4 papers
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…
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…
The Roots of Performance Disparity in Multilingual Language Models: Intrinsic Modeling Difficulty or Design Choices?
Chen Shani, Yuval Reif, Nathan Roll +2
Multilingual language models (LMs) promise broader NLP access, yet current systems deliver uneven performance across the world's languages. This survey examines why these gaps pers…
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…