Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
LLM-Assisted Code Cleaning For Training Accurate Code Generators
Authors: Naman Jain, Tianjun Zhang, Wei-Lin Chiang, Joseph E. Gonzalez, Koushik Sen, Ion Stoica
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach on two challenging algorithmic code generation benchmarks and find that fine-tuning CODELLAMA-7B on our transformed modularized programs improves the performance by up to 30% compared to fine-tuning on the original dataset. |
| Researcher Affiliation | Academia | Naman Jain, Tianjun Zhang, Wei-Lin Chiang, Joseph E. Gonzalez, Koushik Sen & Ion Stoica University of California, Berkeley EMAIL |
| Pseudocode | No | The paper describes the steps for data cleaning and transformation in text, but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper refers to using CODELLAMA-7B model checkpoint from Hugging Face and VLLM for inference, but does not state that the authors are releasing their own code for the described methodology. |
| Open Datasets | Yes | We use two standard algorithmic code generation benchmarks, APPS and CODE-CONTESTS. The benchmarks provide a collection of problem statements described in natural language and corresponding test cases. |
| Dataset Splits | No | Table 1: Details about the number of problems, the median number of test cases per problem, and the number of solutions in the APPS and CODE-CONTESTS datasets. (Table only lists 'train' and 'test' counts for problems and solutions, no validation split) |
| Hardware Specification | Yes | We train the models for two epochs on the APPS dataset and one epoch on the CODE-CONTESTS dataset using a 5e 5 learning rate and an effective batch size of 256 on 4 A6000 GPUs. |
| Software Dependencies | No | The paper mentions using CODELLAMA-7B model, Hugging Face, VLLM, DeepSpeed, and gaoya, but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We train the models for two epochs on the APPS dataset and one epoch on the CODE-CONTESTS dataset using a 5e 5 learning rate and an effective batch size of 256 on 4 A6000 GPUs. |