Recently, the development of large language models (LLMs) has attracted wide
attention in academia and industry. Deploying LLMs to real scenarios is one of
the key directions in the current Internet industry. In this paper, we present
a novel pipeline to apply LLMs for domain-specific question answering (QA) that
incorporates domain knowledge graphs (KGs), addressing an important direction
of LLM application. As a real-world application, the content generated by LLMs
should be user-friendly to serve the customers. Additionally, the model needs
to utilize domain knowledge properly to generate reliable answers. These two
issues are the two major difficulties in the LLM application as vanilla
fine-tuning can not adequately address them. We think both requirements can be
unified as the model preference problem that needs to align with humans to
achieve practical application. Thus, we introduce Knowledgeable Preference
AlignmenT (KnowPAT), which constructs two kinds of preference set called style
preference set and knowledge preference set respectively to tackle the two
issues. Besides, we design a new alignment objective to align the LLM
preference with human preference, aiming to train a better LLM for
real-scenario domain-specific QA to generate reliable and user-friendly
answers. Adequate experiments and comprehensive with 15 baseline methods
demonstrate that our KnowPAT is an outperforming pipeline for real-scenario
domain-specific QA with LLMs. Our code is open-source at
this https URL

应用大型语言模型和领域知识图谱解决真实场景中的特定领域问答问题，通过构建两种偏好集合来解决用户友好性和可靠性的问题，同时使用新的对齐目标训练更好的语言模型。

领域特定问答中对 LLM 的知识偏好调整

Knowledgeable Preference Alignment for LLMs in Domain-specific Question  Answering

Large language models (LLMs) have demonstrated remarkable performance by
following natural language instructions without fine-tuning them on
domain-specific tasks and data. However, leveraging LLMs for domain-specific
question answering suffers from severe limitations. The generated answer tends
to hallucinate due to the training data collection time (when using
off-the-shelf), complex user utterance and wrong retrieval (in
retrieval-augmented generation). Furthermore, due to the lack of awareness
about the domain and expected output, such LLMs may generate unexpected and
unsafe answers that are not tailored to the target domain. In this paper, we
propose CarExpert, an in-car retrieval-augmented conversational
question-answering system leveraging LLMs for different tasks. Specifically,
CarExpert employs LLMs to control the input, provide domain-specific documents
to the extractive and generative answering components, and controls the output
to ensure safe and domain-specific answers. A comprehensive empirical
evaluation exhibits that CarExpert outperforms state-of-the-art LLMs in
generating natural, safe and car-specific answers.

CarExpert 通过借助大型语言模型（LLMs）在不针对特定任务和数据进行微调的情况下，控制输入，为抽取和生成回答组件提供特定领域的文档，并控制输出，以确保生成自然、安全且针对汽车领域的答案，优于最先进的 LLMs。