Papers by Yonatan Toib
Improving LLM Attributions with Randomized Path-Integration (2024.findings-emnlp)
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| Challenge: | Recent advances in AI research have impacted numerous application domains, fueling innovation and progress in user modeling and personalization. |
| Approach: | They propose a path-integration method for explaining language models via randomization of the integration path over the attention information in the model. |
| Outcome: | The proposed method outperforms state-of-the-art methods across 4 datasets and 5 evaluation metrics. |
LLM Explainability via Attributive Masking Learning (2024.findings-emnlp)
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| Challenge: | In this paper, we introduce Attributive Masking Learning (AML), a method designed for explaining language model predictions by learning input masks. |
| Approach: | They introduce a method for explaining language model predictions by learning input masks and ensuring a significant change in the model's explanation when applying the inverse mask to the input. |
| Outcome: | The proposed method outperforms state-of-the-art explanation methods on multiple benchmarks. |