Automatic Composition of ASP Programs from Natural Language Specifications

Keskeiset käsitteet

The author introduces the NL2CNL dataset and NL2ASP tool for automatic composition of ASP programs, utilizing NMT models T5-small and Bart-base. The experiments show T5-small outperforms Bart-base in translation quality and syntactic correctness.

Tiivistelmä

The paper introduces a dataset focused on graph-related problem specifications and a two-step architecture for generating ASP programs from natural language statements. NMT models T5-small and Bart-base were used to translate NL to CNL, with T5-small performing better. End-to-end evaluation showed NL2ASP produced correct ASP programs for most cases.
The content discusses the benefits of automated program composition, the challenges in ASP coding, the use of CNLs, and the development of datasets for question answering. It also explores the application of LLMs like GPT-3 in semantic parsing tasks.

Tilastot

A dataset with 6810 NL-CNL pairs was created.
T5-small model achieved BLEU scores ranging from 0.90% to 0.97%.
Bart-base model scored lower than T5-small in translation quality measures.
During syntax checking, T5-small achieved an accuracy of about 94%, while Bart-base had an accuracy around 55%.

Lainaukset

"The first steps towards filling this gap are taken by providing a dataset focused on graph-related problem specifications."
"NL2ASP processes input using NMT techniques to translate natural language into Controlled Natural Language (CNL) statements."

Tärkeimmät oivallukset

Towards Automatic Composition of ASP Programs from Natural Language Specifications

by Manuel Borro... klo arxiv.org 03-08-2024

https://arxiv.org/pdf/2403.04541.pdf

Syvällisempiä Kysymyksiä

How can the NL2ASP tool be further optimized for improved performance?

To optimize the NL2ASP tool for better performance, several strategies can be implemented:

Fine-tuning NMT Models: Continuously fine-tuning the Transformer-based models like T5-small and Bart-base on a larger and more diverse dataset can enhance their translation accuracy.

Data Augmentation: Increasing the diversity of the NL2CNL dataset through data augmentation techniques such as back-translation, paraphrasing, or adding more template variations can improve model generalization.

Error Analysis: Conducting thorough error analysis to identify common mistakes made by the models during translation and addressing these specific issues in training data or model architecture.

Post-processing Techniques: Implementing post-processing steps to correct minor syntactic errors in CNL propositions generated by NMT models before converting them into ASP code.

Hybrid Approaches: Exploring hybrid approaches that combine rule-based methods with neural networks to leverage the strengths of both methodologies for accurate program composition.

Model Ensemble: Utilizing ensemble techniques by combining predictions from multiple NMT models or different architectures to boost overall performance and reduce individual model biases.

How are potential implications of using general-purpose LLMs like GPT-3 for ASP program composition?

Using general-purpose Large Language Models (LLMs) like GPT-3 for ASP program composition has several implications:

Semantic Understanding: LLMs have advanced natural language understanding capabilities that enable them to comprehend complex problem statements and generate corresponding ASP programs effectively.

Efficiency: General-purpose LLMs can expedite the process of generating ASP programs from natural language specifications, potentially reducing development time compared to traditional manual coding methods.

Scalability: Leveraging LLMs allows for scalability in handling a wide range of problem domains without requiring extensive domain-specific programming knowledge.

Robustness: While general-purpose LLMs offer convenience in generating code, they may lack specificity required for certain specialized tasks within Knowledge Representation paradigms like Answer Set Programming (ASP).

Quality Assurance: Despite their capabilities, ensuring correctness and adherence to specific constraints in generated ASP programs remains a challenge when relying solely on general-purpose LLMs due to potential inaccuracies or misinterpretations.

How can automated program composition tools impact traditional programming paradigms?

Automated program composition tools have significant implications on traditional programming paradigms:
1.Productivity Enhancement:

Automated tools streamline coding processes, enabling developers to focus more on high-level design aspects rather than low-level implementation details.
2** Error Reduction:**

By automating parts of code generation based on natural language specifications, these tools help minimize human errors commonly found in manual coding.
3** Accessibility:**

Automated tools make programming accessible even to individuals with limited technical expertise by providing intuitive interfaces that convert plain language descriptions into executable code.
4** Innovation Acceleration:**

These tools foster innovation by allowing rapid prototyping and experimentation with ideas without getting bogged down in intricate syntax details.
5** Paradigm Shift:**

The shift towards automated program composition challenges conventional notions of software development where hand-coding was predominant, paving way for new ways of thinking about how we write software.