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Found 6 papers, 3 papers with code
Cross-Domain Detection of GPT-2-Generated Technical Text
Machine-generated text presents a potential threat not only to the public sphere, but also to the scientific enterprise, whereby genuine research is undermined by convincing, synthetic text.
Calibration and Correctness of Language Models for Code
Our contributions will lead to better-calibrated decision-making in the current use of code generated by language models, and offers a framework for future research to further improve calibration methods for generative models in Software Engineering.
Robots-Dont-Cry: Understanding Falsely Anthropomorphic Utterances in Dialog Systems
Dialog systems are often designed or trained to output human-like responses.
The R-U-A-Robot Dataset: Helping Avoid Chatbot Deception by Detecting User Questions About Human or Non-Human Identity
Humans are increasingly interacting with machines through language, sometimes in contexts where the user may not know they are talking to a machine (like over the phone or a text chatbot).
NeurIPS 2020 NLC2CMD Competition: Translating Natural Language to Bash Commands
The NLC2CMD Competition hosted at NeurIPS 2020 aimed to bring the power of natural language processing to the command line.
Code to Comment "Translation": Data, Metrics, Baselining & Evaluation
We carefully examine the underlying assumption here: that the task of generating comments sufficiently resembles the task of translating between natural languages, and so similar models and evaluation metrics could be used.
