The self-rationalising capabilities of LLMs are appealing because the
generated explanations can give insights into the plausibility of the
predictions. However, how faithful the explanations are to the predictions is
questionable, raising the need to explore the patterns behind them further. To
this end, we propose a hypothesis-driven statistical framework. We use a
Bayesian network to implement a hypothesis about how a task (in our example,
natural language inference) is solved, and its internal states are translated
into natural language with templates. Those explanations are then compared to
LLM-generated free-text explanations using automatic and human evaluations.
This allows us to judge how similar the LLM's and the Bayesian network's
decision processes are. We demonstrate the usage of our framework with an
example hypothesis and two realisations in Bayesian networks. The resulting
models do not exhibit a strong similarity to GPT-3.5. We discuss the
implications of this as well as the framework's potential to approximate LLM
decisions better in future work.

我们提出了一个基于假设的统计框架，使用贝叶斯网络将任务的内部状态与模板翻译成自然语言，然后将这些解释与 LLM 生成的自由文本解释进行比较，以判断 LLM 和贝叶斯网络的决策过程的相似性，结果显示贝叶斯网络模型与 GPT-3.5 并没有很强的相似性，进一步工作可以通过该框架更好地近似 LLM 的决策。