Zobrazeno 1 - 10
of 3 734
pro vyhledávání: '"P, Flanigan"'
Autor:
Li, Changmao, Flanigan, Jeffrey
Large Language Models (LLMs) exhibit impressive results across a wide range of natural language processing (NLP) tasks, yet they can often produce factually incorrect outputs. This paper introduces a simple but effective low-latency post-correction m
Externí odkaz:
http://arxiv.org/abs/2410.15667
Autor:
Baharav, Carmel, Flanigan, Bailey
Sortition, the random selection of political representatives, is increasingly being used around the world to choose participants of deliberative processes like Citizens' Assemblies. Motivated by sortition's practical importance, there has been a rece
Externí odkaz:
http://arxiv.org/abs/2406.15009
In recent years, predictive maintenance (PMx) has gained prominence for its potential to enhance efficiency, automation, accuracy, and cost-effectiveness while reducing human involvement. Importantly, PMx has evolved in tandem with digital advancemen
Externí odkaz:
http://arxiv.org/abs/2406.13117
Large language models (LLMs) have advanced to encompass extensive knowledge across diverse domains. Yet controlling what a large language model should not know is important for ensuring alignment and thus safe use. However, accurately and efficiently
Externí odkaz:
http://arxiv.org/abs/2406.07933
Autor:
King, Brendan, Flanigan, Jeffrey
Training task-oriented dialogue systems typically requires turn-level annotations for interacting with their APIs: e.g. a dialogue state and the system actions taken at each step. These annotations can be costly to produce, error-prone, and require b
Externí odkaz:
http://arxiv.org/abs/2404.15219
Autor:
Li, Changmao, Flanigan, Jeffrey
Predicting the future is of great interest across many aspects of human activity. Businesses are interested in future trends, traders are interested in future stock prices, and companies are highly interested in future technological breakthroughs. Wh
Externí odkaz:
http://arxiv.org/abs/2404.10297
A common explanation for negative user impacts of content recommender systems is misalignment between the platform's objective and user welfare. In this work, we show that misalignment in the platform's objective is not the only potential cause of un
Externí odkaz:
http://arxiv.org/abs/2401.05304
Autor:
Li, Changmao, Flanigan, Jeffrey
Large language models (LLMs) offer impressive performance in various zero-shot and few-shot tasks. However, their success in zero-shot and few-shot settings may be affected by task contamination, a potential limitation that has not been thoroughly ex
Externí odkaz:
http://arxiv.org/abs/2312.16337
Autor:
Liu, Chris Yuhao, Flanigan, Jeffrey
The phenomenon of model-wise double descent, where the test error peaks and then reduces as the model size increases, is an interesting topic that has attracted the attention of researchers due to the striking observed gap between theory and practice
Externí odkaz:
http://arxiv.org/abs/2312.03951
Recent digital advances have popularized predictive maintenance (PMx), offering enhanced efficiency, automation, accuracy, cost savings, and independence in maintenance processes. Yet, PMx continues to face numerous limitations such as poor explainab
Externí odkaz:
http://arxiv.org/abs/2311.06993