Papers by Luca Dini
From Human Reading to NLM Understanding: Evaluating the Role of Eye-Tracking Data in Encoder-Based Models (2025.acl-long)
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| Challenge: | integrating eye-tracking features into Neural Language Models does not degrade downstream task performance, enhances alignment between model attention and human attention patterns, and compresses the embedding space. |
| Approach: | They used eye-gaze data from the Ghent Eye-Tracking Corpus to investigate how integrating knowledge of human reading behavior impacts Neural Language Models. |
| Outcome: | The proposed approach does not degrade downstream task performance, enhances alignment between model attention and human attention patterns, and compresses the embedding space. |
TEXT-CAKE: Challenging Language Models on Local Text Coherence (2025.coling-main)
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| Challenge: | a new evaluation benchmark, TEXT-CAKE, is used to evaluate language models for text coherence detection. |
| Approach: | They propose a benchmark to evaluate language models' ability to detect text coherence . they analyze multilingual and monolingual LMs with varying architectures and parameters . |
| Outcome: | The proposed model outperforms other models on the TEXT-CAKE evaluation benchmark. |