Measuring Information in Text Explanations

• URL: http://arxiv.org/abs/2310.04557v1
• Date: Fri, 6 Oct 2023 19:46:51 GMT
• Title: Measuring Information in Text Explanations
• Authors: Zining Zhu, Frank Rudzicz
• Abstract summary: We argue that placing the explanations on an information-theoretic framework could unify the evaluations of two popular text explanation methods. We quantify the information flow through these channels, thereby facilitating the assessment of explanation characteristics. Our work contributes to the ongoing efforts in establishing rigorous and standardized evaluation criteria in the rapidly evolving field of explainable AI.
• Score: 23.929076318334047
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-based explanation is a particularly promising approach in explainable AI, but the evaluation of text explanations is method-dependent. We argue that placing the explanations on an information-theoretic framework could unify the evaluations of two popular text explanation methods: rationale and natural language explanations (NLE). This framework considers the post-hoc text pipeline as a series of communication channels, which we refer to as ``explanation channels''. We quantify the information flow through these channels, thereby facilitating the assessment of explanation characteristics. We set up tools for quantifying two information scores: relevance and informativeness. We illustrate what our proposed information scores measure by comparing them against some traditional evaluation metrics. Our information-theoretic scores reveal some unique observations about the underlying mechanisms of two representative text explanations. For example, the NLEs trade-off slightly between transmitting the input-related information and the target-related information, whereas the rationales do not exhibit such a trade-off mechanism. Our work contributes to the ongoing efforts in establishing rigorous and standardized evaluation criteria in the rapidly evolving field of explainable AI.

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