72 citations · 289 across the 69 of their papers we have counts for
9 papers · 1 filter
Human Learning by Model Feedback: The Dynamics of Iterative Prompting with Midjourney
Shachar Don-Yehiya, Leshem Choshen, Omri Abend
Generating images with a Text-to-Image model often requires multiple trials, where human users iteratively update their prompt based on feedback, namely the output image. Taking in…
ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and Quantization
Prateek Yadav, Leshem Choshen, Colin Raffel +1
Parameter-efficient fine-tuning (PEFT) techniques make it possible to efficiently adapt a language model to create "expert" models that specialize to new tasks or domains. Recent t…
Fuse to Forget: Bias Reduction and Selective Memorization through Model Fusion
Kerem Zaman, Leshem Choshen, Shashank Srivastava
Model fusion research aims to aggregate the knowledge of multiple individual models to enhance performance by combining their weights. In this work, we study the inverse problem: i…
Efficient Benchmarking of Language Models
Yotam Perlitz, Elron Bandel, Ariel Gera +6
The increasing versatility of language models (LMs) has given rise to a new class of benchmarks that comprehensively assess a broad range of capabilities. Such benchmarks are assoc…
TIES-Merging: Resolving Interference When Merging Models
Prateek Yadav, Derek Tam, Leshem Choshen +2
Transfer learning - i.e., further fine-tuning a pre-trained model on a downstream task - can confer significant advantages, including improved downstream performance, faster conver…
MuLER: Detailed and Scalable Reference-based Evaluation
Taelin Karidi, Leshem Choshen, Gal Patel +1
We propose a novel methodology (namely, MuLER) that transforms any reference-based evaluation metric for text generation, such as machine translation (MT) into a fine-grained analy…
Jump to Conclusions: Short-Cutting Transformers With Linear Transformations
Alexander Yom Din, Taelin Karidi, Leshem Choshen +1
Transformer-based language models create hidden representations of their inputs at every layer, but only use final-layer representations for prediction. This obscures the internal…
Knowledge is a Region in Weight Space for Fine-tuned Language Models
Almog Gueta, Elad Venezian, Colin Raffel +3
Research on neural networks has focused on understanding a single model trained on a single dataset. However, relatively little is known about the relationships between different m…
Call for Papers -- The BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus
Alex Warstadt, Leshem Choshen, Aaron Mueller +3
We present the call for papers for the BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus. This shared task is intended for participants with an i…