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

• URL: http://arxiv.org/abs/2409.16146v1
• Date: Tue, 24 Sep 2024 14:52:14 GMT
• Title: Controlling Risk of Retrieval-augmented Generation: A Counterfactual Prompting Framework
• Authors: Lu Chen, Ruqing Zhang, Jiafeng Guo, Yixing Fan, Xueqi Cheng,
• Abstract summary: We focus on how likely it is that a RAG model's prediction is incorrect, resulting in uncontrollable risks in real-world applications. Our research identifies two critical latent factors affecting RAG's confidence in its predictions. We develop a counterfactual prompting framework that induces the models to alter these factors and analyzes the effect on their answers.
• Score: 77.45983464131977
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-augmented generation (RAG) has emerged as a popular solution to mitigate the hallucination issues of large language models. 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.

Related papers

• Toward Robust RALMs: Revealing the Impact of Imperfect Retrieval on Retrieval-Augmented Language Models [5.10832476049103]
  We identify three common scenarios-unanswerable, adversarial, conflicting-where retrieved document sets can confuse RALM with plausible real-world examples. We propose a new adversarial attack method, Generative model-based ADVersarial attack (GenADV) and a novel metric Robustness under Additional Document (RAD) Our findings reveal that RALMs often fail to identify the unanswerability or contradiction of a document set, which frequently leads to hallucinations.
  arXiv  Detail & Related papers  (2024-10-19T13:40:33Z)
• Trustworthiness in Retrieval-Augmented Generation Systems: A Survey [59.26328612791924]
  Retrieval-Augmented Generation (RAG) has quickly grown into a pivotal paradigm in the development of Large Language Models (LLMs) We propose a unified framework that assesses the trustworthiness of RAG systems across six key dimensions: factuality, robustness, fairness, transparency, accountability, and privacy.
  arXiv  Detail & Related papers  (2024-09-16T09:06:44Z)
• RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework [69.4501863547618]
  This paper introduces RAGEval, a framework designed to assess RAG systems across diverse scenarios. With a focus on factual accuracy, we propose three novel metrics Completeness, Hallucination, and Irrelevance. Experimental results show that RAGEval outperforms zero-shot and one-shot methods in terms of clarity, safety, conformity, and richness of generated samples.
  arXiv  Detail & Related papers  (2024-08-02T13:35:11Z)
• Black-Box Opinion Manipulation Attacks to Retrieval-Augmented Generation of Large Language Models [21.01313168005792]
  We reveal the vulnerabilities of Retrieval-Enhanced Generative (RAG) models when faced with black-box attacks for opinion manipulation. We explore the impact of such attacks on user cognition and decision-making.
  arXiv  Detail & Related papers  (2024-07-18T17:55:55Z)
• Challenges and Considerations in the Evaluation of Bayesian Causal Discovery [49.0053848090947]
  Representing uncertainty in causal discovery is a crucial component for experimental design, and more broadly, for safe and reliable causal decision making. Unlike non-Bayesian causal discovery, which relies on a single estimated causal graph and model parameters for assessment, causal discovery presents challenges due to the nature of its quantity. No consensus on the most suitable metric for evaluation.
  arXiv  Detail & Related papers  (2024-06-05T12:45:23Z)
• C-RAG: Certified Generation Risks for Retrieval-Augmented Language Models [57.10361282229501]
  We propose C-RAG, the first framework to certify generation risks for RAG models. Specifically, we provide conformal risk analysis for RAG models and certify an upper confidence bound of generation risks. We prove that RAG achieves a lower conformal generation risk than that of a single LLM when the quality of the retrieval model and transformer is non-trivial.
  arXiv  Detail & Related papers  (2024-02-05T16:46:16Z)
• Less is More: Mitigate Spurious Correlations for Open-Domain Dialogue Response Generation Models by Causal Discovery [52.95935278819512]
  We conduct the first study on spurious correlations for open-domain response generation models based on a corpus CGDIALOG curated in our work. Inspired by causal discovery algorithms, we propose a novel model-agnostic method for training and inference of response generation model.
  arXiv  Detail & Related papers  (2023-03-02T06:33:48Z)
• Two steps to risk sensitivity [4.974890682815778]
  conditional value-at-risk (CVaR) is a risk measure for modeling human and animal planning. We adopt a conventional distributional approach to CVaR in a sequential setting and reanalyze the choices of human decision-makers. We then consider a further critical property of risk sensitivity, namely time consistency, showing alternatives to this form of CVaR.
  arXiv  Detail & Related papers  (2021-11-12T16:27:47Z)
• Feedback Effects in Repeat-Use Criminal Risk Assessments [0.0]
  We show that risk can propagate over sequential decisions in ways that are not captured by one-shot tests. Risk assessment tools operate in a highly complex and path-dependent process, fraught with historical inequity.
  arXiv  Detail & Related papers  (2020-11-28T06:40:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.