Local Explanations and Self-Explanations for Assessing Faithfulness in black-box LLMs
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910615763681280 |
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| author | Fragkathoulas, Christos Chlapanis, Odysseas S. |
| author_facet | Fragkathoulas, Christos Chlapanis, Odysseas S. |
| contents | This paper introduces a novel task to assess the faithfulness of large language models (LLMs) using local perturbations and self-explanations. Many LLMs often require additional context to answer certain questions correctly. For this purpose, we propose a new efficient alternative explainability technique, inspired by the commonly used leave-one-out approach. Using this approach, we identify the sufficient and necessary parts for the LLM to generate correct answers, serving as explanations. We propose a metric for assessing faithfulness that compares these crucial parts with the self-explanations of the model. Using the Natural Questions dataset, we validate our approach, demonstrating its effectiveness in explaining model decisions and assessing faithfulness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_13764 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Local Explanations and Self-Explanations for Assessing Faithfulness in black-box LLMs Fragkathoulas, Christos Chlapanis, Odysseas S. Computation and Language Artificial Intelligence This paper introduces a novel task to assess the faithfulness of large language models (LLMs) using local perturbations and self-explanations. Many LLMs often require additional context to answer certain questions correctly. For this purpose, we propose a new efficient alternative explainability technique, inspired by the commonly used leave-one-out approach. Using this approach, we identify the sufficient and necessary parts for the LLM to generate correct answers, serving as explanations. We propose a metric for assessing faithfulness that compares these crucial parts with the self-explanations of the model. Using the Natural Questions dataset, we validate our approach, demonstrating its effectiveness in explaining model decisions and assessing faithfulness. |
| title | Local Explanations and Self-Explanations for Assessing Faithfulness in black-box LLMs |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2409.13764 |