Pistis-RAG: Enhancing Retrieval-Augmented Generation with Human Feedback (2407.00072v5)

Abstract: RAG systems face limitations when semantic relevance alone does not guarantee improved generation quality. This issue becomes particularly evident due to the sensitivity of LLMs to the ordering of few-shot prompts, which can affect model performance. To address this challenge, aligning LLM outputs with human preferences using structured feedback, such as options to copy, regenerate, or dislike, offers a promising method for improvement. This feedback is applied to the entire list of inputs rather than giving specific ratings for individual documents, making it a Listwide Labels Learning-to-Rank task. To address this task, we propose Pistis-RAG, a new RAG framework designed with a content-centric approach to better align LLMs with human preferences. Pistis-RAG effectively utilizes human feedback, enhancing content ranking and generation quality. To validate our framework, we use public datasets to simulate human feedback, allowing us to evaluate and refine our method effectively. Experimental results indicate that Pistis-RAG improves alignment with human preferences relative to the baseline RAG system, showing a 6.06% increase in MMLU (English) and a 7.08% increase in C-EVAL (Chinese) accuracy metrics. These results highlight Pistis-RAG's effectiveness in overcoming the limitations associated with traditional RAG approaches.

• The paper introduces a cascading framework that integrates content-centric and user feedback mechanisms to enhance retrieval-augmented generation.
• Its multi-stage architecture employs matching, pre-ranking, ranking, reasoning, and aggregating services to improve document relevance and alignment.
• Experimental results on the MMLU benchmark indicate a 9.3% performance boost over existing methods, validating the framework's effectiveness.

Overview of Pistis-RAG: A Scalable Cascading Framework Towards Trustworthy Retrieval-Augmented Generation

The paper "Pistis-RAG: A Scalable Cascading Framework Towards Trustworthy Retrieval-Augmented Generation" introduces a novel framework aimed at enhancing the effectiveness and efficiency of Retrieval-Augmented Generation (RAG) systems. This framework, named Pistis-RAG, is designed to address the alignment issues between LLMs and external knowledge ranking methods, which are often overlooked in current RAG systems.

Core Contributions

The Pistis-RAG framework is structured into multiple distinct stages: matching, pre-ranking, ranking, reasoning, and aggregating. Each stage of this cascading system plays a vital role in narrowing the search space for relevant documents, aligning retrieved information with LLM preferences, and enhancing the overall quality of the generated content. The key contributions of the Pistis-RAG framework are summarized as follows:

1. Content-Centric Integration: Unlike traditional model-centric approaches, Pistis-RAG adopts a content-centric perspective that emphasizes seamless integration of external information sources with LLMs. This approach optimizes content transformation processes to meet specific task requirements.
2. Enhanced User Feedback Mechanisms: The framework effectively incorporates user feedback into the ranking process. By continuously adapting to user preferences and business goals, Pistis-RAG enhances the relevance and contextual appropriateness of the generated content.
3. Novel Framework: Pistis-RAG integrates advanced ranking techniques across different stages, ensuring a systematic approach to content evaluation and selection. This comprehensive framework supports a high degree of retrieval accuracy and relevance, crucial for robust AIGC systems.
4. Experimental Validation: Through extensive experiments, the authors demonstrate the framework's effectiveness using the MMLU benchmark. The introduction of a specialized ranking mechanism and integration of user feedback signals resulted in a 9.3% performance improvement over existing methods.

Detailed Architecture

The architecture of Pistis-RAG comprises several stages, each contributing uniquely to the retrieval and generation process:

1. Matching Service

In the matching stage, the system employs various retrieval algorithms to select relevant documents from a vast corpus. Techniques such as TF-IDF, BM25, and bi-encoder methods are utilized to retrieve documents that align with the user's query. The matching service is optimized for low-latency, making it suitable for large-scale, real-time applications.

2. Pre-Ranking Service

The pre-ranking stage refines the initial set of retrieved documents by scoring them based on semantic relevance. Cross-encoder techniques are applied to ensure a more accurate and relevant subset is passed to the subsequent ranking stage.

3. Ranking Service

The ranking stage prioritizes documents according to LLM preferences and user feedback. This stage addresses the issue of prompt order sensitivity by optimizing the sequence of prompts presented to the LLM. The use of listwise Learning to Rank (LTR) methods ensures that the most informative and relevant documents are highlighted.

4. Re-Ranking Service

Though optional, the re-ranking stage can be employed for domain-specific requirements such as assessing the credibility of information sources. This stage ensures that the final set of documents meets additional criteria relevant to the specific application.

5. Reasoning Service

The reasoning stage leverages multi-path reasoning to generate multiple response sequences for documents with similar semantic content. This approach enhances the diversity and richness of the generated responses.

6. Aggregating Service

The final stage synthesizes the outputs from the reasoning stage through advanced voting techniques, ensuring consistency and accuracy in the final user response. Industry-specific optimizations, such as citation integration, markdown formatting, and content safety checks, are also incorporated.

Experimental Results

The authors validate Pistis-RAG's performance through both simulation of user feedback and experiments on the MMLU dataset. The framework outperforms existing RAG methods, achieving a significant improvement in accuracy and relevance. The introduction of feedback labels and the multi-path reasoning mechanism played crucial roles in these enhancements.

Implications and Future Directions

The Pistis-RAG framework addresses critical challenges in current RAG systems, offering substantial improvements in content alignment and generation quality. The integration of user feedback and advanced ranking techniques not only enhances the relevance of generated content but also ensures that the system remains adaptive to evolving user preferences.

From a theoretical perspective, the content-centric approach proposed by Pistis-RAG provides a new lens through which RAG systems can be designed and optimized. Practically, this framework holds significant potential for improving large-scale online content generation platforms, such as customer service chatbots and real-time recommendation systems.

Future developments in AI could further enhance the capabilities of RAG systems by integrating more sophisticated feedback mechanisms and advanced reasoning techniques. Continuous innovation in this domain will be essential to meet the growing demands for accurate, relevant, and reliable AI-generated content in various applications.

In summary, Pistis-RAG represents a substantial advancement in the field of retrieval-augmented generation, offering a scalable and efficient framework capable of addressing the inherent limitations of existing systems. The detailed architectural and experimental insights provided in the paper lay a strong foundation for future research and development in this critical area of AI.