Synchromesh: Reliable code generation from pre-trained language models (2201.11227v1)

Abstract: Large pre-trained LLMs have been used to generate code,providing a flexible interface for synthesizing programs from natural language specifications. However, they often violate syntactic and semantic rules of their output language, limiting their practical usability. In this paper, we propose Synchromesh: a framework for substantially improving the reliability of pre-trained models for code generation. Synchromesh comprises two components. First, it retrieves few-shot examples from a training bank using Target Similarity Tuning (TST), a novel method for semantic example selection. TST learns to recognize utterances that describe similar target programs despite differences in surface natural language features. Then, Synchromesh feeds the examples to a pre-trained LLM and samples programs using Constrained Semantic Decoding (CSD): a general framework for constraining the output to a set of valid programs in the target language. CSD leverages constraints on partial outputs to sample complete correct programs, and needs neither re-training nor fine-tuning of the LLM. We evaluate our methods by synthesizing code from natural language descriptions using GPT-3 and Codex in three real-world languages: SQL queries, Vega-Lite visualizations and SMCalFlow programs. These domains showcase rich constraints that CSD is able to enforce, including syntax, scope, typing rules, and contextual logic. We observe substantial complementary gains from CSD and TST in prediction accuracy and in effectively preventing run-time errors.

Synchromesh: Reliable Code Generation from Pre-trained LLMs

The paper introduces Synchromesh, a framework aiming to enhance the reliability of program synthesis when utilizing large pre-trained LLMs. It delineates a methodology focused on overcoming the common pitfalls of code generation—namely, syntactic and semantic errors—by aligning the output more closely with the desired specifications and constraints intrinsic to programming languages.

Framework Overview

Synchromesh comprises two primary components: Target Similarity Tuning (TST) and Constrained Semantic Decoding (CSD).

• Target Similarity Tuning (TST): This method dynamically selects relevant few-shot examples to serve as guiding prompts for the LLM. Unlike traditional approaches that prioritize surface-level similarity in natural language descriptions, TST optimizes the selection based on the semantic similarity of the programs described. It leverages a fine-tuning process where a sentence embedding model understands program similarity in terms of tree edit distance. The empirical results in SQL and SMCalFlow demonstrate TST's capacity to guide LLMs towards generating conceptually accurate code by providing pertinent structural examples, even when the language descriptions appear disparate.
• Constrained Semantic Decoding (CSD): CSD is a robust algorithm ensuring that the output adheres to predetermined syntax and semantic constraints, preventing classes of implementation errors during the decoding process. Through a mechanism known as Completion Engines (CE), which define valid continuations of partial outputs, CSD rigorously filters LLM-generated tokens to maintain validity throughout the prediction task. CSD utilizes Brzozowski derivatives as a decision procedure to ascertain if partial programs can be extended to valid ones. The paper showcases that CSD can enhance the reliability of LLMs by integrating rich constraints, such as syntax validity and scope management into the generation phase.

Experimental Validation

The paper evaluates Synchromesh across three domains: SQL, Vega-Lite, and SMCalFlow, using models such as GPT-3 and Codex. The experimental results reveal:

• Synchromesh significantly boosts prediction accuracy and validity across domains, minimizing semantic errors leading to runtime failures.
• The combination of TST and CSD manifests complementary gains—TST navigates conceptual accuracy while CSD assures syntactic and semantic validity.
• Such augmented LLM frameworks approach the performance of supervised models without specific domain-oriented fine-tuning, highlighting a substantial step towards more generic and robust code synthesis.

Theoretical and Practical Implications

The procedural enhancements introduced by Synchromesh have significant implications. Theoretically, addressing conceptual misalignment and ensuring semantic adherence advances our understanding of how best to leverage neural architectures for code synthesis tasks. These methodologies push the boundaries of general-purpose, few-shot learning in LLMs by aligning inference closer to deterministic program synthesis frameworks.

Practically, Synchromesh mitigates issues in existing systems using LLMs for code generation, such as GitHub Copilot. By enhancing the reliability and correctness of generated code, Synchromesh helps developers prevent runtime errors and bugs, thereby fostering trust and efficacy in AI-assisted coding tools.

Future Directions

While Synchromesh addresses many hurdles in LLM-driven code synthesis, the paper acknowledges limitations, particularly in handling conceptual errors and scaling the methodology to Turing-complete languages like Python. Future research may explore integrating richer semantic understanding within TST and extending CSD to handle more complex program structures. The development of Synchromesh presents a foundation upon which further advancements in AI-assisted code generation can be built, offering promising avenues for auto-coding applications.

In summary, Synchromesh is an innovative framework that elevates the reliability of code generation tasks utilizing LLMs, through principled yet practical methodologies addressing the core issues of syntactic and semantic errors. Its successful implementation across various real-world languages demonstrates a significant advancement in the field of AI-driven program synthesis.