5 citations · 16 across the 14 of their papers we have counts for
14 papers
TRIBE: Predicting Team Performance via Communication Behavior Ensembles
Ali Jalal-Kamali, Nikolos Gurney, David V. Pynadath +1
Designing autonomous agents that effectively assist human teams hinges on understanding team dynamics, often without task specific knowledge. We present TRIBE, a domain independent…
Do Methods to Jailbreak and Defend LLMs Generalize Across Languages?
Berk Atil, Rebecca J. Passonneau, Fred Morstatter
Large language models (LLMs) undergo safety alignment after training and tuning, yet recent work shows that safety can be bypassed through jailbreak attacks. While many jailbreaks…
Knowledge Graph Analysis of Legal Understanding and Violations in LLMs
Abha Jha, Abel Salinas, Fred Morstatter
The rise of Large Language Models (LLMs) offers transformative potential for interpreting complex legal frameworks, such as Title 18 Section 175 of the US Code, which governs biolo…
Simulating hashtag dynamics with networked groups of generative agents
Abha Jha, J. Hunter Priniski, Carolyn Steinle +1
Networked environments shape how information embedded in narratives influences individual and group beliefs and behavior. This raises key questions about how group communication ar…
The Shrinking Landscape of Linguistic Diversity in the Age of Large Language Models
Zhivar Sourati, Farzan Karimi-Malekabadi, Meltem Ozcan +7
Language is far more than a communication tool; it encodes a wealth of information about a person's identity, psychological state, and social context, providing valuable insights f…
Online network topology shapes personal narratives and hashtag generation
J. Hunter Priniski, Bryce Linford, Sai Krishna +3
While narratives have shaped cognition and cultures for centuries, digital media and online social networks have introduced new narrative phenomena. With increased narrative agency…
Secret Keepers: The Impact of LLMs on Linguistic Markers of Personal Traits
Zhivar Sourati, Meltem Ozcan, Colin McDaniel +5
Prior research has established associations between individuals' language usage and their personal traits; our linguistic patterns reveal information about our personalities, emoti…
Offset Unlearning for Large Language Models
James Y. Huang, Wenxuan Zhou, Fei Wang +4
Despite the strong capabilities of Large Language Models (LLMs) to acquire knowledge from their training corpora, the memorization of sensitive information in the corpora such as c…
Risk and Response in Large Language Models: Evaluating Key Threat Categories
Bahareh Harandizadeh, Abel Salinas, Fred Morstatter
This paper explores the pressing issue of risk assessment in Large Language Models (LLMs) as they become increasingly prevalent in various applications. Focusing on how reward mode…
Operational Collective Intelligence of Humans and Machines
Nikolos Gurney, Fred Morstatter, David V. Pynadath +2
We explore the use of aggregative crowdsourced forecasting (ACF) as a mechanism to help operationalize ``collective intelligence'' of human-machine teams for coordinated actions. W…
"Define Your Terms" : Enhancing Efficient Offensive Speech Classification with Definition
Huy Nghiem, Umang Gupta, Fred Morstatter
The propagation of offensive content through social media channels has garnered attention of the research community. Multiple works have proposed various semantically related yet s…
The Curious Case of Nonverbal Abstract Reasoning with Multi-Modal Large Language Models
Kian Ahrabian, Zhivar Sourati, Kexuan Sun +4
While large language models (LLMs) are still being adopted to new domains and utilized in novel applications, we are experiencing an influx of the new generation of foundation mode…
The Butterfly Effect of Altering Prompts: How Small Changes and Jailbreaks Affect Large Language Model Performance
Abel Salinas, Fred Morstatter
Large Language Models (LLMs) are regularly being used to label data across many domains and for myriad tasks. By simply asking the LLM for an answer, or ``prompting,'' practitioner…
"Im not Racist but...": Discovering Bias in the Internal Knowledge of Large Language Models
Abel Salinas, Louis Penafiel, Robert McCormack +1
Large language models (LLMs) have garnered significant attention for their remarkable performance in a continuously expanding set of natural language processing tasks. However, the…