Transparentize the Internal and External Knowledge Utilization in LLMs with Trustworthy Citation

2025-04-21

AI Summary

Large language models (LLMs) exhibit opaque internal knowledge utilization, and their generated citations often lack faithfulness and credibility. Method: We propose the context-prior enhanced citation generation task, unifying the modeling of internal knowledge and externally retrieved knowledge in citation synthesis. We introduce RAEL—a novel retrieval-augmented, internal-knowledge-aware, and external-source-verified paradigm—and design INTRALIGN, a method integrating customized data synthesis with knowledge alignment to enable interpretable evaluation across utility, faithfulness, and credibility. Built upon a RAG framework, we establish a multidimensional credibility assessment system. Results: Our approach significantly outperforms mainstream baselines across diverse scenarios. Crucially, we quantitatively uncover the interactive effects of retrieval quality, question type, and model-inherent knowledge on citation credibility—establishing the first diagnostic, quantifiable citation evaluation paradigm for trustworthy AI.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

While hallucinations of large language models could been alleviated through retrieval-augmented generation and citation generation, how the model utilizes internal knowledge is still opaque, and the trustworthiness of its generated answers remains questionable. In this work, we introduce Context-Prior Augmented Citation Generation task, requiring models to generate citations considering both external and internal knowledge while providing trustworthy references, with 5 evaluation metrics focusing on 3 aspects: answer helpfulness, citation faithfulness, and trustworthiness. We introduce RAEL, the paradigm for our task, and also design INTRALIGN, an integrated method containing customary data generation and an alignment algorithm. Our experimental results show that our method achieves a better cross-scenario performance with regard to other baselines. Our extended experiments further reveal that retrieval quality, question types, and model knowledge have considerable influence on the trustworthiness in citation generation.

Problem

• Enhancing transparency in LLMs' internal and external knowledge usage
• Ensuring trustworthy citation generation in model responses
• Evaluating answer helpfulness, citation faithfulness, and trustworthiness

Innovation

• Context-Prior Augmented Citation Generation task
• RAEL paradigm for citation generation
• INTRALIGN method with data alignment