Training Language Models on Synthetic Edit Sequences Improves Code Synthesis
Science & Technology
Introduction
Introduction
In this article, we explore the concept of reparameterizing code data sets using edit sequences. Operating within a supervised learning framework, we start with a dataset of code examples, each potentially paired with a natural language description of its function. We denote this dataset as (D), consisting of (n) example programs. To analyze the differences between code versions, we leverage the Unix diff operator, which identifies changes by comparing strings line by line, a common practice in software development for tracking edits. An edit can include multiple line deletions or insertions for a specific program in our dataset.
To represent a sequence of program states leading to a specific program, we formulate an edit sequence. This involves calculating the difference between an empty program and the initial program in the sequence, followed by computing differences between each pair of successive programs. We create a new dataset (D'), where each natural language instruction is paired with its corresponding edit sequence, effectively refactoring (D) into edit sequences.
Lint Seek Algorithm
We introduce LinSeek, an algorithm that reformulates code synthesis as a sequential edit problem by generating error-free code edit sequences using a linter. We propose that training language models on these edit sequences will enhance the quality and diversity of synthesized code while improving the tradeoff between generation quality and computational cost during inference.
Generating Linter-Guided Synthetic Edit Sequences
In this section, we describe the generation of synthetic edit sequences guided by a linter. A single program edit calculated using the diff operator can involve multiple deletions and insertions, and LinSeek computes edit sequences that consist solely of insertions. The algorithm operates in two phases: the backward sampling phase and the forward edit computation phase.
In the backward sampling phase, we create multiple sequences of intermediate program states starting from an empty program and leading to the original program. Through a process called linter-guided sampling, we work backward from the original program, randomly deleting lines and checking for errors using a linter until we have removed all lines. This process generates program state sequences.
In the forward edit computation phase, we compute the edit sequences for each program state sequence generated earlier by applying the diff operator to find edits between consecutive programs. Ultimately, we pair each edit sequence with its corresponding instruction to create a new set of edit sequences.
The synthetic edit sequences produced by LinSeek possess notable properties: each sequence can reconstruct the original program by applying edits sequentially, and every prefix of the edit sequence corresponds to a subprogram that is error-free. This indicates that LinSeek samples various error-free sequences of line insertions that can be used to build the original program from scratch.
Experimentation and Results
We conduct a series of experiments to explore LinSeek and to evaluate how reframing program synthesis as a sequential edit generation task affects outcomes. Our experiments focus on code synthesis in Python and aim to answer essential questions regarding the impact of fine-tuning language models on data restructured into edit sequences.
To begin, we pre-trained two small transformer models on a large corpus of text and Python code. We then restructured the programs into code edit sequences using LinSeek and gathered the Python components of two open-source instruction datasets for code synthesis. The final dataset contains over 8,900 instruction-python program pairs, which we used to generate synthetic edit trajectory samples for each pair.
Following this preparation, we examined how fine-tuning various autoregressive language models on these edit sequences affects code synthesis compared to traditional program generation methods. We evaluated our models using zero-shot coverage statistics on code synthesis benchmarks, both with and without repeated sampling.
Our results highlighted that fine-tuning these models using LinSeek data significantly enhanced benchmark performance compared to standard fine-tuning approaches. The edit sequence versions of our smaller models outperformed all known code language models of similar size.
Fine-Tuning on Larger Models
We explored the performance of fine-tuning language models (LLMs) of differing sizes, architectures, and tokenizers using edit sequences. Conducting additional pairs of fine-tuning experiments with models like Gemma 2, Phi3, and Llama 3.1 across various parameter sizes showed that fine-tuning models to synthesize code with edits consistently enhanced zero-shot performance on coding benchmarks.
We also investigated the implications of linter guidance in our process. Our results indicated that models trained on linter-guided error-free edits exhibited significantly better benchmark coverage compared to those trained on randomly sampled edits.
Conclusion
In summary, our evaluation shows that training language models using synthetic edit sequences yields significant performance improvements in code synthesis, enhancing both the quality and the diversity of the generated programs. The error-free nature of these edits plays a crucial role in achieving this enhancement.
Keywords
- Edit sequences
- Code synthesis
- Linter guidance
- LinSeek algorithm
- Fine-tuning
- Language models
- Python code
- Synthetic data
- Code benchmarks
- Program states
FAQ
Q1: What is LinSeek?
A1: LinSeek is an algorithm that reformulates code synthesis as a sequential edit problem by generating error-free code edit sequences using a linter to enhance the quality and diversity of synthesized code.
Q2: How does LinSeek generate synthetic edit sequences?
A2: LinSeek generates synthetic edit sequences through a two-phase process: backward sampling of program states from an empty program to the original program and forward computation of edit sequences by applying the diff operator.
Q3: What were the results of the experiments conducted?
A3: The experiments showed that fine-tuning language models on edit sequences significantly improved benchmark performance compared to traditional code generation methods.
Q4: How does linter guidance affect the quality of generated code?
A4: Linter guidance ensures that all edits result in error-free programs, greatly enhancing the quality and diversity of the generated code compared to models trained on randomly sampled edits.
Q5: Can the techniques discussed in the article be applied to different programming languages?
A5: Yes, the LinSeek algorithm can be applied to any code data as long as there is knowledge of the programming language and the linter available.