How Interpretable are Reasoning Explanations from Prompting Large Language Models?
<|reference_start|>How Interpretable are Reasoning Explanations from Prompting Large Language Models?: 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<|reference_end|>
@article{yeo2024how,
    title={How Interpretable are Reasoning Explanations from Prompting Large
  Language Models?},
    author={Wei Jie Yeo, Ranjan Satapathy, Rick Siow Mong Goh, Erik Cambria},
    journal={arXiv preprint arXiv:2402.11863},
    year={2024},
    doi={10.18653/v1/2024.findings-naacl.138},
    archivePrefix={arXiv},
    eprint={2402.11863},
    primaryClass={cs.CL}
}