Summary CTO Pack Instruction Tuning Code Large Language Models arxiv.org
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One Line
A study found that tuning code using large language models can improve performance by comparing different bug fixing formatting strategies using the GitHub archive on GCP.
Slides
Slide Presentation (13 slides)
Key Points
- Instruction tuning is used to improve the performance of large language models (LLMs) on coding tasks.
- Pass@k metric is emphasized for evaluating code execution in LLMs.
- GPT-4 has a high pass rate for Python, indicating saturation of the Python subset.
- Various large language models such as Pythia, GPT-Neo, and GPT-NeoX-20B are discussed.
- References to papers and preprints related to LLMs and code generation are provided.
- Gpt sentence embeddings for semantic search have been developed.
- The importance of tuning code in large language models is highlighted.
- The GitHub archive on GCP is used as a dataset for instruction tuning.
Summaries
32 word summary
A study on tuning code using large language models found that instruction tuning can improve performance. The study uses the GitHub archive on GCP and compares different formatting strategies for bug fixing.
89 word summary
This text excerpt discusses the use of instruction tuning to improve the performance of large language models (LLMs) on coding tasks. The authors apply instruction tuning using code by leveraging the natural structure of Git commits. They compile a dataset called COMMIT PACK, which
The text summarizes a study on tuning code using large language models. The study uses the GitHub archive on GCP, filtering the dataset to improve the quality of commit messages and code lengths. Different formatting strategies for bug fixing are compared, with the line diff
1079 word summary
This text excerpt discusses the use of instruction tuning to improve the performance of large language models (LLMs) on coding tasks. The authors apply instruction tuning using code by leveraging the natural structure of Git commits. They compile a dataset called COMMIT PACK, which
The document discusses the instruction tuning of large language models for code generation tasks. It emphasizes the use of pass@k metric for evaluating code execution instead of heuristic-based metrics. The models are trained on natural language docstrings or comments to synthesize correct code
GPT-4 has a pass rate of 86.6% for Python, showing it is close to fully saturating the Python subset. The score for GPT-4 is higher than previous versions, possibly due to improvements in the API,
A framework for evaluating code generation models, Pythia, GPT-Neo, and GPT-NeoX-20B are among the large language models discussed in this document. Other topics covered include diff models, language model behavior changes, evaluation
This summary provides a list of references to various papers and preprints related to large language models (LLMs) and code generation. The references cover a range of topics including benchmarking LLMs, measuring code similarity, generating functionally correct code edits
This document provides a list of references to articles and papers related to large language models and code generation. The references include papers on benchmark datasets, evaluation methods, model alignment, program repair, code understanding and generation, and training data. These papers cover various
Niklas Muennighoff, Nouamane Tazi, Loic Magne, and Nils Reimers have developed Gpt sentence embeddings for semantic search. Niklas Muennighoff, Thomas Wang, and others have explored crossling
The document provides a list of references to various papers and preprints related to instruction tuning for large language models. The references cover a range of topics including automatic summarization, code models, bug fixing, prompt generation, and instruction-following models. These
The summary includes citations of various research papers related to large language models and code generation. Some of the key papers mentioned include "Finetuned language models are zero-shot learners" by Jason Wei et al., "Coeditor: Leveraging contextual changes for
This text excerpt includes a list of arXiv preprints related to large language models and code generation. The papers mentioned cover various topics such as alignment, code generation evaluation, benchmark datasets, and the performance breakdown of different bug types. The appendix of
The document "CTO Pack Instruction Tuning Code Large Language Models" provides instructions for optimizing code for large language models. The instructions cover various aspects such as data processing, memory management, and parallelization. The document emphasizes the importance of tuning code to
The document "CTO Pack Instruction Tuning Code Large Language Models" provides instructions for optimizing code for large language models. The instructions cover various aspects such as data processing, memory management, and parallelization. The document emphasizes the importance of tuning code to
The document discusses the CTO Pack Instruction Tuning Code Large Language Models. The contributions were made by Niklas Muennighoff, Qian Liu, Armel Zebaze, Qinkai Zheng, Binyuan Hui, Terry Yue
This document provides instructions for tuning code in large language models. It includes a list of programming languages and their respective popularity scores. The document also includes a list of additional programming languages and their corresponding popularity scores.
The document provides a list of code languages and their corresponding tuning code values for large language models. The first part of the document includes a list of code languages and their tuning code values, ranging from 85.8 to 0.0. The
We use the GitHub archive on GCP, containing metadata from GitHub commits up to 2016. The dataset includes over 2.8 million repositories, 145 million unique commits, and 163 million files. We filter the dataset based on criteria
Comparing data before and after filtering, the mean commit subject and message length increase, suggesting that messages are more informative after filtering. Code lengths decrease significantly due to limiting the number of allowed tokens in the filters. The filtered data carries more signal per token
The study compares different formatting strategies for fixing bugs in code using the SantaCoder model. Full code generation and code diff generation are compared. The unified diff format is found to be inefficient due to unnecessary duplication of input. The line diff format is proposed as
Figures 8, 9, and 10 demonstrate the line diff generations of the model. However, the current line diff implementation has limitations in handling code insertion. The diff format is not suitable for HUMAN EVAL EXPLAIN and HUMAN EVAL
Table 14 provides an overview of the bug types manually added to HumanEval solutions for the construction of HUMAN EVAL FIX. Each bug type affects different parts of the solution across programming languages. The bug types include value misuse, operator misuse, variable misuse
The summary below is organized into separate paragraphs to distinguish distinct ideas for readability, while retaining the original order in which ideas were presented.
The excerpted text includes a code snippet for a function that flips the case of characters in a string. It also mentions
The document excerpt provides instructions and examples for tuning code using large language models. It includes a Python function that checks if any two numbers in a list are closer to each other than a given threshold. The document also showcases various prompting formats and includes examples of
The function FruitDistribution takes a string and an integer as arguments. It splits the string into a slice of strings and converts each string to an integer. The function subtracts the converted integer from the given integer and returns the result.
The function Rescale
The first excerpt discusses a corrected function for checking if there are any two elements in a vector that are closer than a given threshold. The function is modified to compute the absolute difference between numbers and start the inner loop from 'i+1' instead of
GPT-4 fixed a bug in the code but introduced two new bugs that caused unit tests to fail. The bug in the code was a missing "+1" after "m-n". GPT-4 fixed this bug but introduced two new bugs
For InstructCodeT5+, the prompt starts directly after "Response:" without a newline. The code provided is a solution to the "Special Array" problem on LeetCode, written in C++ and using several standard libraries. It defines a special
Models evaluated in the study are instruction tuned on text assessed by human raters, potentially leading to bias in generating longer texts. Evaluating code instruction models is challenging due to prompting, processing, execution, and comprehensiveness issues. Fair evaluation requires using