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09/05/2023
Contextual Predictive Mutation Testing
Mutation testing is a powerful technique for assessing and improving tes...
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06/09/2023
GPT-Calls: Enhancing Call Segmentation and Tagging by Generating Synthetic Conversations via Large Language Models
Transcriptions of phone calls are of significant value across diverse fi...
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05/04/2023
On the Expressivity Role of LayerNorm in Transformers' Attention
Layer Normalization (LayerNorm) is an inherent component in all Transfor...
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05/02/2023
Unlimiformer: Long-Range Transformers with Unlimited Length Input
Transformer-based models typically have a predefined bound to their inpu...
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03/30/2023
Self-Refine: Iterative Refinement with Self-Feedback
Like people, LLMs do not always generate the best text for a given gener...
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02/15/2023
Learning Performance-Improving Code Edits
The waning of Moore's Law has shifted the focus of the tech industry tow...
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02/10/2023
CodeBERTScore: Evaluating Code Generation with Pretrained Models of Code
Since the rise of neural models of code that can generate long expressio...
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01/07/2023
Why do Nearest Neighbor Language Models Work?
Language models (LMs) compute the probability of a text by sequentially ...
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11/18/2022
PAL: Program-aided Language Models
Large language models (LLMs) have recently demonstrated an impressive ab...
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10/13/2022
Language Models of Code are Few-Shot Commonsense Learners
We address the general task of structured commonsense reasoning: given a...
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08/06/2022
Oversquashing in GNNs through the lens of information contraction and graph expansion
The quality of signal propagation in message-passing graph neural networ...
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07/13/2022
DocCoder: Generating Code by Retrieving and Reading Docs
Natural-language-to-code models learn to generate a code snippet given a...
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02/26/2022
A Systematic Evaluation of Large Language Models of Code
Large language models (LMs) of code have recently shown tremendous promi...
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01/28/2022
Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval
Retrieval-based language models (R-LM) model the probability of natural ...
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05/30/2021
How Attentive are Graph Attention Networks?
Graph Attention Networks (GATs) are one of the most popular GNN architec...
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11/06/2020
Single-Node Attack for Fooling Graph Neural Networks
Graph neural networks (GNNs) have shown broad applicability in a variety...
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06/09/2020
On the Bottleneck of Graph Neural Networks and its Practical Implications
Graph neural networks (GNNs) were shown to effectively learn from highly...
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05/27/2020
A Structural Model for Contextual Code Changes
We address the problem of predicting edit completions based on a learned...
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10/15/2019
Adversarial Examples for Models of Code
We introduce a novel approach for attacking trained models of code with ...
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09/30/2019
Structural Language Models of Code
We address the problem of any-code completion - generating a missing pie...
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09/30/2019
Structural Language Models for Any-Code Generation
We address the problem of Any-Code Generation (AnyGen) - generating code...
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02/25/2019
Neural Reverse Engineering of Stripped Binaries
We address the problem of predicting procedure names in stripped executa...
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02/21/2019
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Lingvo is a Tensorflow framework offering a complete solution for collab...
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10/29/2018
Contextual Speech Recognition with Difficult Negative Training Examples
Improving the representation of contextual information is key to unlocki...
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08/04/2018
code2seq: Generating Sequences from Structured Representations of Code
The ability to generate natural language sequences from source code snip...
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03/26/2018
A General Path-Based Representation for Predicting Program Properties
Predicting program properties such as names or expression types has a wi...
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03/26/2018