Dawn Drain

Orcid: 0000-0002-6606-4141

Affiliations:
  • Microsoft, USA


According to our database1, Dawn Drain authored at least 25 papers between 2020 and 2023.

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Bibliography

2023
The Capacity for Moral Self-Correction in Large Language Models.
CoRR, 2023


2022
Discovering Language Model Behaviors with Model-Written Evaluations.
CoRR, 2022

Constitutional AI: Harmlessness from AI Feedback.
CoRR, 2022

Measuring Progress on Scalable Oversight for Large Language Models.
CoRR, 2022

In-context Learning and Induction Heads.
CoRR, 2022

Toy Models of Superposition.
CoRR, 2022

Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned.
CoRR, 2022

Language Models (Mostly) Know What They Know.
CoRR, 2022

Scaling Laws and Interpretability of Learning from Repeated Data.
CoRR, 2022

Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.
CoRR, 2022

Predictability and Surprise in Large Generative Models.
CoRR, 2022

Exploring and evaluating personalized models for code generation.
Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2022


Generating Accurate Assert Statements for Unit Test Cases using Pretrained Transformers.
Proceedings of the IEEE/ACM International Conference on Automation of Software Test, 2022

2021
A General Language Assistant as a Laboratory for Alignment.
CoRR, 2021

Distilling Transformers for Neural Cross-Domain Search.
CoRR, 2021

DeepDebug: Fixing Python Bugs Using Stack Traces, Backtranslation, and Code Skeletons.
CoRR, 2021

Generating Code with the Help of Retrieved Template Functions and Stack Overflow Answers.
CoRR, 2021

Generating bug-fixes using pretrained transformers.
Proceedings of the MAPS@PLDI 2021: Proceedings of the 5th ACM SIGPLAN International Symposium on Machine Programming, 2021

CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation.
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

GraphCodeBERT: Pre-training Code Representations with Data Flow.
Proceedings of the 9th International Conference on Learning Representations, 2021

Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy.
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021

2020
Unit Test Case Generation with Transformers.
CoRR, 2020

PyMT5: multi-mode translation of natural language and Python code with transformers.
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020


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