Introduction: The authors developed a new benchmark conducive to multi-source RAG frameworks with varying source reliabilities that reflect real-world conditions. This clear delineation allows for authentic evaluation against heterogeneous source reliability.

Experimental Analysis

The authors conducted extensive experiments on datasets such as Natural Questions, HotpotQA, and TriviaQA. Different LLMs, including Llama3-8B Instruct, Phi3-mini, and GPT-4o-mini, were used to benchmark the performance. The RA-RAG consistently outperformed existing approaches like standard WMV and MV, particularly under conditions where source reliabilities were heterogeneous or adversarial.

RA-RAG showcased notable resilience against misinformation attacks, showing only marginal performance degradation when faced with databases interspersed with 'spammers' — sources predominantly filled with incorrect information. This aspect of RA-RAG is critical, especially given the escalation of data poisoning attacks in sophisticated RAG systems.

Implications and Future Directions

RA-RAG's framework signifies a substantial step forward in the development of reliable RAG systems, offering a robust method for aggregating diverse information sources while quantifiably evaluating their trustworthiness. By achieving near-oracle performance in practical scenarios, it sets a new standard for future frameworks in preventing misinformation.

Further research could explore enhancing semantic understanding in RA-RAG to handle the intrinsic complexity of natural language variations. Also, expanding the scope of RA-RAG's filtering mechanism to work seamlessly with advanced LLM architectures might elevate its robustness. Lastly, the challenges of dynamically generating reliability estimates through user-generated queries remain a promising area for further exploration.

Conclusion

The RA-RAG framework demonstrates a vital advancement in mitigating the propagation of misinformation by intelligently estimating and utilizing source reliability. Its introduction of a realistic benchmark, alongside its sophisticated methodologies, underscores its utility and efficacy in real-world applications, making it a seminal contribution to the field of reliable information retrieval and generation.

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

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now
Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets

This paper has been mentioned in 8 tweets and received 20 likes.

Upgrade to Pro to view all of the tweets about this paper:

Start a free 7-day Pro trial