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Found 13 papers, 7 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.
From Calculation to Adjudication: Examining LLM judges on Mathematical Reasoning Tasks
In contrast, we study LLM judges on mathematical reasoning tasks.
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.
Divergent Token Metrics: Measuring degradation to prune away LLM components -- and optimize quantization
Large Language Models (LLMs) have reshaped natural language processing with their impressive capabilities.
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.
ActiveGLAE: A Benchmark for Deep Active Learning with Transformers
Deep active learning (DAL) seeks to reduce annotation costs by enabling the model to actively query instance annotations from which it expects to learn the most.
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.
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.
