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name="order"><option selected value="-announced_date_first">Announcement date (newest first)</option><option value="announced_date_first">Announcement date (oldest first)</option><option value="-submitted_date">Submission date (newest first)</option><option value="submitted_date">Submission date (oldest first)</option><option value="">Relevance</option></select> </span> </div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2302.01973">arXiv:2302.01973</a> <span>&nbsp;[<a href="https://arxiv.org/pdf/2302.01973">pdf</a>, <a href="https://arxiv.org/format/2302.01973">other</a>]&nbsp;</span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Programming Languages">cs.PL</span> </div> </div> <p class="title is-5 mathjax"> Measuring The Impact Of Programming Language Distribution </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&amp;query=Orlanski%2C+G">Gabriel Orlanski</a>, <a href="/search/cs?searchtype=author&amp;query=Xiao%2C+K">Kefan Xiao</a>, <a href="/search/cs?searchtype=author&amp;query=Garcia%2C+X">Xavier Garcia</a>, <a href="/search/cs?searchtype=author&amp;query=Hui%2C+J">Jeffrey Hui</a>, <a href="/search/cs?searchtype=author&amp;query=Howland%2C+J">Joshua Howland</a>, <a href="/search/cs?searchtype=author&amp;query=Malmaud%2C+J">Jonathan Malmaud</a>, <a href="/search/cs?searchtype=author&amp;query=Austin%2C+J">Jacob Austin</a>, <a href="/search/cs?searchtype=author&amp;query=Singh%2C+R">Rishabh Singh</a>, <a href="/search/cs?searchtype=author&amp;query=Catasta%2C+M">Michele Catasta</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2302.01973v3-abstract-short" style="display: inline;"> Current benchmarks for evaluating neural code models focus on only a small subset of programming languages, excluding many popular languages such as Go or Rust. To ameliorate this issue, we present the BabelCode framework for execution-based evaluation of any benchmark in any language. BabelCode enables new investigations into the qualitative performance of models&#39; memory, runtime, and individual&hellip; <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.01973v3-abstract-full').style.display = 'inline'; document.getElementById('2302.01973v3-abstract-short').style.display = 'none';">&#9661; More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2302.01973v3-abstract-full" style="display: none;"> Current benchmarks for evaluating neural code models focus on only a small subset of programming languages, excluding many popular languages such as Go or Rust. To ameliorate this issue, we present the BabelCode framework for execution-based evaluation of any benchmark in any language. BabelCode enables new investigations into the qualitative performance of models&#39; memory, runtime, and individual test case results. Additionally, we present a new code translation dataset called Translating Python Programming Puzzles (TP3) from the Python Programming Puzzles (Schuster et al. 2021) benchmark that involves translating expert-level python functions to any language. With both BabelCode and the TP3 benchmark, we investigate if balancing the distributions of 14 languages in a training dataset improves a large language model&#39;s performance on low-resource languages. Training a model on a balanced corpus results in, on average, 12.34% higher $pass@k$ across all tasks and languages compared to the baseline. We find that this strategy achieves 66.48% better $pass@k$ on low-resource languages at the cost of only a 12.94% decrease to high-resource languages. In our three translation tasks, this strategy yields, on average, 30.77% better low-resource $pass@k$ while having 19.58% worse high-resource $pass@k$. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.01973v3-abstract-full').style.display = 'none'; document.getElementById('2302.01973v3-abstract-short').style.display = 'inline';">&#9651; Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted to ICML 2023, Code and data release: https://github.com/google-research/babelcode</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2211.07842">arXiv:2211.07842</a> <span>&nbsp;[<a href="https://arxiv.org/pdf/2211.07842">pdf</a>, <a href="https://arxiv.org/format/2211.07842">other</a>]&nbsp;</span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> </div> </div> <p class="title is-5 mathjax"> Evaluating How Fine-tuning on Bimodal Data Effects Code Generation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&amp;query=Orlanski%2C+G">Gabriel Orlanski</a>, <a href="/search/cs?searchtype=author&amp;query=Yang%2C+S">Seonhye Yang</a>, <a href="/search/cs?searchtype=author&amp;query=Healy%2C+M">Michael Healy</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2211.07842v1-abstract-short" style="display: inline;"> Despite the increase in popularity of language models for code generation, it is still unknown how training on bimodal coding forums affects a model&#39;s code generation performance and reliability. We, therefore, collect a dataset of over 2.2M StackOverflow questions with answers for finetuning. These fine-tuned models have average $pass@k$ improvements of 54.64% and 85.35% on the HumanEval (Chen et&hellip; <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.07842v1-abstract-full').style.display = 'inline'; document.getElementById('2211.07842v1-abstract-short').style.display = 'none';">&#9661; More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.07842v1-abstract-full" style="display: none;"> Despite the increase in popularity of language models for code generation, it is still unknown how training on bimodal coding forums affects a model&#39;s code generation performance and reliability. We, therefore, collect a dataset of over 2.2M StackOverflow questions with answers for finetuning. These fine-tuned models have average $pass@k$ improvements of 54.64% and 85.35% on the HumanEval (Chen et al., 2021) and Mostly Basic Program Problems (Austin et al., 2021) tasks, respectively. This regime further decreases the number of generated programs with both syntax and runtime errors. However, we find that at higher temperatures, there are significant decreases to the model&#39;s ability to generate runnable programs despite higher $pass@k$ scores, underscoring the need for better methods of incorporating such data that mitigate these side effects. The code can be found https://github.com/gabeorlanski/bimodalcode-generation <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.07842v1-abstract-full').style.display = 'none'; document.getElementById('2211.07842v1-abstract-short').style.display = 'inline';">&#9651; Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">4 pages, 4 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2203.15754">arXiv:2203.15754</a> <span>&nbsp;[<a href="https://arxiv.org/pdf/2203.15754">pdf</a>, <a href="https://arxiv.org/format/2203.15754">other</a>]&nbsp;</span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Evaluating Prompts Across Multiple Choice Tasks In a Zero-Shot Setting </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&amp;query=Orlanski%2C+G">Gabriel Orlanski</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2203.15754v1-abstract-short" style="display: inline;"> Large language models have shown that impressive zero-shot performance can be achieved through natural language prompts (Radford et al., 2019; Brown et al., 2020; Sanh et al., 2021). Creating an effective prompt, however, requires significant trial and error. That \textit{prompts} the question: how do the qualities of a prompt effects its performance? To this end, we collect and standardize prompt&hellip; <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.15754v1-abstract-full').style.display = 'inline'; document.getElementById('2203.15754v1-abstract-short').style.display = 'none';">&#9661; More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2203.15754v1-abstract-full" style="display: none;"> Large language models have shown that impressive zero-shot performance can be achieved through natural language prompts (Radford et al., 2019; Brown et al., 2020; Sanh et al., 2021). Creating an effective prompt, however, requires significant trial and error. That \textit{prompts} the question: how do the qualities of a prompt effects its performance? To this end, we collect and standardize prompts from a diverse range of tasks for use with tasks they were not designed for. We then evaluate these prompts across fixed multiple choice datasets for a quantitative analysis of how certain attributes of a prompt affect performance. We find that including the choices and using prompts not used during pre-training provide significant improvements. All experiments and code can be found https://github.com/gabeorlanski/zero-shot-cross-task. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.15754v1-abstract-full').style.display = 'none'; document.getElementById('2203.15754v1-abstract-short').style.display = 'inline';">&#9651; Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 March, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">4 pages, 4 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2106.04447">arXiv:2106.04447</a> <span>&nbsp;[<a href="https://arxiv.org/pdf/2106.04447">pdf</a>, <a href="https://arxiv.org/format/2106.04447">other</a>]&nbsp;</span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Reading StackOverflow Encourages Cheating: Adding Question Text Improves Extractive Code Generation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&amp;query=Orlanski%2C+G">Gabriel Orlanski</a>, <a href="/search/cs?searchtype=author&amp;query=Gittens%2C+A">Alex Gittens</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2106.04447v1-abstract-short" style="display: inline;"> Answering a programming question using only its title is difficult as salient contextual information is omitted. Based on this observation, we present a corpus of over 40,000 StackOverflow question texts to be used in conjunction with their corresponding intents from the CoNaLa dataset (Yin et al., 2018). Using both the intent and question body, we use BART to establish a baseline BLEU score of 34&hellip; <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.04447v1-abstract-full').style.display = 'inline'; document.getElementById('2106.04447v1-abstract-short').style.display = 'none';">&#9661; More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2106.04447v1-abstract-full" style="display: none;"> Answering a programming question using only its title is difficult as salient contextual information is omitted. Based on this observation, we present a corpus of over 40,000 StackOverflow question texts to be used in conjunction with their corresponding intents from the CoNaLa dataset (Yin et al., 2018). Using both the intent and question body, we use BART to establish a baseline BLEU score of 34.35 for this new task. We find further improvements of $2.8\%$ by combining the mined CoNaLa data with the labeled data to achieve a 35.32 BLEU score. We evaluate prior state-of-the-art CoNaLa models with this additional data and find that our proposed method of using the body and mined data beats the BLEU score of the prior state-of-the-art by $71.96\%$. Finally, we perform ablations to demonstrate that BART is an unsupervised multimodal learner and examine its extractive behavior. The code and data can be found https://github.com/gabeorlanski/stackoverflow-encourages-cheating. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.04447v1-abstract-full').style.display = 'none'; document.getElementById('2106.04447v1-abstract-short').style.display = 'inline';">&#9651; Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 June, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">To be published in ACL-IJCNLP NLP4Prog workshop. (The First Workshop on Natural Language Processing for Programming)</span> </p> </li> </ol> <div class="is-hidden-tablet"> <!-- feedback for mobile only --> <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>&nbsp;&nbsp;</span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary"> <!-- MetaColumn 1 --> <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul class="nav-spaced"> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>contact arXiv</title><desc>Click here to contact arXiv</desc><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 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