Search Results for author:
Found 21 papers, 10 papers with code
Pre-trained language models evaluating themselves - A comparative study
Evaluating generated text received new attention with the introduction of model-based metrics in recent years.
Can OpenSource beat ChatGPT? -- A Comparative Study of Large Language Models for Text-to-Code Generation
To gain further insights, we measure the runtime as well as the memory usage of the generated outputs and compared them to the other code submissions on Leetcode.
Adaptive Contrastive Search: Uncertainty-Guided Decoding for Open-Ended Text Generation
Decoding from the output distributions of large language models to produce high-quality text is a complex challenge in language modeling.
Introducing sgboost: A Practical Guide and Implementation of sparse-group boosting in R
This paper introduces the sgboost package in R, which implements sparse-group boosting for modeling high-dimensional data with natural groupings in covariates.
Variational Approach for Efficient KL Divergence Estimation in Dirichlet Mixture Models
This study tackles the efficient estimation of Kullback-Leibler (KL) Divergence in Dirichlet Mixture Models (DMM), crucial for clustering compositional data.
Position Paper: Bridging the Gap Between Machine Learning and Sensitivity Analysis
We argue that interpretations of machine learning (ML) models or the model-building process can bee seen as a form of sensitivity analysis (SA), a general methodology used to explain complex systems in many fields such as environmental modeling, engineering, or economics.
Automatic Transcription of Handwritten Old Occitan Language
While existing neural network-based approaches have shown promising results in Handwritten Text Recognition (HTR) for high-resource languages and standardized/machine-written text, their application to low-resource languages often presents challenges, resulting in reduced effectiveness.
fmeffects: An R Package for Forward Marginal Effects
Forward marginal effects (FMEs) have recently been introduced as a versatile and effective model-agnostic interpretation method.
How Prevalent is Gender Bias in ChatGPT? -- Exploring German and English ChatGPT Responses
With the introduction of ChatGPT, OpenAI made large language models (LLM) accessible to users with limited IT expertise.
A tailored Handwritten-Text-Recognition System for Medieval Latin
This dictionary entails record cards referring to lemmas in medieval Latin, a low-resource language.
Classifying multilingual party manifestos: Domain transfer across country, time, and genre
We explore the potential of domain transfer across geographical locations, languages, time, and genre in a large-scale database of political manifestos.
How Different Is Stereotypical Bias Across Languages?
Recent studies have demonstrated how to assess the stereotypical bias in pre-trained English language models.
Using interpretable boosting algorithms for modeling environmental and agricultural data
We describe how interpretable boosting algorithms based on ridge-regularized generalized linear models can be used to analyze high-dimensional environmental data.
Multimodal Deep Learning
This book is the result of a seminar in which we reviewed multimodal approaches and attempted to create a solid overview of the field, starting with the current state-of-the-art approaches in the two subfields of Deep Learning individually.
Sparse-group boosting -- Unbiased group and variable selection
By using component-wise and group-wise gradient boosting at the same time with adjusted degrees of freedom, a model with similar properties as the sparse group lasso can be fitted through boosting.
Forecasting foreign exchange rates with regression networks tuned by Bayesian optimization
The article is concerned with the problem of multi-step financial time series forecasting of Foreign Exchange (FX) rates.
Marginal Effects for Non-Linear Prediction Functions
Hence, marginal effects are typically used as approximations for feature effects, either in the shape of derivatives of the prediction function or forward differences in prediction due to a change in a feature value.
On the comparability of Pre-trained Language Models
It is not always obvious where these improvements originate from, as it is not possible to completely disentangle the contributions of the three driving forces.
Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations
Model-agnostic interpretation techniques allow us to explain the behavior of any predictive model.
