Papers by Michael Toker
Diffusion Lens: Interpreting Text Encoders in Text-to-Image Pipelines (2024.acl-long)
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| Challenge: | Text-to-image diffusion models use a latent text prompt to guide image generation . however, the process by which the encoder produces the text representation is unknown . |
| Approach: | They propose a method for analyzing the text encoder of T2I models by generating images from its intermediate representations. |
| Outcome: | The proposed method provides valuable insights into the text encoder component in T2I pipelines. |
A Dataset for Metaphor Detection in Early Medieval Hebrew Poetry (2024.eacl-short)
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| Challenge: | a corpus of late antique and medieval Hebrew poetry is rich in metaphors and similes . scholars in the humanities need to distinguish between figurative and literal language . |
| Approach: | They present a corpus of late antique and medieval Hebrew poetry with expert annotations of metaphor . they hope to facilitate further research in this area . |
| Outcome: | The proposed dataset includes a corpus of late antique and medieval Hebrew poetry with expert annotations of metaphor. |
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. |
Padding Tone: A Mechanistic Analysis of Padding Tokens in T2I Models (2025.naacl-long)
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| Challenge: | Text-to-image (T2I) diffusion models rely on encoded prompts to guide the image generation process. |
| Approach: | They conduct the first in-depth analysis of the role padding tokens play in T2I diffusion models by using two causal techniques to analyze how information is encoded in the representation of tokens across different components of the pipeline. |
| Outcome: | The proposed techniques reveal that padding tokens may affect the model’s output during text encoding, during the diffusion process, or be effectively ignored. |