TACO: Topics in Algorithmic COde generation dataset
By Rongao Li, Jie Fu, Bo-wen Zhang, Tao Huang, Zhihong Sun, Chen Lyu, Guang Liu, Zhi Jin, Ge Li Beijing Academy of Artificial Intelligence, School of Materials Science and 7 more. 2023
141 citations recorded by the source corpus.
We introduce TACO, an open-source, large-scale code generation dataset, with a focus on the optics of algorithms, designed to provide a more challenging training dataset and evaluation benchmark in the field of code generation models. TACO includes competition-level programming questions that are more challenging, to enhance or evaluate problem understanding and reasoning abilities in real-world programming scenarios. There are 25433 and 1000 coding problems in training and test set, as well as up to 1.55 million diverse solution answers. Moreover, each TACO problem includes several fine-grained labels such as task topics, algorithms, programming skills, and difficulty levels, providing a more precise reference for the training and evaluation of code generation models. The dataset and evaluation scripts are available on Hugging Face Hub (https://huggingface.co/datasets/BAAI/TACO) and Github (https://github.com/FlagOpen/TACO).
Subfields: Information Systems, Software, Evaluation & Benchmarks, Code Generation & Program Synthesis, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Information Systems
- Armando Solar-Lezama
- Koushik Sen
- Naman Jain
- Tianjun Zhang
- Ion Stoica
- Rishabh Singh
- Mosharaf Chowdhury
- Charles Sutton
In Software
- Armando Solar-Lezama
- Koushik Sen
- Naman Jain
- Tianjun Zhang
- Ion Stoica
- Patrice Godefroid
- Rishabh Singh
- Gordon Fraser
In Evaluation & Benchmarks
In Code Generation & Program Synthesis
In Signal Processing
- Armando Solar-Lezama
- Koushik Sen
- Tianjun Zhang
- Ion Stoica
- Patrice Godefroid
- Andrea Arcuri
- Gordon Fraser
Citations in the corpus
- LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code (2024, background): “(Li et al., 2023c) propose a large dataset of competitive programming problems with additional generated tests but do not study contamination or tasks beyond generation.”
- Process Reinforcement through Implicit Rewards (2025, unknown): “For coding, we source from APPS (Hendrycks et al., 2021a), CodeContests (Li et al., 2022), TACO (Li et al., 2023), and Codeforces 2 .”
- MAGECODE: Machine-Generated Code Detection Method Using Large Language Models (2024, unknown): “We collected programming problems from two open-source datasets: Topics in Algorithmic Code Generation (TACO) [24] and CodeContest [25].”