On the Generalizability of Transformer Models to Code Completions of Different Lengths | Paper Citation Record

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This paper cites Toward deep learning software repositories,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Toward deep learning software repositories,

Reference 1

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Observation 5cb7f04b-ecb4-4652-b78d-31d86138d0db · outbound

This paper cites An empirical study on the usage of transformer models for code completion,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths An empirical study on the usage of transformer models for code completion,

Reference 2

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Observation 9427dc71-e9df-4a93-a44e-611f0c72afbf · outbound

This paper cites Using pre-trained models to boost code review automa- tion,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Using pre-trained models to boost code review automa- tion,

Reference 3

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This paper cites Recommendations for datasets for source code summarization,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Recommendations for datasets for source code summarization,

Reference 4

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This paper cites An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation,

Reference 5

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This paper cites SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair.

On the Generalizability of Transformer Models to Code Completions of Different Lengths SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair

Reference 6

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This paper cites On learning meaningful assert statements for unit test cases,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths On learning meaningful assert statements for unit test cases,

Reference 7

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This paper cites Learning how to mutate source code from bug-fixes,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Learning how to mutate source code from bug-fixes,

Reference 8

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This paper cites A systematic literature review on the use of deep learning in software engineering research,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths A systematic literature review on the use of deep learning in software engineering research,

Reference 9

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This paper cites Using transfer learning for code- related tasks,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Using transfer learning for code- related tasks,

Reference 10

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This paper cites On learning meaningful code changes via neural machine translation,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths On learning meaningful code changes via neural machine translation,

Reference 11

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Observation 26057cc0-0743-478a-9a9e-3b462a395203 · outbound

This paper cites Attention is all you need,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Attention is all you need,

Reference 12

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This paper cites Learning internal representations by error propagation,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Learning internal representations by error propagation,

Reference 13

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Observation 6efc2ee2-c3aa-4aca-a5e6-c576ed35a91b · outbound

This paper cites Evaluating Large Language Models Trained on Code.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Evaluating Large Language Models Trained on Code

Reference 14

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This paper cites Program Synthesis with Large Language Models.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Program Synthesis with Large Language Models

Reference 15

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This paper cites InCoder: A Generative Model for Code Infilling and Synthesis.

On the Generalizability of Transformer Models to Code Completions of Different Lengths InCoder: A Generative Model for Code Infilling and Synthesis

Reference 16

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Observation 2c43d8f4-5c55-4daa-9abf-2e9c30b76dc1 · outbound

This paper cites Shortformer: Better Language Modeling using Shorter Inputs.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Shortformer: Better Language Modeling using Shorter Inputs

Reference 17

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This paper cites Train short, test long: Attention with linear biases enables input length extrapolation,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Train short, test long: Attention with linear biases enables input length extrapolation,

Reference 18

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Observation 11fdf168-1b64-4648-9457-f334f8e13992 · outbound

This paper cites Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context

Reference 19

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Observation 82d224e3-fd30-4e9e-b176-9b5bbf616f51 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 20

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Observation 46efd67f-8c2c-4493-9a63-a336ad53171f · outbound

This paper cites A Length-Extrapolatable Transformer.

On the Generalizability of Transformer Models to Code Completions of Different Lengths A Length-Extrapolatable Transformer

Reference 21

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This paper cites Github copilot your ai pair programmer,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Github copilot your ai pair programmer,

Reference 22

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This paper cites RoFormer: Enhanced Transformer with Rotary Position Embedding.

On the Generalizability of Transformer Models to Code Completions of Different Lengths RoFormer: Enhanced Transformer with Rotary Position Embedding

Reference 23

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This paper cites Neural Machine Translation of Rare Words with Subword Units.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Neural Machine Translation of Rare Words with Subword Units

Reference 24

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This paper cites On the naturalness of software,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths On the naturalness of software,

Reference 25

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This paper cites On the localness of software,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths On the localness of software,

Reference 26

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This paper cites When code completion fails: A case study on real-world completions,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths When code completion fails: A case study on real-world completions,

Reference 27

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This paper cites Code completion with statistical language models,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Code completion with statistical language models,

Reference 28

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This paper cites Learning from examples to improve code completion systems,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Learning from examples to improve code completion systems,

Reference 29

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This paper cites The hidden cost of code completion: Understand- ing the impact of the recommendation-list length on its efficiency,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths The hidden cost of code completion: Understand- ing the impact of the recommendation-list length on its efficiency,

Reference 30

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This paper cites CodeFill: Multi-token Code Completion by Jointly Learning from Structure and Naming Sequences.

On the Generalizability of Transformer Models to Code Completions of Different Lengths CodeFill: Multi-token Code Completion by Jointly Learning from Structure and Naming Sequences

Reference 31

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This paper cites Learning autocompletion from real- world datasets,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Learning autocompletion from real- world datasets,

Reference 32

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This paper cites Are deep neural networks the best choice for modeling source code?.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Are deep neural networks the best choice for modeling source code?

Reference 33

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This paper cites Towards a better code completion system by api grouping, filtering, and popularity-based ranking,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Towards a better code completion system by api grouping, filtering, and popularity-based ranking,

Reference 34

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This paper cites Combining program anal- ysis and statistical language model for code statement completion,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Combining program anal- ysis and statistical language model for code statement completion,

Reference 35

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This paper cites How program history can improve code completion,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths How program history can improve code completion,

Reference 36

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This paper cites Improving code completion with program history,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Improving code completion with program history,

Reference 37

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This paper cites Fast and memory-efficient neural code completion,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Fast and memory-efficient neural code completion,

Reference 38

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This paper cites Pythia: Ai- assisted code completion system,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Pythia: Ai- assisted code completion system,

Reference 39

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This paper cites A language model for statements of software code,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths A language model for statements of software code,

Reference 40

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This paper cites CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis.

On the Generalizability of Transformer Models to Code Completions of Different Lengths CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis

Reference 41

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This paper cites SantaCoder: don't reach for the stars!.

On the Generalizability of Transformer Models to Code Completions of Different Lengths SantaCoder: don't reach for the stars!

Reference 42

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This paper cites Jungloid mining: helping to navigate the api jungle,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Jungloid mining: helping to navigate the api jungle,

Reference 43

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This paper cites Codegen: An open large language model for code with multi-turn program synthesis,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Codegen: An open large language model for code with multi-turn program synthesis,

Reference 44

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This paper cites Measuring Coding Challenge Competence With APPS.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Measuring Coding Challenge Competence With APPS

Reference 45

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This paper cites On the relation between position informa- tion and sentence length in neural machine translation,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths On the relation between position informa- tion and sentence length in neural machine translation,

Reference 46

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This paper cites Location Attention for Extrapolation to Longer Sequences,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Location Attention for Extrapolation to Longer Sequences,

Reference 47

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This paper cites The EOS Decision and Length Extrapolation.

On the Generalizability of Transformer Models to Code Completions of Different Lengths The EOS Decision and Length Extrapolation

Reference 48

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On the Generalizability of Transformer Models to Code Completions of Different Lengths SHAPE: Shifted Absolute Position Embedding for Transformers

Reference 49

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This paper cites Cape: Encoding relative positions with continuous augmented positional embeddings,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Cape: Encoding relative positions with continuous augmented positional embeddings,

Reference 50

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This paper cites Analysis of positional encodings for neural machine translation,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Analysis of positional encodings for neural machine translation,

Reference 51

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This paper cites Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks,

Reference 52

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This paper cites Compositionality decomposed: How do neural networks generalise?.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Compositionality decomposed: How do neural networks generalise?

Reference 53

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This paper cites Sampling projects in github for MSR studies,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Sampling projects in github for MSR studies,

Reference 54

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This paper cites Unit Test Case Generation with Transformers and Focal Context.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Unit Test Case Generation with Transformers and Focal Context

Reference 55

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This paper cites Adam: A method for stochastic optimization,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Adam: A method for stochastic optimization,

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On the Generalizability of Transformer Models to Code Completions of Different Lengths X-transformers,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:51.156413Z

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On the Generalizability of Transformer Models to Code Completions of Different Lengths PyTorch Lightning,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:51.062084Z

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This paper cites The Curious Case of Neural Text Degeneration.

On the Generalizability of Transformer Models to Code Completions of Different Lengths The Curious Case of Neural Text Degeneration

Reference 59

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On the Generalizability of Transformer Models to Code Completions of Different Lengths Bleu: a method for automatic evaluation of machine translation,

Reference 60

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This paper cites chrf: character n-gram f-score for automatic mt evalu- ation,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths chrf: character n-gram f-score for automatic mt evalu- ation,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:51.044152Z

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On the Generalizability of Transformer Models to Code Completions of Different Lengths Binary codes capable of correcting deletions, insertions, and reversals,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:51.036460Z

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This paper cites Rouge: A package for automatic evaluation of summaries,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Rouge: A package for automatic evaluation of summaries,

Reference 63

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On the Generalizability of Transformer Models to Code Completions of Different Lengths Meteor: An automatic metric for mt evalua- tion with improved correlation with human judgments,

Reference 64

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This paper cites CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation.

On the Generalizability of Transformer Models to Code Completions of Different Lengths CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation

Reference 65

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This paper cites Out of the BLEU: how should we assess quality of the Code Generation models?.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Out of the BLEU: how should we assess quality of the Code Generation models?

Reference 66

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On the Generalizability of Transformer Models to Code Completions of Different Lengths Cooper and R

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:51.019225Z

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This paper cites Datasets: A community library for natural language processing,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Datasets: A community library for natural language processing,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:51.009132Z

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Observation 23f1ffa4-35f3-414d-ad89-50da1cf8eb0a · outbound

This paper cites The adverse effects of code duplication in machine learning models of code,.

On the Generalizability of Transformer Models to Code Completions of Different Lengths The adverse effects of code duplication in machine learning models of code,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:51.001224Z

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This paper cites CodeBLEU: a Method for Automatic Evaluation of Code Synthesis.

On the Generalizability of Transformer Models to Code Completions of Different Lengths CodeBLEU: a Method for Automatic Evaluation of Code Synthesis

Reference 70

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This paper cites Available: https://github.com/Lightning-AI/lightning.

On the Generalizability of Transformer Models to Code Completions of Different Lengths Available: https://github.com/Lightning-AI/lightning

Reference 2019

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