Local Explanations and Self-Explanations for Assessing Faithfulness in black-box LLMs
<|reference_start|>Local Explanations and Self-Explanations for Assessing Faithfulness in black-box LLMs: 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.<|reference_end|>
@article{fragkathoulas2024local,
    title={Local Explanations and Self-Explanations for Assessing Faithfulness in
  black-box LLMs},
    author={Christos Fragkathoulas, Odysseas S. Chlapanis},
    journal={arXiv preprint arXiv:2409.13764},
    year={2024},
    doi={10.1145/3688671.3688775},
    archivePrefix={arXiv},
    eprint={2409.13764},
    primaryClass={cs.CL cs.AI}
}