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Found 12 papers, 2 papers with code
Multilingual large language models leak human stereotypes across language boundaries
We evaluate human stereotypes and stereotypical associations manifested in multilingual large language models such as mBERT, mT5, and ChatGPT.
Seamful XAI: Operationalizing Seamful Design in Explainable AI
We found that the Seamful XAI design process helped users foresee AI harms, identify underlying reasons (seams), locate them in the AI's lifecycle, learn how to leverage seamful information to improve XAI and user agency.
Content Selection in Deep Learning Models of Summarization
We carry out experiments with deep learning models of summarization across the domains of news, personal stories, meetings, and medical articles in order to understand how content selection is performed.
Active Learning for Cost-Sensitive Classification
We design an active learning algorithm for cost-sensitive multiclass classification: problems where different errors have different costs.
Logarithmic Time One-Against-Some
Compared to previous approaches, we obtain substantially better statistical performance for two reasons: First, we prove a tighter and more complete boosting theorem, and second we translate the results more directly into an algorithm.
Modeling Dynamic Relationships Between Characters in Literary Novels
Studying characters plays a vital role in computationally representing and interpreting narratives.
Ask, and shall you receive?: Understanding Desire Fulfillment in Natural Language Text
The ability to comprehend wishes or desires and their fulfillment is important to Natural Language Understanding.
Parser for Abstract Meaning Representation using Learning to Search
We develop a novel technique to parse English sentences into Abstract Meaning Representation (AMR) using SEARN, a Learning to Search approach, by modeling the concept and the relation learning in a unified framework.
Learning to Search in Branch and Bound Algorithms
Branch-and-bound is a widely used method in combinatorial optimization, including mixed integer programming, structured prediction and MAP inference.
Bayesian Multitask Learning with Latent Hierarchies
We learn multiple hypotheses for related tasks under a latent hierarchical relationship between tasks.
Binary to Bushy: Bayesian Hierarchical Clustering with the Beta Coalescent
Discovering hierarchical regularities in data is a key problem in interacting with large datasets, modeling cognition, and encoding knowledge.
Learning Task Grouping and Overlap in Multi-task Learning
In the paradigm of multi-task learning, mul- tiple related prediction tasks are learned jointly, sharing information across the tasks.
