Search Results for author:
Found 14 papers, 5 papers with code
ICL Markup: Structuring In-Context Learning using Soft-Token Tags
Large pretrained language models (LLMs) can be rapidly adapted to a wide variety of tasks via a text-to-text approach, where the instruction and input are fed to the model in natural language.
Sparsify-then-Classify: From Internal Neurons of Large Language Models To Efficient Text Classifiers
Among the many tasks that Large Language Models (LLMs) have revolutionized is text classification.
Generating Faithful Synthetic Data with Large Language Models: A Case Study in Computational Social Science
Large Language Models (LLMs) have democratized synthetic data generation, which in turn has the potential to simplify and broaden a wide gamut of NLP tasks.
Detecting Individual Decision-Making Style: Exploring Behavioral Stylometry in Chess
We present a transformer-based approach to behavioral stylometry in the context of chess, where one attempts to identify the player who played a set of games.
Mimetic Models: Ethical Implications of AI that Acts Like You
An emerging theme in artificial intelligence research is the creation of models to simulate the decisions and behavior of specific people, in domains including game-playing, text generation, and artistic expression.
Quantifying social organization and political polarization in online platforms
However, our ability to measure the social makeup of online communities, and in turn understand the social organization of online platforms, is limited by the pseudonymous, unstructured, and large-scale nature of digital discussion.
Adoption of Twitter's New Length Limit: Is 280 the New 140?
The prevalence of tweets around 140 characters before the switch in a given language is strongly correlated with the prevalence of tweets around 280 characters after the switch in the same language, and very long tweets are vastly more popular on Web clients than on mobile clients.
Learning Models of Individual Behavior in Chess
AI systems that can capture human-like behavior are becoming increasingly useful in situations where humans may want to learn from these systems, collaborate with them, or engage with them as partners for an extended duration.
De-anonymization of authors through arXiv submissions during double-blind review
Under two conditions: papers that are released on arXiv before the review phase and papers that are not, we examine the correlation between the reputation of their authors with the review scores and acceptance decisions.
Aligning Superhuman AI with Human Behavior: Chess as a Model System
We develop and introduce Maia, a customized version of Alpha-Zero trained on human chess games, that predicts human moves at a much higher accuracy than existing engines, and can achieve maximum accuracy when predicting decisions made by players at a specific skill level in a tuneable way.
Understanding the Origins of Bias in Word Embeddings
Given a word embedding trained on a corpus, our method identifies how perturbing the corpus will affect the bias of the resulting embedding.
Assessing Human Error Against a Benchmark of Perfection
An increasing number of domains are providing us with detailed trace data on human decisions in settings where we can evaluate the quality of these decisions via an algorithm.
Engaging with Massive Online Courses
We also report on a large-scale deployment of badges as incentives for engagement in a MOOC, including randomized experiments in which the presentation of badges was varied across sub-populations.
