Machine Generated Explanations and Their Evaluation
Abstract
Rapid adoption of a new generation of LLMs has demonstrated their considerable capabilities. However, these models are far from infallible, raising significant ethical concerns, especially in decision-making applications, prompting calls for increased restraint [2]. The Augmented Intelligence paradigm is one proposed mitigation. Therein LLMs are tools used by human decision makers to improve performance without corresponding loss of accountability. However, this mitigation imposes requirements on models that are not the primary focus of existing evaluation approaches. In particular, current explanation evaluation approaches tend to prioritize premises and conclusions over reasoning quality. It is evident that logical soundness is a crucial aspect of system operation, as the output must be interpretable to the user. This work therefore proposes adopting a technique from programming language theory, wherein intermediate representations are employed to simplify the evaluation of code [3]. Rather than the model mapping directly from queries to solutions, code generation is used to produce an executable intermediate. An effect of this design is to shift the LLM from being a producer of solutions to the creator delegation plans. Production of a more structured output is expected to ease estimation of model reasoning quality via the comparison to golden solutions. Use of a bespoke representation, designed to take advantage of the particulars of automated code generation, aims to…
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