Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering (2311.06503v3)

Abstract: Deploying 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.

Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering

The paper "Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering" provides a well-articulated exploration into the application of LLMs in the field of domain-specific question answering (QA) tasks. As the use of LLMs continues to permeate various sectors, the need for models to adapt to specific domains—where general LLMs often fall short—becomes increasingly evident. This work investigates the integration of domain-specific knowledge graphs (KGs) with LLMs to enhance performance in specialized QA tasks, offering a thorough examination of a novel training pipeline dubbed "Knowledgeable Preference Alignment" or KnowPAT.

Primary Contributions and Methods

1. Domain-specific Knowledge Incorporation: The paper proposes a method for integrating domain-specific KGs with LLMs. The domain-specific knowledge graphs act as repositories of specialized knowledge that are essential for accurately answering questions within a specific domain, such as cloud computing services. This method effectively addresses the shortcomings of vanilla LLMs which may lack specialized knowledge due to their general training datasets.
2. Preference Alignment Framework: Recognizing that LLMs have inherent style and knowledge preferences, this paper introduces a framework for aligning these preferences with human expectations. The authors create two preference sets: the style preference set and the knowledge preference set. These are used to guide the LLM during fine-tuning, aiming to enhance both the stylistic and factual elements of the generated answers.
3. Novel Training Objective: The paper introduces a new training objective within the alignment process, which fine-tunes the model using both golden answer pairs and preference data. This approach is motivated by the observation that existing models need better alignment with human preferences in real-world applications.
4. Empirical Validation: Experiments conducted within this paper demonstrate significant improvements over baseline methods, including vanilla fine-tuning and several state-of-the-art preference alignment methods. The comprehensive evaluation using various traditional and model-based metrics substantiates the efficacy of the KnowPAT framework. Notably, human evaluation further corroborates these numerical results, emphasizing favorable human-centric outcomes.

Implications and Future Directions

Practically, this research offers a compelling approach for the deployment of LLMs in commercial and industrial settings, where domain-specific knowledge is a prerequisite. The alignment between model-generated responses and human expectations ensures user-friendly and reliable communication, crucial for customer-facing applications.

From a theoretical perspective, this paper highlights the effectiveness of strategic preference alignment in enhancing the applicability of LLMs. The approach paves the way for further exploration into hybrid systems that combine powerful generative models with structured domain-specific knowledge.

Future developments could explore the adaptability of this framework to other domains, potentially underlining the universality and scalability of the presented method. Additionally, refining the unsupervised triple linking process to minimize noise and optimize knowledge retrieval could further enhance the performance of domain-specific QA systems. Delightfully, by sharing their codebase openly, the authors have fostered opportunities for further advancements by enabling broader experimentation and refinement within the research community.

In summary, the paper contributes a significant stride toward improving LLM applications through the inventive use of knowledgeable preference alignment, underscoring a thoughtful approach to tailored model training in domain-specific contexts.