Controlling Risk of Retrieval-augmented Generation: A Counterfactual Prompting Framework (2409.16146v2)

Abstract: Retrieval-augmented generation (RAG) has emerged as a popular solution to mitigate the hallucination issues of LLMs. However, existing studies on RAG seldom address the issue of predictive uncertainty, i.e., how likely it is that a RAG model's prediction is incorrect, resulting in uncontrollable risks in real-world applications. In this work, we emphasize the importance of risk control, ensuring that RAG models proactively refuse to answer questions with low confidence. Our research identifies two critical latent factors affecting RAG's confidence in its predictions: the quality of the retrieved results and the manner in which these results are utilized. To guide RAG models in assessing their own confidence based on these two latent factors, we develop a counterfactual prompting framework that induces the models to alter these factors and analyzes the effect on their answers. We also introduce a benchmarking procedure to collect answers with the option to abstain, facilitating a series of experiments. For evaluation, we introduce several risk-related metrics and the experimental results demonstrate the effectiveness of our approach. Our code and benchmark dataset are available at https://github.com/ict-bigdatalab/RC-RAG.

• The paper introduces a counterfactual prompting framework to evaluate and mitigate risk in retrieval-augmented generation.
• It employs a multi-module method that challenges and fuses responses to judiciously control model confidence.
• Extensive experiments show significant improvements in risk reduction and careful rejection of inaccurate outputs.

Controlling Risk of Retrieval-augmented Generation: A Counterfactual Prompting Framework

The paper "Controlling Risk of Retrieval-augmented Generation: A Counterfactual Prompting Framework" addresses a notable gap in the current body of research concerning Retrieval-augmented Generation (RAG) by proposing a novel counterfactual prompting framework aimed at risk control. Despite RAG’s efficacy in enhancing factual accuracy, it often falls short in terms of predictive uncertainty, thereby introducing unmanageable risks in practical applications. This paper identifies the quality and the usage of retrievals as two fundamental factors influencing the confidence in RAG models and proposes a systematic approach to assess and manage these risks effectively.

Framework and Methodology

The paper introduces a counterfactual prompting framework to assess the confidence of RAG outputs. The proposed framework operates through several distinct components:

1. Prompting Generation Module:
   • This module generates counterfactual prompts designed to challenge the quality and usage of retrieved results. By forcing the model to reconsider these factors, it assesses the robustness of its initial predictions.
2. Judgment Module:
   • By comparing the initial and regenerated answers under counterfactual scenarios, this module judges whether to keep or discard the initial answer. Iterative execution ensures thorough evaluation, mitigating overconfidence.
3. Fusion Module:
   • This module consolidates the judgments from different counterfactual scenarios using strategies such as direct selection or probability comparison, deriving the final decision on whether to keep or discard the answer.

The overall framework emphasizes proactive abstention in scenarios where the model's confidence is low, aligning with practical requirements for applications in sensitive domains like healthcare and law.

Experimental Results and Analysis

A comprehensive benchmark was constructed using two QA datasets, Natural Questions (NQ) and TriviaQA (TQ), complemented with newly introduced risk-related metrics: risk, carefulness, alignment, and coverage. Extensive experiments using LLMs such as Mistral and ChatGPT validate the effectiveness of the counterfactual prompting framework. Key findings include:

1. Risk Reduction:
   • The proposed framework consistently outperforms existing baselines, with a significant reduction in risk scores (up to 4.39% lower on certain configurations).
2. Improvement in Carefulness:
   • The framework demonstrates up to a 14.77% improvement in carefully rejecting inaccurate outputs compared to baseline methods.

However, the framework does incur trade-offs, notably in terms of coverage, sacrificing some correct answers to ensure higher confidence in retained responses.

Implications and Future Directions

The proposed framework has noteworthy implications for improving the reliability of RAG models. Its robust assessment mechanism can be pivotal in critical applications that demand high accuracy. Future research could explore several avenues:

1. Incorporating Additional Factors:
   • Future models may integrate more latent factors affecting predictive uncertainty, such as conflict resolution between internal and external knowledge bases.
2. Joint Optimization:
   • Developing objective functions based on risk-related metrics for joint training with RAG frameworks could harmonize risk control with performance, addressing the observed trade-offs in coverage and confidence.
3. Efficient Prompting Techniques:
   • The iterative nature of the current framework is computationally intensive. Future work could seek more efficient prompting and assessment techniques to reduce computational overhead.

Conclusion

The counterfactual prompting framework presented in this paper significantly advances the state of risk control in RAG, proposing a sophisticated mechanism to enhance predictive reliability. By efficiently managing the inherent uncertainties in RAG models, the framework sets a foundation for further research and development in creating more trustworthy and reliable AI systems capable of handling practical and sensitive information retrieval tasks. The implications for improved QA systems across various domains underscore the importance and potential impact of this work in the ongoing development and deployment of advanced LLMs.