Search Results for author:
Found 5 papers, 4 papers with code
Why do LLaVA Vision-Language Models Reply to Images in English?
We uncover a surprising multilingual bias occurring in a popular class of multimodal vision-language models (VLMs).
Evaluating the Elementary Multilingual Capabilities of Large Language Models with MultiQ
Therefore, in this paper, we investigate the basic multilingual capabilities of state-of-the-art open LLMs beyond their intended use.
What the Weight?! A Unified Framework for Zero-Shot Knowledge Composition
The knowledge encapsulated in a model is the core factor determining its final performance on downstream tasks.
ScaLearn: Simple and Highly Parameter-Efficient Task Transfer by Learning to Scale
We introduce ScaLearn, a simple and highly parameter-efficient two-stage MTL method that capitalizes on the knowledge of the source tasks by learning a minimal set of scaling parameters that enable effective transfer to a target task.
Fair and Argumentative Language Modeling for Computational Argumentation
We employ our resource to assess the effect of argumentative fine-tuning and debiasing on the intrinsic bias found in transformer-based language models using a lightweight adapter-based approach that is more sustainable and parameter-efficient than full fine-tuning.
