Search Results for author:
Found 16 papers, 9 papers with code
Social Skill Training with Large Language Models
People rely on social skills like conflict resolution to communicate effectively and to thrive in both work and personal life.
Grounding Gaps in Language Model Generations
To understand the roots of the identified grounding gap, we examine the role of instruction tuning and preference optimization, finding that training on contemporary preference data leads to a reduction in generated grounding acts.
Rehearsal: Simulating Conflict to Teach Conflict Resolution
Compared to a control group with lecture material covering the same IRP theory, participants with simulated training from Rehearsal significantly improved their performance in the unaided conflict: they reduced their use of escalating competitive strategies by an average of 67%, while doubling their use of cooperative strategies.
Modeling Cross-Cultural Pragmatic Inference with Codenames Duet
Prior work uses simple reference games to test models of pragmatic reasoning, often with unidentified speakers and listeners.
Can Large Language Models Transform Computational Social Science?
We conclude that the performance of today's LLMs can augment the CSS research pipeline in two ways: (1) serving as zero-shot data annotators on human annotation teams, and (2) bootstrapping challenging creative generation tasks (e. g., explaining the underlying attributes of a text).
On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning
Generating a Chain of Thought (CoT) has been shown to consistently improve large language model (LLM) performance on a wide range of NLP tasks.
Concept Evolution in Deep Learning Training: A Unified Interpretation Framework and Discoveries
We present ConceptEvo, a unified interpretation framework for deep neural networks (DNNs) that reveals the inception and evolution of learned concepts during training.
NeuroCartography: Scalable Automatic Visual Summarization of Concepts in Deep Neural Networks
Through a large-scale human evaluation, we demonstrate that our technique discovers neuron groups that represent coherent, human-meaningful concepts.
EnergyVis: Interactively Tracking and Exploring Energy Consumption for ML Models
The advent of larger machine learning (ML) models have improved state-of-the-art (SOTA) performance in various modeling tasks, ranging from computer vision to natural language.
RECAST: Enabling User Recourse and Interpretability of Toxicity Detection Models with Interactive Visualization
With the widespread use of toxic language online, platforms are increasingly using automated systems that leverage advances in natural language processing to automatically flag and remove toxic comments.
Examining the Ordering of Rhetorical Strategies in Persuasive Requests
We find that specific (orderings of) strategies interact uniquely with a request's content to impact success rate, and thus the persuasiveness of a request.
Mapping Researchers with PeopleMap
Discovering research expertise at universities can be a difficult task.
PeopleMap: Visualization Tool for Mapping Out Researchers using Natural Language Processing
Discovering research expertise at institutions can be a difficult task.
CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization
Deep learning's great success motivates many practitioners and students to learn about this exciting technology.
CNN 101: Interactive Visual Learning for Convolutional Neural Networks
The success of deep learning solving previously-thought hard problems has inspired many non-experts to learn and understand this exciting technology.
RECAST: Interactive Auditing of Automatic Toxicity Detection Models
As toxic language becomes nearly pervasive online, there has been increasing interest in leveraging the advancements in natural language processing (NLP), from very large transformer models to automatically detecting and removing toxic comments.
