@inproceedings{wei-jie-etal-2024-interpretable,
    title = "How Interpretable are Reasoning Explanations from Prompting Large Language Models?",
    author = "Wei Jie, Yeo  and
      Satapathy, Ranjan  and
      Goh, Rick  and
      Cambria, Erik",
    editor = "Duh, Kevin  and
      Gomez, Helena  and
      Bethard, Steven",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2024",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.findings-naacl.138/",
    doi = "10.18653/v1/2024.findings-naacl.138",
    pages = "2148--2164",
    abstract = "Prompt Engineering has garnered significant attention for enhancing the performance of large language models across a multitude of tasks. Techniques such as the Chain-of-Thought not only bolster task performance but also delineate a clear trajectory of reasoning steps, offering a tangible form of explanation for the audience. Prior works on interpretability assess the reasoning chains yielded by Chain-of-Thought solely along a singular axis, namely faithfulness. We present a comprehensive and multifaceted evaluation of interpretability, examining not only faithfulness but also robustness and utility across multiple commonsense reasoning benchmarks. Likewise, our investigation is not confined to a single prompting technique; it expansively covers a multitude of prevalent prompting techniques employed in large language models, thereby ensuring a wide-ranging and exhaustive evaluation. In addition, we introduce a simple interpretability alignment technique, termed Self-Entailment-Alignment Chain-of-thought, that yields more than 70{\%} improvements across multiple dimensions of interpretability. Code is available at https://github.com/SenticNet/CoT{\_}interpretability"
}