no code implementations • 5 Apr 2024 • Diyi Yang, Caleb Ziems, William Held, Omar Shaikh, Michael S. Bernstein, John Mitchell
People rely on social skills like conflict resolution to communicate effectively and to thrive in both work and personal life.
no code implementations • 15 Nov 2023 • Omar Shaikh, Kristina Gligorić, Ashna Khetan, Matthias Gerstgrasser, Diyi Yang, Dan Jurafsky
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.
no code implementations • 21 Sep 2023 • Omar Shaikh, Valentino Chai, Michele J. Gelfand, Diyi Yang, Michael S. Bernstein
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.
1 code implementation • 4 Jun 2023 • Omar Shaikh, Caleb Ziems, William Held, Aryan J. Pariani, Fred Morstatter, Diyi Yang
Prior work uses simple reference games to test models of pragmatic reasoning, often with unidentified speakers and listeners.
1 code implementation • 12 Apr 2023 • Caleb Ziems, William Held, Omar Shaikh, Jiaao Chen, Zhehao Zhang, Diyi Yang
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).
1 code implementation • 15 Dec 2022 • Omar Shaikh, Hongxin Zhang, William Held, Michael Bernstein, Diyi Yang
Generating a Chain of Thought (CoT) has been shown to consistently improve large language model (LLM) performance on a wide range of NLP tasks.
no code implementations • 30 Mar 2022 • Haekyu Park, Seongmin Lee, Benjamin Hoover, Austin P. Wright, Omar Shaikh, Rahul Duggal, Nilaksh Das, Kevin Li, Judy Hoffman, Duen Horng Chau
We present ConceptEvo, a unified interpretation framework for deep neural networks (DNNs) that reveals the inception and evolution of learned concepts during training.
1 code implementation • 29 Aug 2021 • Haekyu Park, Nilaksh Das, Rahul Duggal, Austin P. Wright, Omar Shaikh, Fred Hohman, Duen Horng Chau
Through a large-scale human evaluation, we demonstrate that our technique discovers neuron groups that represent coherent, human-meaningful concepts.
2 code implementations • 30 Mar 2021 • Omar Shaikh, Jon Saad-Falcon, Austin P Wright, Nilaksh Das, Scott Freitas, Omar Isaac Asensio, Duen Horng Chau
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.
no code implementations • 8 Feb 2021 • Austin P Wright, Omar Shaikh, Haekyu Park, Will Epperson, Muhammed Ahmed, Stephane Pinel, Duen Horng Chau, Diyi Yang
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.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Omar Shaikh, Jiaao Chen, Jon Saad-Falcon, Duen Horng Chau, Diyi Yang
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.
1 code implementation • 31 Aug 2020 • Jon Saad-Falcon, Omar Shaikh, Zijie J. Wang, Austin P. Wright, Sasha Richardson, Duen Horng Chau
Discovering research expertise at universities can be a difficult task.
1 code implementation • 10 Jun 2020 • Jon Saad-Falcon, Omar Shaikh, Zijie J. Wang, Austin P. Wright, Sasha Richardson, Duen Horng Chau
Discovering research expertise at institutions can be a difficult task.
5 code implementations • 30 Apr 2020 • Zijie J. Wang, Robert Turko, Omar Shaikh, Haekyu Park, Nilaksh Das, Fred Hohman, Minsuk Kahng, Duen Horng Chau
Deep learning's great success motivates many practitioners and students to learn about this exciting technology.
no code implementations • 7 Jan 2020 • Zijie J. Wang, Robert Turko, Omar Shaikh, Haekyu Park, Nilaksh Das, Fred Hohman, Minsuk Kahng, Duen Horng Chau
The success of deep learning solving previously-thought hard problems has inspired many non-experts to learn and understand this exciting technology.
no code implementations • 7 Jan 2020 • Austin P. Wright, Omar Shaikh, Haekyu Park, Will Epperson, Muhammed Ahmed, Stephane Pinel, Diyi Yang, Duen Horng Chau
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.