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Stimulating Large Language Models with Skeleton Heuristics for Generating High-Quality Knowledge Base Questions

Core Concepts
A simple and effective framework, SGSH, that stimulates GPT-3.5 with skeleton heuristics to generate high-quality questions from knowledge bases.
The paper proposes a framework called SGSH (Stimulate GPT-3.5 with Skeleton Heuristics) to enhance the performance of large language models (LLMs) like GPT-3.5 on the task of Knowledge Base Question Generation (KBQG). The key insights are: Directly applying LLMs like GPT-3.5 to KBQG does not fully leverage their vast knowledge, resulting in suboptimal performance compared to state-of-the-art pre-trained language model (PLM) approaches. Introducing "skeleton heuristics" - which provide fine-grained guidance on the question structure (e.g., question phrase, auxiliary verb) - can significantly boost the performance of LLMs on KBQG. The SGSH framework consists of two main components: A PLM-based skeleton generator: This module generates the skeleton for each input, which acts as a guiding signal for the LLM. The authors propose an automatic data construction strategy leveraging ChatGPT to train the skeleton generator. A frozen GPT-3.5 model: This module utilizes the skeleton heuristics through two approaches: (i) skeleton injection, where the generated skeleton is injected into the test input, and (ii) skeleton-aware in-context learning, where the skeleton information is incorporated into the in-context examples. Extensive experiments on two KBQG datasets demonstrate that SGSH outperforms existing state-of-the-art methods, including PLM-based approaches. The authors also show that SGSH can benefit downstream question answering tasks by augmenting the training data with generated questions.
Directly applying GPT-3.5 (Davinci003) to KBQG reduces 1.26% BLEU-1 and 2.45% ROUGE-L compared to the state-of-the-art PLM-based method DSM on the WebQuestions (WQ) dataset. Incorporating skeleton heuristics into GPT-3.5 (Davinci003+SH) outperforms both DSM and Davinci003 on the WQ dataset. SGSH(Davinci003) achieves 68.16% BLEU-1, 47.30% BLEU-3, and 69.59% ROUGE-L on the WQ dataset, surpassing all baselines. SGSH(Davinci003) obtains 88.87% BLEU-1, 79.52% BLEU-3, and 92.47% ROUGE-L on the PathQuestions (PQ) dataset, outperforming all baselines.
"Directly applying GPT-3.5 to KBQG fails to achieve good performance. Compared to existing state-of-the-art (SOTA) PLMs-based method (i.e., DSM), we notice that ChatGPT reduces 6.48% BLEU-1 and 6.02% ROUGE-L, while Davinci003 reduces 1.26% BLEU-1 and 2.45% ROUGE-L on WQ." "Our proposed framework SGSH can motivate GPT-3.5 to produce high-quality questions, which demonstrates the effectiveness of introducing skeleton heuristics."

Key Insights Distilled From

by Shasha Guo,L... at 04-03-2024

Deeper Inquiries

How can the automatic data construction strategy be further improved to generate even higher-quality skeletons for training the skeleton generator?

To enhance the automatic data construction strategy for generating higher-quality skeletons, several improvements can be considered: Refinement with Human Feedback: Incorporating human feedback to validate and refine the generated skeletons can improve their quality. Human annotators can provide insights into the accuracy and relevance of the skeletons, helping to fine-tune the generation process. Utilizing Domain-Specific Knowledge: Incorporating domain-specific knowledge or ontologies can help in generating more contextually relevant skeletons. By leveraging domain-specific information, the automatic strategy can produce skeletons that are more aligned with the knowledge base and the specific task at hand. Iterative Training: Implementing an iterative training process where the skeleton generator learns from its own mistakes and refines its output over multiple iterations can lead to the generation of higher-quality skeletons. Diverse Training Data: Ensuring that the training data used for constructing skeletons is diverse and covers a wide range of scenarios can help in generating more robust and accurate skeletons. This diversity can help the model generalize better to unseen data. Fine-tuning with Adversarial Examples: Introducing adversarial examples during the training process can help the model learn to generate skeletons that are robust to variations and edge cases, leading to higher-quality outputs.

What other types of fine-grained guidance, beyond the skeleton heuristics, could be explored to better steer large language models towards generating more accurate and relevant questions?

In addition to skeleton heuristics, several other types of fine-grained guidance can be explored to improve the question generation process for large language models: Semantic Constraints: Incorporating semantic constraints or rules that govern the structure and content of the questions can guide the language model towards generating more contextually relevant and accurate questions. Task-specific Prompts: Designing task-specific prompts that provide detailed instructions or cues to the model about the desired question structure and content can help steer the language model towards generating more precise questions. Contextual Embeddings: Leveraging contextual embeddings that capture the context of the input data and the desired question can help the model generate questions that are more aligned with the specific context and information provided. Multi-step Prompting: Implementing a multi-step prompting approach where the model receives incremental guidance at each step of the question generation process can lead to more accurate and coherent questions. Knowledge Graph Integration: Integrating knowledge graph information into the prompting process can help the model access relevant knowledge and incorporate it into the question generation process, leading to more informed and accurate questions.

Given the success of SGSH in enhancing question generation, how could this approach be adapted or extended to improve the performance of large language models on other knowledge-intensive tasks, such as knowledge base question answering or fact checking?

The approach used in SGSH can be adapted and extended to improve the performance of large language models on other knowledge-intensive tasks in the following ways: Task-specific Prompting: Tailoring the prompting strategy to the specific requirements of tasks like knowledge base question answering or fact-checking can help guide the language model towards generating more accurate and relevant responses. Structured Data Integration: Incorporating structured data sources, such as knowledge graphs or databases, into the prompting process can provide additional context and information for the language model to generate more informed answers. Multi-modal Prompting: Utilizing multi-modal prompts that combine text, images, or other forms of data can enhance the language model's understanding and enable it to generate more comprehensive responses for knowledge-intensive tasks. Feedback Mechanisms: Implementing feedback mechanisms where the model receives input on the accuracy of its responses and adjusts its prompting strategy accordingly can help improve performance over time. Domain-specific Knowledge Integration: Integrating domain-specific knowledge and terminology into the prompting process can help the language model generate responses that are more aligned with the specific domain requirements of tasks like knowledge base question answering or fact-checking.