3 citations · 3 across the 4 of their papers we have counts for
6 papers · 1 filter
More Than Words: Compositional Tokenization for Efficient Language Models
Yuval Reif, Guy Kaplan, Roy Schwartz
Language models process and generate text sequentially in token units, and the tokenizer determines how much text each inference step covers. Under standard tokenization, a short E…
Last Translation Benchmark
Vilém Zouhar, Niyati Bafna, Mukund Choudhary +263
For scientific progress, we need benchmarks that test the limits of state-of-the-art models, and evaluation methods that inform us about failure cases. As models get stronger, stan…
Why Fine-Tuning Encourages Hallucinations and How to Fix It
Guy Kaplan, Zorik Gekhman, Zhen Zhu +5
Large language models are prone to hallucinating factually incorrect statements. A key source of these errors is exposure to new factual information through supervised fine-tuning…
Vocab Diet: Reshaping the Vocabulary of LLMs via Vector Arithmetic
Yuval Reif, Guy Kaplan, Roy Schwartz
Large language models (LLMs) often encode word-form variation (e.g., walk vs. walked) as linear directions in the embedding space. However, standard tokenization algorithms treat s…
Follow the Flow: On Information Flow Across Textual Tokens in Text-to-Image Models
Guy Kaplan, Michael Toker, Yuval Reif +2
Text-to-image generation models suffer from alignment problems, where generated images fail to accurately capture the objects and relations in the text prompt. Prior work has focus…
From Tokens to Words: On the Inner Lexicon of LLMs
Guy Kaplan, Matanel Oren, Yuval Reif +1
Natural language is composed of words, but modern large language models (LLMs) process sub-words as input. A natural question raised by this discrepancy is whether LLMs encode word…