Note
Go to the end to download the full example code.
Automatically converts a topic or question of interests into a survey over relevant papers
When searching for research papers, the results from a search engine can vary significantly depending on the specific keywords used, even if those keywords are conceptually similar. For instance, searching for “LLMs” versus “Large Language Models” may yield different sets of papers. Additionally, when experimenting with new keywords, it can be challenging to remember whether a particular paper has already been checked. Furthermore, the process of downloading papers and organizing them with appropriate filenames can be tedious and time-consuming.
The function topic_to_survey streamlines the entire process by automating several key tasks.
It suggests multiple related keywords to ensure comprehensive coverage of the topic,
merges duplicate results to avoid redundancy, automatically names downloaded files
using the paper titles for easy reference, and automatically ranks the papers based on their impacts
(see auto_research.search.core.AutoSearch.score_threshold). Moreover, it leverages LLMs
to generate summaries of each paper, saving researchers valuable time and effort.
This script demonstrates the usage of the topic_to_survey function from the auto_research.applications.surveys module to:
Conduct an automated research process based on a user-provided topic.
Generate and refine a list of keywords for searching research articles.
Retrieve and download articles based on the specified search criteria.
Rank, organize, and summarize the downloaded articles.
Check the code availability of the summarized articles (optional).
To get started with the package, you need to set up API keys. For detailed instructions, see Setting up API keys for LLMs.
This script assumes that:
A valid
key.jsonfile is available (located at the current working directory (“”))
The process involves user interaction, including selecting keywords, summarizing articles, and optionally checking code availability.
Below is an example output from the following input:
generate code with LLMs
select
1,3
select
2,3
yes
Please enter your research topic or question (e.g., 'Applications of AI in healthcare'): Sequence generation under testing: attempt 1 of 3
Operation under time limit: attempt 1 of 3
The operation finishes in time
Test passed
Suggested keywords for searching articles based on your input:
1. code generation with language models
2. code generation with large language models
3. code synthesis using language models
4. automatic code generation
5. program synthesis with large language models
6. machine learning for code generation
7. deep learning for code generation
8. code completion using language models
9. program generation with LLMs
10. AI-assisted code generation
How would you like to proceed with the suggested keywords?
1. 'all': Use all the suggested keywords for searching.
2. 'select': Choose specific keywords by their ranks.
3. 'custom': Enter your own list of keywords manually.
Choose an option ('all', 'select', or 'custom'):
Available keywords with their ranks:
1. code generation with language models
2. code generation with large language models
3. code synthesis using language models
4. automatic code generation
5. program synthesis with large language models
6. machine learning for code generation
7. deep learning for code generation
8. code completion using language models
9. program generation with LLMs
10. AI-assisted code generation
Enter the ranks of the keywords you want to use, separated by commas (e.g., 1,3,5):
Using the following keywords: ['code generation with language models', 'code synthesis using language models']
Final keywords to search: ['code generation with language models', 'code synthesis using language models']
------Searching for the 1th keyword 'code generation with language models'------
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Paper 1:
Title: Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation
Abstract:
Program synthesis has been long studied with recent approaches focused on
directly using the power of Large Language Models (LLMs) to generate code.
Programming benchmarks, with curated synthesis problems and test-cases, are
used to measure the performance of various LLMs on code synthesis. However,
these test-cases can be limited in both quantity and quality for fully
assessing the functional correctness of the generated code. Such limitation in
the existing benchmarks begs the following question: In the era of LLMs, is the
code generated really correct? To answer this, we propose EvalPlus -- a code
synthesis evaluation framework to rigorously benchmark the functional
correctness of LLM-synthesized code. EvalPlus augments a given evaluation
dataset with large amounts of test-cases newly produced by an automatic test
input generator, powered by both LLM- and mutation-based strategies. While
EvalPlus is general, we extend the test-cases of the popular HumanEval
benchmark by 80x to build HumanEval+. Our extensive evaluation across 26
popular LLMs (e.g., GPT-4 and ChatGPT) demonstrates that HumanEval+ is able to
catch significant amounts of previously undetected wrong code synthesized by
LLMs, reducing the pass@k by up-to 19.3-28.9%. We also surprisingly found that
test insufficiency can lead to mis-ranking. For example, both
WizardCoder-CodeLlama and Phind-CodeLlama now outperform ChatGPT on HumanEval+,
while none of them could on HumanEval. Our work not only indicates that prior
popular code synthesis evaluation results do not accurately reflect the true
performance of LLMs for code synthesis, but also opens up a new direction to
improve such programming benchmarks through automated testing. We have
open-sourced our tools, enhanced datasets as well as all LLM-generated code at
https://github.com/evalplus/evalplus to facilitate and accelerate future
LLM-for-code research.
Combined Score: 16.636241089982548
Citation count: 778
Year of publication: 2023
Publication venue: Advances in Neural …
Authors: J Liu, CS Xia, Y Wang, L Zhang
Link: https://proceedings.neurips.cc/paper_files/paper/2023/file/43e9d647ccd3e4b7b5baab53f0368686-Paper-Conference.pdf
ArXiv Link: http://arxiv.org/pdf/2305.01210v3
Downloading Is your code generated by chatgpt really correct rigorous evaluation of large language models for code generation.pdf... with upper time limit: 10 seconds
Downloaded: Is your code generated by chatgpt really correct rigorous evaluation of large language models for code generation.pdf.
Paper 2:
Title: Self-planning code generation with large language models
Abstract:
Large language models have demonstrated the ability to generate both natural
language and programming language text. Such models open up the possibility of
multi-language code generation: could code generation models generalize
knowledge from one language to another? Although contemporary code generation
models can generate semantically correct Python code, little is known about
their abilities with other languages. We propose MultiPL-E, a system for
translating unit test-driven code generation benchmarks to new languages. We
create the first massively multilingual code generation benchmark by using
MultiPL-E to translate two popular Python code generation benchmarks to 18
additional programming languages.
We use MultiPL-E to extend the HumanEval benchmark and MBPP benchmark to 18
languages that encompass a range of programming paradigms and popularity. Using
these new parallel benchmarks, we evaluate the multi-language performance of
three state-of-the-art code generation models: Codex, CodeGen, and InCoder. We
find that Codex matches or even exceeds its performance on Python for several
other languages. The range of programming languages represented in MultiPL-E
allow us to explore the impact of language frequency and language features on
model performance. Finally, the MultiPL-E approach of compiling code generation
benchmarks to new programming languages is both scalable and extensible, making
it straightforward to evaluate new models, benchmarks, and languages.
Combined Score: 11.932426932522988
Citation count: 135
Year of publication: 2024
Publication venue: ACM Transactions on …
Authors: X Jiang, Y Dong, L Wang, Z Fang, Q Shang
Link: https://dl.acm.org/doi/full/10.1145/3672456
ArXiv Link: http://arxiv.org/pdf/2208.08227v4
Downloading Self-planning code generation with large language models.pdf... with upper time limit: 10 seconds
Downloaded: Self-planning code generation with large language models.pdf.
/home/j/experiments/auto_research/auto_research/search/files_management.py:55: UserWarning: Error opening PDF: Failed to open file 'papers/Self-planning code generation with large language models.pdf'.
warnings.warn(f"Error opening PDF: {e}", UserWarning)
The downloaded PDF file 'Self-planning code generation with large language models.pdf' is corrupted.
File removed: Self-planning code generation with large language models.pdf
Trying to download from ArXiv link: http://arxiv.org/pdf/2208.08227v4
Downloading Self-planning code generation with large language models.pdf... with upper time limit: 10 seconds
Downloaded: Self-planning code generation with large language models.pdf.
Paper 3:
Title: A survey on large language models for code generation
Abstract:
Large Language Models (LLMs) have garnered remarkable advancements across
diverse code-related tasks, known as Code LLMs, particularly in code generation
that generates source code with LLM from natural language descriptions. This
burgeoning field has captured significant interest from both academic
researchers and industry professionals due to its practical significance in
software development, e.g., GitHub Copilot. Despite the active exploration of
LLMs for a variety of code tasks, either from the perspective of natural
language processing (NLP) or software engineering (SE) or both, there is a
noticeable absence of a comprehensive and up-to-date literature review
dedicated to LLM for code generation. In this survey, we aim to bridge this gap
by providing a systematic literature review that serves as a valuable reference
for researchers investigating the cutting-edge progress in LLMs for code
generation. We introduce a taxonomy to categorize and discuss the recent
developments in LLMs for code generation, covering aspects such as data
curation, latest advances, performance evaluation, ethical implications,
environmental impact, and real-world applications. In addition, we present a
historical overview of the evolution of LLMs for code generation and offer an
empirical comparison using the HumanEval, MBPP, and BigCodeBench benchmarks
across various levels of difficulty and types of programming tasks to highlight
the progressive enhancements in LLM capabilities for code generation. We
identify critical challenges and promising opportunities regarding the gap
between academia and practical development. Furthermore, we have established a
dedicated resource GitHub page (https://github.com/juyongjiang/CodeLLMSurvey)
to continuously document and disseminate the most recent advances in the field.
Combined Score: 10.16465997955662
Citation count: 115
Year of publication: 2024
Publication venue: arXiv preprint arXiv:2406.00515
Authors: J Jiang, F Wang, J Shen, S Kim, S Kim
Link: https://arxiv.org/pdf/2406.00515
ArXiv Link: http://arxiv.org/pdf/2406.00515v2
Downloading A survey on large language models for code generation.pdf... with upper time limit: 10 seconds
Downloaded: A survey on large language models for code generation.pdf.
Paper 4:
Title: Planning with large language models for code generation
Abstract:
Developing domain models is one of the few remaining places that require
manual human labor in AI planning. Thus, in order to make planning more
accessible, it is desirable to automate the process of domain model generation.
To this end, we investigate if large language models (LLMs) can be used to
generate planning domain models from simple textual descriptions. Specifically,
we introduce a framework for automated evaluation of LLM-generated domains by
comparing the sets of plans for domain instances. Finally, we perform an
empirical analysis of 7 large language models, including coding and chat models
across 9 different planning domains, and under three classes of natural
language domain descriptions. Our results indicate that LLMs, particularly
those with high parameter counts, exhibit a moderate level of proficiency in
generating correct planning domains from natural language descriptions. Our
code is available at https://github.com/IBM/NL2PDDL.
Combined Score: 3.2930348687111985
Citation count: 154
Year of publication: 2023
Publication venue: arXiv preprint arXiv …
Authors: S Zhang, Z Chen, Y Shen, M Ding
Link: https://arxiv.org/pdf/2303.05510
ArXiv Link: http://arxiv.org/pdf/2405.06650v1
Downloading Planning with large language models for code generation.pdf... with upper time limit: 10 seconds
Downloaded: Planning with large language models for code generation.pdf.
Paper 5:
Title: A survey on evaluating large language models in code generation tasks
Abstract:
This paper provides a comprehensive review of the current methods and metrics
used to evaluate the performance of Large Language Models (LLMs) in code
generation tasks. With the rapid growth in demand for automated software
development, LLMs have demonstrated significant potential in the field of code
generation. The paper begins by reviewing the historical development of LLMs
and their applications in code generation. Next, it details various methods and
metrics for assessing the code generation capabilities of LLMs, including code
correctness, efficiency, readability, and evaluation methods based on expert
review and user experience. The paper also evaluates the widely used benchmark
datasets, identifying their limitations and proposing directions for future
improvements. Specifically, the paper analyzes the performance of code
generation models across different tasks by combining multiple evaluation
metrics, such as code compilation/interpretation success rates, unit test pass
rates, and performance and efficiency metrics, to comprehensively assess the
practical application of LLMs in code generation. Finally, the paper discusses
the challenges faced in evaluating LLMs in code generation, particularly how to
ensure the comprehensiveness and accuracy of evaluation methods and how to
adapt to the evolving practices of software development. These analyses and
discussions provide valuable insights for further optimizing and improving the
application of LLMs in code generation tasks.
Combined Score: 0.795495128834866
Citation count: 9
Year of publication: 2024
Publication venue: arXiv.org
Authors: L Chen, Q Guo, H Jia, Z Zeng, X Wang, Y Xu
Link: https://arxiv.org/pdf/2408.16498
ArXiv Link: http://arxiv.org/pdf/2408.16498v1
Downloading A survey on evaluating large language models in code generation tasks.pdf... with upper time limit: 10 seconds
Downloaded: A survey on evaluating large language models in code generation tasks.pdf.
The above displays all paper with a combined score no less than 0
Metadata saved to papers/metadata.json
Folder saved to papers.zip
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Paper 1:
Title: Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation
Abstract:
Program synthesis has been long studied with recent approaches focused on
directly using the power of Large Language Models (LLMs) to generate code.
Programming benchmarks, with curated synthesis problems and test-cases, are
used to measure the performance of various LLMs on code synthesis. However,
these test-cases can be limited in both quantity and quality for fully
assessing the functional correctness of the generated code. Such limitation in
the existing benchmarks begs the following question: In the era of LLMs, is the
code generated really correct? To answer this, we propose EvalPlus -- a code
synthesis evaluation framework to rigorously benchmark the functional
correctness of LLM-synthesized code. EvalPlus augments a given evaluation
dataset with large amounts of test-cases newly produced by an automatic test
input generator, powered by both LLM- and mutation-based strategies. While
EvalPlus is general, we extend the test-cases of the popular HumanEval
benchmark by 80x to build HumanEval+. Our extensive evaluation across 26
popular LLMs (e.g., GPT-4 and ChatGPT) demonstrates that HumanEval+ is able to
catch significant amounts of previously undetected wrong code synthesized by
LLMs, reducing the pass@k by up-to 19.3-28.9%. We also surprisingly found that
test insufficiency can lead to mis-ranking. For example, both
WizardCoder-CodeLlama and Phind-CodeLlama now outperform ChatGPT on HumanEval+,
while none of them could on HumanEval. Our work not only indicates that prior
popular code synthesis evaluation results do not accurately reflect the true
performance of LLMs for code synthesis, but also opens up a new direction to
improve such programming benchmarks through automated testing. We have
open-sourced our tools, enhanced datasets as well as all LLM-generated code at
https://github.com/evalplus/evalplus to facilitate and accelerate future
LLM-for-code research.
Combined Score: 16.636241089982548
Citation count: 778
Year of publication: 2023
Publication venue: Advances in Neural …
Authors: J Liu, CS Xia, Y Wang, L Zhang
Link: https://proceedings.neurips.cc/paper_files/paper/2023/file/43e9d647ccd3e4b7b5baab53f0368686-Paper-Conference.pdf
ArXiv Link: http://arxiv.org/pdf/2305.01210v3
Downloading Is your code generated by chatgpt really correct rigorous evaluation of large language models for code generation.pdf... with upper time limit: 10 seconds
Downloaded: Is your code generated by chatgpt really correct rigorous evaluation of large language models for code generation.pdf.
Paper 2:
Title: A systematic evaluation of large language models of code
Abstract:
Language models (LMs) have exhibited impressive abilities in generating codes
from natural language requirements. In this work, we highlight the diversity of
code generated by LMs as a critical criterion for evaluating their code
generation capabilities, in addition to functional correctness. Despite its
practical implications, there is a lack of studies focused on assessing the
diversity of generated code, which overlooks its importance in the development
of code LMs. We propose a systematic approach to evaluate the diversity of
generated code, utilizing various metrics for inter-code similarity as well as
functional correctness. Specifically, we introduce a pairwise code similarity
measure that leverages large LMs' capabilities in code understanding and
reasoning, demonstrating the highest correlation with human judgment. We
extensively investigate the impact of various factors on the quality of
generated code, including model sizes, temperatures, training approaches,
prompting strategies, and the difficulty of input problems. Our consistent
observation of a positive correlation between the test pass score and the
inter-code similarity score indicates that current LMs tend to produce
functionally correct code with limited diversity.
Combined Score: 5.3984375
Citation count: 691
Year of publication: 2022
Publication venue: Proceedings of the 6th ACM …
Authors: FF Xu, U Alon, G Neubig, VJ Hellendoorn
Link: https://dl.acm.org/doi/pdf/10.1145/3520312.3534862
ArXiv Link: http://arxiv.org/pdf/2408.14504v1
Downloading A systematic evaluation of large language models of code.pdf... with upper time limit: 10 seconds
Downloaded: A systematic evaluation of large language models of code.pdf.
Paper 3:
Title: Program synthesis with large language models
Abstract:
GitHub Copilot, an extension for the Visual Studio Code development
environment powered by the large-scale language model Codex, makes automatic
program synthesis available for software developers. This model has been
extensively studied in the field of deep learning, however, a comparison to
genetic programming, which is also known for its performance in automatic
program synthesis, has not yet been carried out. In this paper, we evaluate
GitHub Copilot on standard program synthesis benchmark problems and compare the
achieved results with those from the genetic programming literature. In
addition, we discuss the performance of both approaches. We find that the
performance of the two approaches on the benchmark problems is quite similar,
however, in comparison to GitHub Copilot, the program synthesis approaches
based on genetic programming are not yet mature enough to support programmers
in practical software development. Genetic programming usually needs a huge
amount of expensive hand-labeled training cases and takes too much time to
generate solutions. Furthermore, source code generated by genetic programming
approaches is often bloated and difficult to understand. For future work on
program synthesis with genetic programming, we suggest researchers to focus on
improving the execution time, readability, and usability.
Combined Score: 5.219877086675509
Citation count: 1459
Year of publication: 2021
Publication venue: arXiv.org
Authors: J Austin, A Odena, M Nye, M Bosma
Link: https://arxiv.org/pdf/2108.07732
ArXiv Link: http://arxiv.org/pdf/2111.07875v1
Downloading Program synthesis with large language models.pdf... with upper time limit: 10 seconds
Downloaded: Program synthesis with large language models.pdf.
Paper 4:
Title: Jigsaw: Large language models meet program synthesis
Abstract:
Large pre-trained language models such as GPT-3, Codex, and Google's language
model are now capable of generating code from natural language specifications
of programmer intent. We view these developments with a mixture of optimism and
caution. On the optimistic side, such large language models have the potential
to improve productivity by providing an automated AI pair programmer for every
programmer in the world. On the cautionary side, since these large language
models do not understand program semantics, they offer no guarantees about
quality of the suggested code. In this paper, we present an approach to augment
these large language models with post-processing steps based on program
analysis and synthesis techniques, that understand the syntax and semantics of
programs. Further, we show that such techniques can make use of user feedback
and improve with usage. We present our experiences from building and evaluating
such a tool jigsaw, targeted at synthesizing code for using Python Pandas API
using multi-modal inputs. Our experience suggests that as these large language
models evolve for synthesizing code from intent, jigsaw has an important role
to play in improving the accuracy of the systems.
Combined Score: 1.7890625
Citation count: 229
Year of publication: 2022
Publication venue: International Conference on Software Engineering
Authors: N Jain, S Vaidyanath, A Iyer, N Natarajan
Link: https://arxiv.org/pdf/2112.02969
ArXiv Link: http://arxiv.org/pdf/2112.02969v1
Downloading Jigsaw Large language models meet program synthesis.pdf... with upper time limit: 10 seconds
Downloaded: Jigsaw Large language models meet program synthesis.pdf.
Paper 5:
Title: A hazard analysis framework for code synthesis large language models
Abstract:
Codex, a large language model (LLM) trained on a variety of codebases,
exceeds the previous state of the art in its capacity to synthesize and
generate code. Although Codex provides a plethora of benefits, models that may
generate code on such scale have significant limitations, alignment problems,
the potential to be misused, and the possibility to increase the rate of
progress in technical fields that may themselves have destabilizing impacts or
have misuse potential. Yet such safety impacts are not yet known or remain to
be explored. In this paper, we outline a hazard analysis framework constructed
at OpenAI to uncover hazards or safety risks that the deployment of models like
Codex may impose technically, socially, politically, and economically. The
analysis is informed by a novel evaluation framework that determines the
capacity of advanced code generation techniques against the complexity and
expressivity of specification prompts, and their capability to understand and
execute them relative to human ability.
Combined Score: 0.2109375
Citation count: 27
Year of publication: 2022
Publication venue: arXiv.org
Authors: H Khlaaf, P Mishkin, J Achiam, G Krueger
Link: https://arxiv.org/pdf/2207.14157
ArXiv Link: http://arxiv.org/pdf/2207.14157v1
Downloading A hazard analysis framework for code synthesis large language models.pdf... with upper time limit: 10 seconds
Downloaded: A hazard analysis framework for code synthesis large language models.pdf.
The above displays all paper with a combined score no less than 0
Metadata saved to papers/metadata.json
Folder saved to papers.zip
How would you like to summarize the papers?
1. 'all': Summarize all papers in the organized folder.
2. 'select': Choose specific papers by their ranks to summarize.
Choose an option ('all' or 'select'):
Available papers with their ranks:
1. 001_16.6_Is your code generated by chatgpt really correct rigorous evaluation of large language models for code generation.pdf
2. 002_11.9_Self-planning code generation with large language models.pdf
3. 003_10.2_A survey on large language models for code generation.pdf
4. 004_5.4_A systematic evaluation of large language models of code.pdf
5. 005_5.22_Program synthesis with large language models.pdf
6. 006_3.29_Planning with large language models for code generation.pdf
7. 007_1.79_Jigsaw Large language models meet program synthesis.pdf
8. 008_0.795_A survey on evaluating large language models in code generation tasks.pdf
9. 009_0.211_A hazard analysis framework for code synthesis large language models.pdf
Enter the ranks of the papers you want to summarize, separated by commas (e.g., 1,3,5):
Summarizing the following papers: ['002_11.9_Self-planning code generation with large language models.pdf', '003_10.2_A survey on large language models for code generation.pdf']
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The summary is:
1. The main topic: The paper introduces MultiPL-E, the first massively parallel, multi-language benchmark system aimed at evaluating code generation models across multiple programming languages by translating established Python benchmarks.
2. Existing problems: Previous studies predominantly focused on evaluating code generation models using Python, limiting generalizability and failing to consider the performance of models across a diverse set of programming languages. Additionally, existing benchmarks lacked the complexity and representativeness needed for real-world applications and varied programming environments.
3. The main contributions: The authors developed MultiPL-E to translate the unit test-driven code generation benchmarks HumanEval and MBPP into 18 different languages, facilitating a comparative analysis of code generation models such as Codex, CodeGen, and InCoder across diverse programming paradigms. This approach allows for scalable evaluation and is straightforward to extend for new languages and problems.
4. Experimental results: The study evaluates the performance of three state-of-the-art models on the new multilingual benchmarks. Results show that Codex performs comparably to its Python performance across several languages, particularly excelling in JavaScript. The evaluation also highlights the effects of language features and frequency on model performance.
5. Conclusions: The findings affirm that code generation models like Codex can effectively generalize across languages, exposing common error patterns in code generation that resemble those of human programmers. The paper emphasizes the importance of evaluating models with a diverse language set and offers a publicly available benchmark to aid future research in multi-language code generation models. Future work should focus on improving prompt designs and exploring the impact of specific programming language features on code generation performance.
The total cost is 0.0036958499999999997 USD
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The summary is:
1. The main topic: The paper provides a systematic literature review focused on Large Language Models (LLMs) specifically for code generation, addressing the natural language-to-code (NL2Code) task and its advancements in software development.
2. Existing problems: Previous studies have often lacked a comprehensive, up-to-date review dedicated solely to code generation by LLMs, with many existing surveys covering a broad range of code-related tasks rather than delving deeply into advanced topics and contemporary models.
3. The main contributions: This paper introduces a taxonomy to categorize recent advances in LLMs for code generation, covering areas such as data curation, performance evaluation, and real-world applications. It also provides a historical overview of model development and identifies critical challenges and opportunities to bridge the gap between research and practical applications.
4. Experimental results: The paper presents empirical comparisons using benchmarks such as HumanEval, MBPP, and BigCodeBench, showcasing progress in LLM capabilities across various programming tasks. It highlights the dramatic improvements in metrics like Pass@1, marking the evolution of performance from earlier models to state-of-the-art implementations.
5. Conclusions: The findings indicate significant advancements in LLM capabilities for code generation, particularly in democratizing programming for non-experts. The survey suggests that continued exploration of challenging topics and practical applications is essential for bridging theoretical research and industry needs, advocating for ongoing documentation of advancements through a dedicated GitHub resource.
The total cost is 0.00307935 USD
Total cost for summarizing all files: 0.0067751999999999995
The summaries for all selected files are printed below:
------Paper title: 002_11.9_Self-planning code generation with large language models.pdf------
1. The main topic: The paper introduces MultiPL-E, the first massively parallel, multi-language benchmark system aimed at evaluating code generation models across multiple programming languages by translating established Python benchmarks.
2. Existing problems: Previous studies predominantly focused on evaluating code generation models using Python, limiting generalizability and failing to consider the performance of models across a diverse set of programming languages. Additionally, existing benchmarks lacked the complexity and representativeness needed for real-world applications and varied programming environments.
3. The main contributions: The authors developed MultiPL-E to translate the unit test-driven code generation benchmarks HumanEval and MBPP into 18 different languages, facilitating a comparative analysis of code generation models such as Codex, CodeGen, and InCoder across diverse programming paradigms. This approach allows for scalable evaluation and is straightforward to extend for new languages and problems.
4. Experimental results: The study evaluates the performance of three state-of-the-art models on the new multilingual benchmarks. Results show that Codex performs comparably to its Python performance across several languages, particularly excelling in JavaScript. The evaluation also highlights the effects of language features and frequency on model performance.
5. Conclusions: The findings affirm that code generation models like Codex can effectively generalize across languages, exposing common error patterns in code generation that resemble those of human programmers. The paper emphasizes the importance of evaluating models with a diverse language set and offers a publicly available benchmark to aid future research in multi-language code generation models. Future work should focus on improving prompt designs and exploring the impact of specific programming language features on code generation performance.
------Paper title: 003_10.2_A survey on large language models for code generation.pdf------
1. The main topic: The paper provides a systematic literature review focused on Large Language Models (LLMs) specifically for code generation, addressing the natural language-to-code (NL2Code) task and its advancements in software development.
2. Existing problems: Previous studies have often lacked a comprehensive, up-to-date review dedicated solely to code generation by LLMs, with many existing surveys covering a broad range of code-related tasks rather than delving deeply into advanced topics and contemporary models.
3. The main contributions: This paper introduces a taxonomy to categorize recent advances in LLMs for code generation, covering areas such as data curation, performance evaluation, and real-world applications. It also provides a historical overview of model development and identifies critical challenges and opportunities to bridge the gap between research and practical applications.
4. Experimental results: The paper presents empirical comparisons using benchmarks such as HumanEval, MBPP, and BigCodeBench, showcasing progress in LLM capabilities across various programming tasks. It highlights the dramatic improvements in metrics like Pass@1, marking the evolution of performance from earlier models to state-of-the-art implementations.
5. Conclusions: The findings indicate significant advancements in LLM capabilities for code generation, particularly in democratizing programming for non-experts. The survey suggests that continued exploration of challenging topics and practical applications is essential for bridging theoretical research and industry needs, advocating for ongoing documentation of advancements through a dedicated GitHub resource.
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Checking code availability for the summarized articles...
Checking code availability for: 002_11.9_Self-planning code generation with large language models.pdf
Sequence generation under testing: attempt 1 of 3
Operation under time limit: attempt 1 of 3
The operation finishes in time
Test passed
The retrieved information is:
https://github.com/nuprl/MultiPL-E
The total cost is 0.005967599999999999 USD
Checking code availability for: 003_10.2_A survey on large language models for code generation.pdf
Sequence generation under testing: attempt 1 of 3
Operation under time limit: attempt 1 of 3
The operation finishes in time
Test passed
The retrieved information is:
https://github.com/juyongjiang/CodeLLMSurvey
The total cost is 0.01090695 USD
Total cost for checking code availability: 0.01687455 USD
Total cost for the entire process (summaries + code availability check): 0.023649749999999997 USD
from auto_research.applications.surveys import topic_to_survey
if __name__ == "__main__":
"""
Main execution block for the `topic_to_survey` function.
This block initializes the `topic_to_survey` function with the specified parameters and runs the automated research process.
Example:
# Sample usage:
topic_to_survey(
num_results=5,
sort_by="relevance",
date_cutoff="2024-12-01",
score_threshold=0,
destination_folder="papers",
model="gpt-4o-mini",
api_key_path="",
api_key_type="OpenAI",
organize_files=True,
order_by_score=True,
zip_folder=True,
api_key=None, # Directly provide the API key as a string. If None, the key will be retrieved from the file.
)
Parameters
----------
num_results : int, optional
Number of search results to retrieve. Defaults to 30.
sort_by : str, optional
Sorting criteria for search results. Options: "relevance", "date". Defaults to "relevance".
date_cutoff : str, optional
Cutoff date for search results. Only articles published before this date will be included. Defaults to "2024-12-01". Only relevant when `sort_by` is set as "date".
score_threshold : float, optional
Minimum score threshold for articles. Articles with a score below this will be excluded. Defaults to 0.5.
destination_folder : str, optional
Folder to store downloaded articles. Defaults to "papers".
model : str, optional
Model to use for summarization and keyword suggestions. Defaults to "gpt-4o-mini".
api_key_path : str, optional
Path to the directory containing the API key. Defaults to "../". Set it as "" if the file is located at the current directory.
api_key_type : str, optional
Type of API key to retrieve. Options: "OpenAI", "DeepSeek". Defaults to "OpenAI".
organize_files : bool, optional
Whether to organize the downloaded articles into subfolders based on their rank and score. Defaults to True.
order_by_score : bool, optional
Whether to order articles by their score when organizing. Defaults to True.
zip_folder : bool, optional
Whether to zip the organized folder after processing. Defaults to True.
api_key : str, optional
Directly provide the API key as a string. If None, the key will be retrieved from the file. Defaults to None.
Returns
-------
None
"""
topic_to_survey(
num_results=5,
sort_by="relevance",
date_cutoff="2024-12-01",
score_threshold=0,
destination_folder="papers",
model="gpt-4o-mini",
api_key_path="",
api_key_type="OpenAI",
organize_files=True,
order_by_score=True,
zip_folder=True,
api_key=None,
)
Total running time of the script: (3 minutes 53.159 seconds)