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..
CodeLutra: Boosting LLM Code Generation via Preference-Guided Refinement
Authors: Leitian Tao, Xiang Chen, Tong Yu, Tung Mai, Ryan A. Rossi, Yixuan Li, Saayan Mitra
TMLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We comprehensively evaluate the effectiveness of Code Lutra on challenging data query and data science tasks, where the LLM is tasked with generating the correct SQL or Python code to solve a given problem. We compare Code Lutra with 13 open-source and closed-source LLMs that are competitive in code generation. Notably, on the data query task, our framework allows Llama-3-8B (Dubey et al., 2024) to achieve an execution accuracy of 76.6%, which exceeds GPT-4 s 74.4%. |
| Researcher Affiliation | Collaboration | Leitian Tao EMAIL University of Wisconsin-Madison, Xiang Chen EMAIL Adobe Research, Tong Yu EMAIL Adobe Research, Tung Mai EMAIL Adobe Research, Ryan A. Rossi EMAIL Adobe Research, Yixuan Li EMAIL University of Wisconsin-Madison, Saayan Mitra EMAIL Adobe Research |
| Pseudocode | Yes | We summarize our algorithm in implementation in the Algorithm 1. Algorithm 1 Code Lutra |
| Open Source Code | No | The paper does not contain any explicit statement about releasing the source code for their methodology, nor does it provide a link to a code repository or mention code in supplementary materials. |
| Open Datasets | Yes | We conduct our experiments on two cross-domain datasets for data query, Spider (Yu et al., 2018) and BIRD (Li et al., 2024), and a data science dataset, DS-1000 (Lai et al., 2023). |
| Dataset Splits | Yes | We split DS-1000 into 500 samples for training and 500 for evaluation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running the experiments. |
| Software Dependencies | No | The paper mentions hyperparameters and a framework (Deep Speed Zero stage 2) but does not provide specific version numbers for software dependencies or libraries used to replicate the experiment. |
| Experiment Setup | Yes | Table 4: Summary of training hyperparameters for data query and data science for each iteration. Parameters: Number of epochs 1, Learning rate 5 * 10^-5, Beta 0.1 (Data Query) / 0.5 (Data Science), Batch size 16, Gradient accumulation steps 1, Maximum sequence length 2048 (Data Query) / 512 (Data Science), Deep Speed Zero stage 2, Weight decay 0.0001, Lo RA rank 8, Lambda 1.0 (Data Query) / 0.5 (Data Science). |