Zobrazeno 1 - 10
of 31 145
pro vyhledávání: '"Bergen, A A"'
Autor:
Lyu, Bohan, Cao, Yadi, Watson-Parris, Duncan, Bergen, Leon, Berg-Kirkpatrick, Taylor, Yu, Rose
Large Language Models (LLMs) demonstrate promising capabilities in solving simple scientific problems but often produce hallucinations for complex ones. While integrating LLMs with tools can increase reliability, this approach typically results in ov
Externí odkaz:
http://arxiv.org/abs/2411.00412
Prototype-based methods are intrinsically interpretable XAI methods that produce predictions and explanations by comparing input data with a set of learned prototypical examples that are representative of the training data. In this work, we discuss a
Externí odkaz:
http://arxiv.org/abs/2410.19856
Autor:
Manivannan, Veeramakali Vignesh, Jafari, Yasaman, Eranky, Srikar, Ho, Spencer, Yu, Rose, Watson-Parris, Duncan, Ma, Yian, Bergen, Leon, Berg-Kirkpatrick, Taylor
The use of foundation models in climate science has recently gained significant attention. However, a critical issue remains: the lack of a comprehensive evaluation framework capable of assessing the quality and scientific validity of model outputs.
Externí odkaz:
http://arxiv.org/abs/2410.16701
For many low-resource languages, the only available language models are large multilingual models trained on many languages simultaneously. However, using FLORES perplexity as a metric, we find that these models perform worse than bigrams for many la
Externí odkaz:
http://arxiv.org/abs/2408.10441
Autor:
Thach, Nguyen, Habecker, Patrick, Johnston, Bergen, Cervantes, Lillianna, Eisenbraun, Anika, Mason, Alex, Tyler, Kimberly, Khan, Bilal, Chan, Hau
Substance use is a global issue that negatively impacts millions of persons who use drugs (PWUDs). In practice, identifying vulnerable PWUDs for efficient allocation of appropriate resources is challenging due to their complex use patterns (e.g., the
Externí odkaz:
http://arxiv.org/abs/2407.13047
Everyday AI detection requires differentiating between people and AI in informal, online conversations. In many cases, people will not interact directly with AI systems but instead read conversations between AI systems and other people. We measured h
Externí odkaz:
http://arxiv.org/abs/2407.08853
We propose a prototype-based approach for improving explainability of softmax classifiers that provides an understandable prediction confidence, generated through stochastic sampling of prototypes, and demonstrates potential for out of distribution d
Externí odkaz:
http://arxiv.org/abs/2407.02271
Recent empirical results have sparked a debate about whether or not Large Language Models (LLMs) are capable of Theory of Mind (ToM). While some have found LLMs to be successful on ToM evaluations such as the False Belief task (Kosinski, 2023), other
Externí odkaz:
http://arxiv.org/abs/2406.14737
Autor:
Daymude, Joshua J., Espinoza, Antonio M., Bergen, Sean, Mixon-Baca, Benjamin, Knockel, Jeffrey, Crandall, Jedidiah R.
The battle for a more secure Internet is waged on many fronts, including the most basic of networking protocols. Our focus is the IPv4 Identifier (IPID), an IPv4 header field as old as the Internet with an equally long history as an exploited side ch
Externí odkaz:
http://arxiv.org/abs/2406.06483
Large language models (LLMs) often improve their performance in downstream tasks when they generate Chain of Thought reasoning text before producing an answer. We investigate how LLMs recover from errors in Chain of Thought. Through analysis of error
Externí odkaz:
http://arxiv.org/abs/2405.15092