FlexKBQA: A Flexible LLM-Powered Framework for Few-Shot Knowledge Base Question Answering (2308.12060v3)

Abstract: Knowledge base question answering (KBQA) is a critical yet challenging task due to the vast number of entities within knowledge bases and the diversity of natural language questions posed by users. Unfortunately, the performance of most KBQA models tends to decline significantly in real-world scenarios where high-quality annotated data is insufficient. To mitigate the burden associated with manual annotation, we introduce FlexKBQA by utilizing LLMs as program translators for addressing the challenges inherent in the few-shot KBQA task. Specifically, FlexKBQA leverages automated algorithms to sample diverse programs, such as SPARQL queries, from the knowledge base, which are subsequently converted into natural language questions via LLMs. This synthetic dataset facilitates training a specialized lightweight model for the KB. Additionally, to reduce the barriers of distribution shift between synthetic data and real user questions, FlexKBQA introduces an executionguided self-training method to iterative leverage unlabeled user questions. Furthermore, we explore harnessing the inherent reasoning capability of LLMs to enhance the entire framework. Consequently, FlexKBQA delivers substantial flexibility, encompassing data annotation, deployment, and being domain agnostic. Through extensive experiments on GrailQA, WebQSP, and KQA Pro, we observe that under the few-shot even the more challenging zero-shot scenarios, FlexKBQA achieves impressive results with a few annotations, surpassing all previous baselines and even approaching the performance of supervised models, achieving a remarkable 93% performance relative to the fully-supervised models. We posit that FlexKBQA represents a significant advancement towards exploring better integration of large and lightweight models. The code is open-sourced.

An Academic Overview of FlexKBQA: A Flexible LLM-Powered Framework for Few-Shot Knowledge Base Question Answering

This paper presents FlexKBQA, a novel framework leveraging LLMs, specifically designed to address the challenges of few-shot knowledge base question answering (KBQA). KBQA is a complex task, demanding the conversion of natural language questions into structured queries executable on a knowledge base. The traditional requirement for extensive manual annotation poses significant impediments for deploying KBQA systems in real-world scenarios, particularly when high-quality labeled data is sparse.

Framework Design and Methodology

FlexKBQA mitigates the annotation burden by utilizing LLMs as program translators, converting synthetic programs from the knowledge base into natural language questions, which facilitates the training of lightweight models. This novel approach doctrinally contrasts with conventional methods that rely on LLMs for in-context learning of question-to-program mapping. The framework consists of key components designed to improve performance and flexibility:

• Automatic Program Sampling: This component employs automated algorithms to create diverse program templates, ensuring a comprehensive sample space for program conversion. It uses a step-wise grounding technique to iteratively determine variable values, enabling the derivation of executable programs from structured query templates.
• Low-Resource Program Translation: LLMs are employed to translate sampled programs into natural language questions, thereby utilizing their generative capabilities. The translation process is facilitated through a structured prompt containing directives and seed pairs of question-program exemplars.
• Execution-Guided Self-Training (EGST): To address the distribution shift between synthetic data and real-world user queries, EGST uses iterative self-training with unlabeled user questions, leveraging execution-guided filtering mechanisms to enhance data purity and model robustness.
• Inherent Reasoning Augmentation: This augmentation utilizes the internal knowledge capabilities of LLMs, providing a secondary method to syntactically generated programs, enhancing both training data quality and inference accuracy.

Experimental Results and Contributions

FlexKBQA was extensively evaluated on diverse datasets including GrailQA, WebQSP, and KQA Pro, demonstrating robust performance in few-shot settings, often surpassing existing baselines and achieving near-parity with supervised methods. Notably, it achieved an impressive 93% of supervised performance, indicating its efficacy in real-world applications with limited annotated data.

The paper emphasizes three dimensions of flexibility offered by FlexKBQA:

1. Data Annotation Flexibility: Requiring minimal annotated pairs, the framework provides a scalable solution for diverse KBs.
2. Domain-Agnostic Flexibility: Applicable across different KBs and program formats, FlexKBQA alleviates distribution shift challenges through EGST.
3. Deployment Flexibility: Lightweight models ensure deployability advantages over large, closed-source LLMs, enabling seamless integration of domain-specific knowledge through fine-tuning.

Theoretical and Practical Implications

The paper enhances the theoretical framework of KBQA by demonstrating a synergistic relationship between LLMs and lightweight models. The practical implications extend to efficient deployment in low-resource environments, where the integration of LLM-generated synthetic data provides a feasible path for robust KBQA system development.

Future Directions

FlexKBQA opens avenues for future exploration into zero-shot KBQA settings, enriching the methodological toolkit for knowledge base interactions. Further research into batch prompting and advanced semantic filtering methods could refine its capabilities and extend its applicability across broader language understanding tasks.

Overall, FlexKBQA represents a significant step toward advancing KBQA methodology, particularly in scenarios constrained by limited training annotations, ensuring enhanced adaptability, and deployment efficiency across knowledge-rich environments.