most citedFew-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation

6 citations · 11 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2024

Data Contamination Report from the 2024 CONDA Shared Task

Oscar Sainz, Iker García-Ferrero, Alon Jacovi +25

The 1st Workshop on Data Contamination (CONDA 2024) focuses on all relevant aspects of data contamination in natural language processing, where data contamination is understood as…

cs.CL2024

Detection and Measurement of Syntactic Templates in Generated Text

Chantal Shaib, Yanai Elazar, Junyi Jessy Li +1

Recent work on evaluating the diversity of text generated by LLMs has focused on word-level features. Here we offer an analysis of syntactic features to characterize general repeti…

cs.LG20232 cited

The Bias Amplification Paradox in Text-to-Image Generation

Preethi Seshadri, Sameer Singh, Yanai Elazar

Bias amplification is a phenomenon in which models exacerbate biases or stereotypes present in the training data. In this paper, we study bias amplification in the text-to-image do…

cs.CL20236 cited

Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation

Marius Mosbach, Tiago Pimentel, Shauli Ravfogel +2

Few-shot fine-tuning and in-context learning are two alternative strategies for task adaptation of pre-trained language models. Recently, in-context learning has gained popularity…

cs.CV20233 cited

At Your Fingertips: Extracting Piano Fingering Instructions from Videos

Amit Moryossef, Yanai Elazar, Yoav Goldberg

Piano fingering -- knowing which finger to use to play each note in a musical piece, is a hard and important skill to master when learning to play the piano. While some sheet music…