Papers by Yuval Reif
Vocab Diet: Reshaping the Vocabulary of LLMs via Vector Arithmetic (2026.findings-acl)
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| Challenge: | Large language models often encode word-form variation as linear directions in the embedding space. |
| Approach: | They propose a compact reshaping of large language models' vocabulary by using shared vectors instead of unique tokens. |
| Outcome: | The proposed approach frees 10-40% of vocabulary slots to be reallocated where tokenization is inefficient. |
Fighting Bias With Bias: Promoting Model Robustness by Amplifying Dataset Biases (2023.findings-acl)
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| Challenge: | Recent work sought to develop robust, unbiased models by filtering biased examples from training sets. |
| Approach: | They propose to filter out biased examples from training sets to improve models' performance. |
| Outcome: | The proposed evaluation framework is more challenging than the original dataset splits and even more challenging that hand-crafted challenge sets. |
Beyond Performance: Quantifying and Mitigating Label Bias in LLMs (2024.naacl-long)
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| Challenge: | Large language models exhibit undesirable preference toward predicting certain answers over others, despite their adaptability to diverse tasks. |
| Approach: | They propose a label bias calibration method that outperforms recent calibration approaches for improving performance and mitigating label bias. |
| Outcome: | The proposed method outperforms calibration approaches for improving performance and mitigating label bias. |
Follow the Flow: On Information Flow Across Textual Tokens in Text-to-Image Models (2026.acl-long)
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| Challenge: | Prior work focused on improving alignment by refining the diffusion process, ignoring the role of the text encoder, which guides the diffusion. |
| Approach: | They investigate how semantic information is distributed across token representations in text-to-image prompts by patching techniques to uncover encoding patterns. |
| Outcome: | The proposed model can improve alignment and generation quality by modifying the diffusion stage and the cross-attention mechanism. |