Search Results for author:
Found 8 papers, 4 papers with code
The Colorful Future of LLMs: Evaluating and Improving LLMs as Emotional Supporters for Queer Youth
Queer youth face increased mental health risks, such as depression, anxiety, and suicidal ideation.
Faithful Explanations of Black-box NLP Models Using LLM-generated Counterfactuals
We hence present a second approach based on matching, and propose a method that is guided by an LLM at training-time and learns a dedicated embedding space.
Measuring the Robustness of NLP Models to Domain Shifts
We then conducted a comprehensive large-scale DR study involving over 14, 000 domain shifts across 21 fine-tuned models and few-shot LLMs.
A Systematic Study of Knowledge Distillation for Natural Language Generation with Pseudo-Target Training
In this work, we study the potential of compressing them, which is crucial for real-world applications serving millions of users.
A Picture May Be Worth a Thousand Lives: An Interpretable Artificial Intelligence Strategy for Predictions of Suicide Risk from Social Media Images
Notably, the study illustrates the advantages of hybrid models in such complicated tasks and provides simple and flexible prediction strategies that could be utilized to develop real-life monitoring tools of suicide.
A Functional Information Perspective on Model Interpretation
This work suggests a theoretical framework for model interpretability by measuring the contribution of relevant features to the functional entropy of the network with respect to the input.
From Limited Annotated Raw Material Data to Quality Production Data: A Case Study in the Milk Industry (Technical Report)
In this paper we propose a design methodology, using active learning to enhance learning capabilities, for building a model of production outcome using a constrained amount of raw material training data.
DoCoGen: Domain Counterfactual Generation for Low Resource Domain Adaptation
Natural language processing (NLP) algorithms have become very successful, but they still struggle when applied to out-of-distribution examples.
