Leveraging Pre-existing Coding Abilities of Large Language Models to Improve In-context Learning for Semantic Parsing

Core Concepts

Using general-purpose programming languages like Python instead of domain-specific languages, and augmenting prompts with structured domain descriptions, can dramatically improve the accuracy of in-context learning for semantic parsing, especially on compositional generalization tasks.

Abstract

The core idea of this work is to leverage the pre-existing coding abilities of large language models (LLMs) to improve in-context learning (ICL) for semantic parsing. The authors make two key changes:

Using general-purpose programming languages (PLs) like Python instead of domain-specific languages (DSLs) as the output representation. This allows LLMs to leverage their existing knowledge of coding practices and standard operations, rather than having to learn a new DSL syntax and semantics from just a few demonstrations.

Augmenting the ICL prompt with a structured Domain Description (DD) that outlines the available classes, methods, and types in the target domain. This provides crucial information that helps the LLM understand the functionality and usage of the output program, which is especially important when only a few demonstrations are available.

The authors evaluate their approach on three semantic parsing datasets - GeoQuery, SMCalFlow, and Overnight - using both ChatGPT and the open-source Starcoder model. They find that prompting the models with Python programs and a DD consistently outperforms prompting with the original DSLs, often by a large margin. This is true even when the DSL prompts are augmented with a DD.
Notably, the Python+DD approach dramatically improves compositional generalization, nearly closing the performance gap between i.i.d. and compositional test splits. Further analysis shows that the prevalence of a PL in pretraining corpora is not the sole factor determining performance - even rare PLs like Scala can outperform more common ones like Python, as long as the PL's syntax and structure resemble general-purpose code.
Overall, the findings suggest that when using LLMs for semantic parsing, it is better to either prompt them with PLs or design DSLs to closely resemble PLs, while also providing a detailed DD. This provides an improved methodology for building semantic parsing applications in the modern context of in-context learning with LLMs.

Stats

The authors report the following key statistics:

For GeoQuery, the average program length in FunQL is 49.4 characters, while in Python it is 115.4 characters.
For Overnight, the average program length in λ-DCS is 282.0 characters, while in the simplified λ-DCS it is 164.1 characters, and in Python it is 270.0 characters.
For SMCalFlow, the average program length in Dataflow is 372.6 characters, while in the simplified Dataflow it is 118.7 characters, and in Python it is 174.4 characters.
The average maximum depth of programs in FunQL is 4.8, in λ-DCS is 6.8, and in Dataflow is 8.7.

Quotes

None

Key Insights Distilled From

Leveraging Code to Improve In-context Learning for Semantic Parsing

by Ben Bogin,Sh... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2311.09519.pdf

Deeper Inquiries

How can the insights from this work be applied to other generative tasks beyond semantic parsing, where the output must conform to a structured format

The insights from this work can be applied to other generative tasks beyond semantic parsing by leveraging general-purpose programming languages (PLs) and structured domain descriptions (DDs). For tasks where the output must conform to a structured format, using PLs instead of domain-specific languages (DSLs) can help improve the performance of large language models (LLMs). By providing DDs that outline the available operators, classes, methods, and attributes in the domain, LLMs can better understand the task requirements and generate more accurate outputs. This approach can be beneficial for tasks such as code generation, recipe generation, travel itinerary planning, and other tasks where the output needs to adhere to a specific structure or format.

What are the limitations of the current Python environments used in the experiments, and how could they be improved to better reflect the original DSL environments

The limitations of the current Python environments used in the experiments include the potential inaccuracies in replicating the original DSL environments and the bias introduced by using OpenAI's API to annotate Python programs. To improve the Python environments for better reflection of the original DSL environments, the following steps could be taken:

Validate the Python implementations against the original DSL environments more rigorously to ensure accuracy.
Minimize bias by using a diverse set of annotations and validation methods for the generated Python programs.
Implement a more comprehensive testing framework to verify the fidelity of the Python environments in executing programs accurately.
Consider incorporating additional validation steps or tools to ensure that the Python implementations faithfully represent the original DSL environments.

Given the finding that the prevalence of a programming language in pretraining corpora is not the sole factor determining performance, what other characteristics of a language's design and structure might contribute to its suitability for in-context learning with LLMs

The finding that the prevalence of a programming language in pretraining corpora is not the sole factor determining performance suggests that other characteristics of a language's design and structure play a role in its suitability for in-context learning with LLMs. Some factors that might contribute to a language's effectiveness in this context include:

Simplicity and Expressiveness: Languages that are simple yet expressive, allowing for concise and clear representations of tasks, may be more suitable for in-context learning with LLMs.
Consistency and Familiarity: Languages that follow consistent syntax and structures, resembling popular general-purpose languages, can be easier for LLMs to learn and generalize from.
Modularity and Abstraction: Languages that support modularity, abstraction, and the breakdown of tasks into intermediate steps may facilitate better understanding and generation of complex outputs by LLMs.
Ease of Interpretation: Languages that are easy to interpret and understand, both for humans and LLMs, can lead to more accurate and reliable outputs in in-context learning scenarios.
By considering these factors in the design and selection of programming languages for in-context learning tasks, researchers can optimize the performance and generalization capabilities of LLMs in various applications.