Question answering (QA) with reference texts is a classic application scenario for large language models (LLMs), where high standards for the credibility and traceability of generated answers are crucial. Many existing approaches focus on generating multi-level citations linked to specific references within the answer, making it verifiable and trustworthy. However, they often overlook key challenges such as citation granularity, the awareness of unknown information, and the adoption of effective training strategies. In this paper, we introduce Knowledge-informed Citation (KFC), which addresses these issues through a novel data construction pipeline, a new benchmark, and an innovative training strategy. With approximately 42K samples spanning 19 distinct domains, KFC includes both traditional citations referencing known entity-level information and specialized citations referring to unknown knowledge in the given question. This structure provides a more granular approach to citations, guiding the model to recognize and explicitly indicate unknown information, thus enhancing the quality and credibility of the response. Additionally, we propose a self-correction paradigm, Self-KFC, designed to fine-tune LLMs by refining poorly cited answers into more accurate ones, making it particularly suitable for citation-dependent scenarios. We present comprehensive experimental results to demonstrate the effectiveness and generalization of Self-KFC on the KFC benchmark.

Large language models (LLMs) have demonstrated exceptional performance in text generation within current NLP research. However, the lack of factual accuracy is still a dark cloud hanging over the LLM skyscraper. Structural knowledge prompting (SKP) is a prominent paradigm to integrate external knowledge into LLMs by incorporating structural representations, achieving state-of-the-art results in many knowledge-intensive tasks. However, existing methods often focus on specific problems, lacking a comprehensive exploration of the generalization and capability boundaries of SKP. This paper aims to evaluate and rethink the generalization capability of the SKP paradigm from four perspectives including Granularity, Transferability, Scalability, and Universality. To provide a thorough evaluation, we introduce a novel multi-granular, multi-level benchmark called SUBARU, consisting of 9 different tasks with varying levels of granularity and difficulty. Through extensive experiments, we draw key conclusions regarding the generalization of SKP, offering insights to guide the future development and extension of the SKP paradigm.

Deploying large language models (LLMs) to real scenarios for domain-specific question answering (QA) is a key thrust for LLM applications, which poses numerous challenges, especially in ensuring that responses are both accommodating to user requirements and appropriately leveraging domain-specific knowledge bases. They are the two major difficulties for LLM application as vanilla fine-tuning falls short of addressing. Combining these requirements, we conceive of them as the requirement for the model’s preference to be harmoniously aligned with humans’. Thus, we introduce Knowledgeable Preference AlignmenT (KnowPAT), which constructs two kinds of preference sets to tackle the two issues. Besides, we design a new alignment objective to align the LLM preference with different human preferences uniformly, aiming to optimize LLM performance in real-world, domain-specific QA settings. Adequate experiments and comprehensive comparisons with 15 baseline methods illustrate that our KnowPAT is a superior pipeline for real-scenario domain-specific QA with LLMs.