Dawn Drain


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Discovering Language Model Behaviors with Model-Written Evaluations
Ethan Perez | Sam Ringer | Kamile Lukosiute | Karina Nguyen | Edwin Chen | Scott Heiner | Craig Pettit | Catherine Olsson | Sandipan Kundu | Saurav Kadavath | Andy Jones | Anna Chen | Benjamin Mann | Brian Israel | Bryan Seethor | Cameron McKinnon | Christopher Olah | Da Yan | Daniela Amodei | Dario Amodei | Dawn Drain | Dustin Li | Eli Tran-Johnson | Guro Khundadze | Jackson Kernion | James Landis | Jamie Kerr | Jared Mueller | Jeeyoon Hyun | Joshua Landau | Kamal Ndousse | Landon Goldberg | Liane Lovitt | Martin Lucas | Michael Sellitto | Miranda Zhang | Neerav Kingsland | Nelson Elhage | Nicholas Joseph | Noemi Mercado | Nova DasSarma | Oliver Rausch | Robin Larson | Sam McCandlish | Scott Johnston | Shauna Kravec | Sheer El Showk | Tamera Lanham | Timothy Telleen-Lawton | Tom Brown | Tom Henighan | Tristan Hume | Yuntao Bai | Zac Hatfield-Dodds | Jack Clark | Samuel R. Bowman | Amanda Askell | Roger Grosse | Danny Hernandez | Deep Ganguli | Evan Hubinger | Nicholas Schiefer | Jared Kaplan
Findings of the Association for Computational Linguistics: ACL 2023

As language models (LMs) scale, they develop many novel behaviors, good and bad, exacerbating the need to evaluate how they behave. Prior work creates evaluations with crowdwork (which is time-consuming and expensive) or existing data sources (which are not always available). Here, we automatically generate evaluations with LMs. We explore approaches with varying amounts of human effort, from instructing LMs to write yes/no questions to making complex Winogender schemas with multiple stages of LM-based generation and filtering. Crowdworkers rate the examples as highly relevant and agree with 90-100% of labels, sometimes more so than corresponding human-written datasets. We generate 154 datasets and discover new cases of inverse scaling where LMs get worse with size. Larger LMs repeat back a dialog user’s preferred answer (“sycophancy”) and express greater desire to pursue concerning goals like resource acquisition and goal preservation. We also find some of the first examples of inverse scaling in RL from Human Feedback (RLHF), where more RLHF makes LMs worse. For example, RLHF makes LMs express stronger political views (on gun rights and immigration) and a greater desire to avoid shut down. Overall, LM-written evaluations are high-quality and let us quickly discover many novel LM behaviors.


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Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy
Colin Clement | Shuai Lu | Xiaoyu Liu | Michele Tufano | Dawn Drain | Nan Duan | Neel Sundaresan | Alexey Svyatkovskiy
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Statistical language modeling and translation with transformers have found many successful applications in program understanding and generation tasks, setting high benchmarks for tools in modern software development environments. The finite context window of these neural models means, however, that they will be unable to leverage the entire relevant context of large files and packages for any given task. While there are many efforts to extend the context window, we introduce an architecture-independent approach for leveraging the syntactic hierarchies of source code for incorporating entire file-level context into a fixed-length window. Using concrete syntax trees of each source file we extract syntactic hierarchies and integrate them into context window by selectively removing from view more specific, less relevant scopes for a given task. We evaluate this approach on code generation tasks and joint translation of natural language and source code in Python programming language, achieving a new state-of-the-art in code completion and summarization for Python in the CodeXGLUE benchmark. We also introduce new CodeXGLUE benchmarks for user-experience-motivated tasks: code completion with normalized literals, method body completion/code summarization conditioned on file-level context.


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PyMT5: multi-mode translation of natural language and Python code with transformers
Colin Clement | Dawn Drain | Jonathan Timcheck | Alexey Svyatkovskiy | Neel Sundaresan
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Simultaneously modeling source code and natural language has many exciting applications in automated software development and understanding. Pursuant to achieving such technology, we introduce PyMT5, the Python method text-to-text transfer transformer, which is trained to translate between all pairs of Python method feature combinations: a single model that can both predict whole methods from natural language documentation strings (docstrings) and summarize code into docstrings of any common style. We present an analysis and modeling effort of a large-scale parallel corpus of 26 million Python methods and 7.7 million method-docstring pairs, demonstrating that for docstring and method generation, PyMT5 outperforms similarly-sized auto-regressive language models (GPT2) which were English pre-trained or randomly initialized. On the CodeSearchNet test set, our best model predicts 92.1% syntactically correct method bodies, achieved a BLEU score of 8.59 for method generation and 16.3 for docstring generation (summarization), and achieved a ROUGE-L F-score of 24.8 for method generation and 36.7 for docstring generation.