On the Impact of Language Selection for Training and Evaluating Programming Language Models

• URL: http://arxiv.org/abs/2308.13354v1
• Date: Fri, 25 Aug 2023 12:57:59 GMT
• Title: On the Impact of Language Selection for Training and Evaluating Programming Language Models
• Authors: Jonathan Katzy and Maliheh Izadi and Arie van Deursen
• Abstract summary: We evaluate the similarity of programming languages by analyzing their representations using a CodeBERT-based model. Our experiments reveal that token representation in languages such as C++, Python, and Java exhibit proximity to one another, whereas the same tokens in languages such as Mathematica and R display significant dissimilarity.
• Score: 16.125924759649106
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The recent advancements in Transformer-based Language Models have demonstrated significant potential in enhancing the multilingual capabilities of these models. The remarkable progress made in this domain not only applies to natural language tasks but also extends to the domain of programming languages. Despite the ability of these models to learn from multiple languages, evaluations typically focus on particular combinations of the same languages. In this study, we evaluate the similarity of programming languages by analyzing their representations using a CodeBERT-based model. Our experiments reveal that token representation in languages such as C++, Python, and Java exhibit proximity to one another, whereas the same tokens in languages such as Mathematica and R display significant dissimilarity. Our findings suggest that this phenomenon can potentially result in performance challenges when dealing with diverse languages. Thus, we recommend using our similarity measure to select a diverse set of programming languages when training and evaluating future models.

Related papers

• The Role of Language Imbalance in Cross-lingual Generalisation: Insights from Cloned Language Experiments [57.273662221547056]
  In this study, we investigate an unintuitive novel driver of cross-lingual generalisation: language imbalance. We observe that the existence of a predominant language during training boosts the performance of less frequent languages. As we extend our analysis to real languages, we find that infrequent languages still benefit from frequent ones, yet whether language imbalance causes cross-lingual generalisation there is not conclusive.
  arXiv  Detail & Related papers  (2024-04-11T17:58:05Z)
• Counterfactually Probing Language Identity in Multilingual Models [15.260518230218414]
  We use AlterRep, a method of counterfactual probing, to explore the internal structure of multilingual models. We find that, given a template in Language X, pushing towards Language Y systematically increases the probability of Language Y words.
  arXiv  Detail & Related papers  (2023-10-29T01:21:36Z)
• The Less the Merrier? Investigating Language Representation in Multilingual Models [8.632506864465501]
  We investigate the linguistic representation of different languages in multilingual models. We observe from our experiments that community-centered models perform better at distinguishing between languages in the same family for low-resource languages.
  arXiv  Detail & Related papers  (2023-10-20T02:26:34Z)
• L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models [102.00201523306986]
  We present L2CEval, a systematic evaluation of the language-to-code generation capabilities of large language models (LLMs) We analyze the factors that potentially affect their performance, such as model size, pretraining data, instruction tuning, and different prompting methods. In addition to assessing model performance, we measure confidence calibration for the models and conduct human evaluations of the output programs.
  arXiv  Detail & Related papers  (2023-09-29T17:57:00Z)
• Multi-lingual Evaluation of Code Generation Models [82.7357812992118]
  We present new benchmarks on evaluation code generation models: MBXP and Multilingual HumanEval, and MathQA-X. These datasets cover over 10 programming languages. We are able to assess the performance of code generation models in a multi-lingual fashion.
  arXiv  Detail & Related papers  (2022-10-26T17:17:06Z)
• Cross-Lingual Fine-Grained Entity Typing [26.973783464706447]
  We present a unified cross-lingual fine-grained entity typing model capable of handling over 100 languages. We analyze this model's ability to generalize to languages and entities unseen during training.
  arXiv  Detail & Related papers  (2021-10-15T03:22:30Z)
• Discovering Representation Sprachbund For Multilingual Pre-Training [139.05668687865688]
  We generate language representation from multilingual pre-trained models and conduct linguistic analysis. We cluster all the target languages into multiple groups and name each group as a representation sprachbund. Experiments are conducted on cross-lingual benchmarks and significant improvements are achieved compared to strong baselines.
  arXiv  Detail & Related papers  (2021-09-01T09:32:06Z)
• Towards Zero-shot Language Modeling [90.80124496312274]
  We construct a neural model that is inductively biased towards learning human languages. We infer this distribution from a sample of typologically diverse training languages. We harness additional language-specific side information as distant supervision for held-out languages.
  arXiv  Detail & Related papers  (2021-08-06T23:49:18Z)
• Automated Source Code Generation and Auto-completion Using Deep Learning: Comparing and Discussing Current Language-Model-Related Approaches [0.0]
  This paper compares different deep learning architectures to create and use language models based on programming code. We discuss each approach's different strengths and weaknesses and what gaps we find to evaluate the language models or apply them in a real programming context.
  arXiv  Detail & Related papers  (2020-09-16T15:17:04Z)
• Linguistic Typology Features from Text: Inferring the Sparse Features of World Atlas of Language Structures [73.06435180872293]
  We construct a recurrent neural network predictor based on byte embeddings and convolutional layers. We show that some features from various linguistic types can be predicted reliably.
  arXiv  Detail & Related papers  (2020-04-30T21:00:53Z)
• An Empirical Study of Factors Affecting Language-Independent Models [11.976665726887733]
  We show that language-independent models can be comparable to or even outperforms the models trained using monolingual data. We experiment language-independent models with many different languages and show that they are more suitable for typologically similar languages.
  arXiv  Detail & Related papers  (2019-12-30T22:41:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.