Not All Contexts Are Equal: Teaching LLMs Credibility-aware Generation

• URL: http://arxiv.org/abs/2404.06809v3
• Date: Wed, 09 Oct 2024 17:16:15 GMT
• Title: Not All Contexts Are Equal: Teaching LLMs Credibility-aware Generation
• Authors: Ruotong Pan, Boxi Cao, Hongyu Lin, Xianpei Han, Jia Zheng, Sirui Wang, Xunliang Cai, Le Sun,
• Abstract summary: Credibility-aware Generation (CAG) aims to equip models with the ability to discern and process information based on its credibility. Our model can effectively understand and utilize credibility for generation, significantly outperform other models with retrieval augmentation, and exhibit resilience against the disruption caused by noisy documents.
• Score: 47.42366169887162
• License:
• Abstract: The rapid development of large language models has led to the widespread adoption of Retrieval-Augmented Generation (RAG), which integrates external knowledge to alleviate knowledge bottlenecks and mitigate hallucinations. However, the existing RAG paradigm inevitably suffers from the impact of flawed information introduced during the retrieval phrase, thereby diminishing the reliability and correctness of the generated outcomes. In this paper, we propose Credibility-aware Generation (CAG), a universally applicable framework designed to mitigate the impact of flawed information in RAG. At its core, CAG aims to equip models with the ability to discern and process information based on its credibility. To this end, we propose an innovative data transformation framework that generates data based on credibility, thereby effectively endowing models with the capability of CAG. Furthermore, to accurately evaluate the models' capabilities of CAG, we construct a comprehensive benchmark covering three critical real-world scenarios. Experimental results demonstrate that our model can effectively understand and utilize credibility for generation, significantly outperform other models with retrieval augmentation, and exhibit resilience against the disruption caused by noisy documents, thereby maintaining robust performance. Moreover, our model supports customized credibility, offering a wide range of potential applications.

Related papers

• Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey [92.36487127683053]
  Retrieval-Augmented Generation (RAG) is an advanced technique designed to address the challenges of Artificial Intelligence-Generated Content (AIGC) RAG provides reliable and up-to-date external knowledge, reduces hallucinations, and ensures relevant context across a wide range of tasks. Despite RAG's success and potential, recent studies have shown that the RAG paradigm also introduces new risks, including privacy concerns, adversarial attacks, and accountability issues.
  arXiv  Detail & Related papers  (2025-02-08T06:50:47Z)
• RbFT: Robust Fine-tuning for Retrieval-Augmented Generation against Retrieval Defects [12.5122702720856]
  We propose Robust Fine-Tuning (RbFT) to enhance the resilience of large language models against retrieval defects. Experimental results demonstrate that RbFT significantly improves the robustness of RAG systems across diverse retrieval conditions.
  arXiv  Detail & Related papers  (2025-01-30T14:15:09Z)
• Understanding the Impact of Confidence in Retrieval Augmented Generation: A Case Study in the Medical Domain [27.517686277349735]
  We study the impact of RAG on confidence within the medical domain under various configurations and models. Our findings reveal large variation in confidence and accuracy depending on the model, settings, and the format of input prompts.
  arXiv  Detail & Related papers  (2024-12-29T00:58:33Z)
• Towards More Robust Retrieval-Augmented Generation: Evaluating RAG Under Adversarial Poisoning Attacks [45.07581174558107]
  Retrieval-Augmented Generation (RAG) systems have emerged as a promising solution to mitigate hallucinations. RAG systems are vulnerable to adversarial poisoning attacks, where malicious passages injected into retrieval databases can mislead the model into generating factually incorrect outputs. This paper investigates both the retrieval and the generation components of RAG systems to understand how to enhance their robustness against such attacks.
  arXiv  Detail & Related papers  (2024-12-21T17:31:52Z)
• Controlling Risk of Retrieval-augmented Generation: A Counterfactual Prompting Framework [77.45983464131977]
  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.
  arXiv  Detail & Related papers  (2024-09-24T14:52:14Z)
• 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)
• Improving Retrieval Augmented Language Model with Self-Reasoning [20.715106330314605]
  We propose a novel self-reasoning framework aimed at improving the reliability and traceability of RALMs. The framework involves constructing self-reason trajectories with three processes: a relevance-aware process, an evidence-aware selective process, and a trajectory analysis process. We have evaluated our framework across four public datasets to demonstrate the superiority of our method.
  arXiv  Detail & Related papers  (2024-07-29T09:05:10Z)
• 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)
• "Glue pizza and eat rocks" -- Exploiting Vulnerabilities in Retrieval-Augmented Generative Models [74.05368440735468]
  Retrieval-Augmented Generative (RAG) models enhance Large Language Models (LLMs) In this paper, we demonstrate a security threat where adversaries can exploit the openness of these knowledge bases.
  arXiv  Detail & Related papers  (2024-06-26T05:36:23Z)
• Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity [80.16488817177182]
  GNNs are vulnerable to the model stealing attack, a nefarious endeavor geared towards duplicating the target model via query permissions. We introduce three model stealing attacks to adapt to different actual scenarios.
  arXiv  Detail & Related papers  (2023-12-18T05:42:31Z)

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.