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Vu Le 0002

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2

ICLR Conference 2025 Conference Paper

Execution-guided within-prompt search for programming-by-example

  • Gust Verbruggen
  • Ashish Tiwari 0001
  • Mukul Singh
  • Vu Le 0002
  • Sumit Gulwani

Large language models (LLMs) can generate code from examples without being limited to a DSL, but they lack search, as sampled programs are independent. In this paper, we use an LLM as a policy that generates lines of code and then join these lines of code to let the LLM implicitly estimate the value of each of these lines in its next iteration. We further guide the policy and value estimation by executing each line and annotating it with its results on the given examples. This allows us to search for programs within a single (expanding) prompt until a sound program is found, by letting the policy reason in both the syntactic (code) and semantic (execution) space. We evaluate within-prompt search on straight-line Python code generation using five benchmarks across different domains (strings, lists, and arbitrary Python programming problems). We show that the model uses the execution results to guide the search and that within-prompt search performs well at low token budgets. We also analyze how the model behaves as a policy and value, show that it can parallelize the search, and that it can implicitly backtrack over earlier generations.

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ICLR Conference 2022 Conference Paper

Synchromesh: Reliable Code Generation from Pre-trained Language Models

  • Gabriel Poesia
  • Alex Polozov
  • Vu Le 0002
  • Ashish Tiwari 0001
  • Gustavo Soares
  • Christopher Meek
  • Sumit Gulwani

Large pre-trained language models 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 of differences in surface natural language features. Then, Synchromesh feeds the examples to a pre-trained language model 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 language model. 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, scoping and typing rules. Across all languages, we observe complementary gains from CSD and TST in prediction accuracy and in effectively preventing parsing, type and run-time errors.

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