Papers by Jeng-Yue Liu
Probing Functional Correctness in Diffusion Language Models (2026.acl-srw)
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| Challenge: | Diffusion language models generate text by iteratively denoising all tokens in parallel, but when and where hidden states encode whether output will be functionally correct remains unknown. |
| Approach: | They present the first probing study of Diffusion language models to train classifiers on hidden states to predict functional correctness. |
| Outcome: | The proposed model generates all tokens simultaneously, but when and where does it encode correctness? |